Optimal cost for strengthening or destroying a given network
Patron, Amikam; Cohen, Reuven; Li, Daqing; Havlin, Shlomo
2017-05-01
Strengthening or destroying a network is a very important issue in designing resilient networks or in planning attacks against networks, including planning strategies to immunize a network against diseases, viruses, etc. Here we develop a method for strengthening or destroying a random network with a minimum cost. We assume a correlation between the cost required to strengthen or destroy a node and the degree of the node. Accordingly, we define a cost function c (k ) , which is the cost of strengthening or destroying a node with degree k . Using the degrees k in a network and the cost function c (k ) , we develop a method for defining a list of priorities of degrees and for choosing the right group of degrees to be strengthened or destroyed that minimizes the total price of strengthening or destroying the entire network. We find that the list of priorities of degrees is universal and independent of the network's degree distribution, for all kinds of random networks. The list of priorities is the same for both strengthening a network and for destroying a network with minimum cost. However, in spite of this similarity, there is a difference between their pc, the critical fraction of nodes that has to be functional to guarantee the existence of a giant component in the network.
Hooda, Nikhil; Damani, Om
2017-06-01
The classic problem of the capital cost optimization of branched piped networks consists of choosing pipe diameters for each pipe in the network from a discrete set of commercially available pipe diameters. Each pipe in the network can consist of multiple segments of differing diameters. Water networks also consist of intermediate tanks that act as buffers between incoming flow from the primary source and the outgoing flow to the demand nodes. The network from the primary source to the tanks is called the primary network, and the network from the tanks to the demand nodes is called the secondary network. During the design stage, the primary and secondary networks are optimized separately, with the tanks acting as demand nodes for the primary network. Typically the choice of tank locations, their elevations, and the set of demand nodes to be served by different tanks is manually made in an ad hoc fashion before any optimization is done. It is desirable therefore to include this tank configuration choice in the cost optimization process itself. In this work, we explain why the choice of tank configuration is important to the design of a network and describe an integer linear program model that integrates the tank configuration to the standard pipe diameter selection problem. In order to aid the designers of piped-water networks, the improved cost optimization formulation is incorporated into our existing network design system called JalTantra.
Charging cost optimization for EV buses using neural network based energy predictor
Nageshrao, S.P.; Jacob, J.; Wilkins, S.
2017-01-01
For conventional buses, based on the decades of their operational knowledge, public transport companies are able to optimize their cost of operation. However, with recent trend in the usage of electric buses, cost optimal operation can become challenging. In this paper an offline optimal charging
International Nuclear Information System (INIS)
Li, Huajiao; An, Haizhong; Fang, Wei; Jiang, Meng
2017-01-01
The logistical issues surrounding the timing and transport of flowback generated by each shale gas well to the next is a big challenge. Due to more and more flowback being stored temporarily near the shale gas well and reused in the shale gas development, both transportation cost and storage cost are the heavy burden for the developers. This research proposed a theoretical cost optimization model to get the optimal flowback distribution solution for regional multi shale gas wells in a holistic perspective. Then, we used some empirical data of Marcellus Shale to do the empirical study. In addition, we compared the optimal flowback distribution solution by considering both the transportation cost and storage cost with the flowback distribution solution which only minimized the transportation cost or only minimized the storage cost. - Highlights: • A theoretical cost optimization model to get optimal flowback distribution solution. • An empirical study using the shale gas data in Bradford County of Marcellus Shale. • Visualization of optimal flowback distribution solutions under different scenarios. • Transportation cost is a more important factor for reducing the cost. • Help the developers to cut the storage and transportation cost of reusing flowback.
Optimal Network-Topology Design
Li, Victor O. K.; Yuen, Joseph H.; Hou, Ting-Chao; Lam, Yuen Fung
1987-01-01
Candidate network designs tested for acceptability and cost. Optimal Network Topology Design computer program developed as part of study on topology design and analysis of performance of Space Station Information System (SSIS) network. Uses efficient algorithm to generate candidate network designs consisting of subsets of set of all network components, in increasing order of total costs and checks each design to see whether it forms acceptable network. Technique gives true cost-optimal network and particularly useful when network has many constraints and not too many components. Program written in PASCAL.
Directory of Open Access Journals (Sweden)
Chun Fu
2015-05-01
Full Text Available Purpose: By studying the case of a Changsha engineering machinery manufacturing firm, this paper aims to find out the optimization tactics to reduce enterprise’s logistics operational cost. Design/methodology/approach: This paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of inter-firm network and simulates the model based on system dynamics. Findings: It concludes that applying system dynamics in the research of manufacturing enterprise’s logistics cost control can better reflect the relationship of factors in the system. And the case firm can optimize the logistics costs by implement joint distribution. Research limitations/implications: This study still lacks comprehensive consideration about the variables quantities and quantitative of the control factors. In the future, we should strengthen the collection of data and information about the engineering manufacturing firms and improve the logistics operational cost model. Practical implications: This study puts forward some optimization tactics to reduce enterprise’s logistics operational cost. And it is of great significance for enterprise’s supply chain management optimization and logistics cost control. Originality/value: Differing from the existing literatures, this paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of inter-firm network and simulates the model based on system dynamics.
A bridge network maintenance framework for Pareto optimization of stakeholders/users costs
International Nuclear Information System (INIS)
Orcesi, Andre D.; Cremona, Christian F.
2010-01-01
For managing highway bridges, stakeholders require efficient and practical decision making techniques. In a context of limited bridge management budget, it is crucial to determine the most effective breakdown of financial resources over the different structures of a bridge network. Bridge management systems (BMSs) have been developed for such a purpose. However, they generally rely on an individual approach. The influence of the position of bridges in the transportation network, the consequences of inadequate service for the network users, due to maintenance actions or bridge failure, are not taken into consideration. Therefore, maintenance strategies obtained with current BMSs do not necessarily lead to an optimal level of service (LOS) of the bridge network for the users of the transportation network. Besides, the assessment of the structural performance of highway bridges usually requires the access to the geometrical and mechanical properties of its components. Such information might not be available for all structures in a bridge network for which managers try to schedule and prioritize maintenance strategies. On the contrary, visual inspections are performed regularly and information is generally available for all structures of the bridge network. The objective of this paper is threefold (i) propose an advanced network-level bridge management system considering the position of each bridge in the transportation network, (ii) use information obtained at visual inspections to assess the performance of bridges, and (iii) compare optimal maintenance strategies, obtained with a genetic algorithm, when considering interests of users and bridge owner either separately as conflicting criteria, or simultaneously as a common interest for the whole community. In each case, safety and serviceability aspects are taken into account in the model when determining optimal strategies. The theoretical and numerical developments are applied on a French bridge network.
DEFF Research Database (Denmark)
Hansen, C. T.; Madsen, Kaj; Nielsen, Hans Bruun
1991-01-01
algorithm using successive linear programming is presented. The performance of the algorithm is illustrated by optimizing a network with 201 pipes and 172 nodes. It is concluded that the new algorithm seems to be very efficient and stable, and that it always finds a solution with a cost near the best...
OPTIMAL NETWORK TOPOLOGY DESIGN
Yuen, J. H.
1994-01-01
This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Directory of Open Access Journals (Sweden)
Haiping Shi
2016-08-01
Full Text Available The capacity of the ship-lock at the Three Gorges Dam has become bottleneck of waterway transport and caused serious congestion. In this article, a continual network design model is established to solve the problem with minimizing the transport cost and environmental emission as well as infrastructure construction cost. In this bi-level model, the upper model gives the schemes of ship-lock expansion or construction of pass-dam highway. The lower model assigns the containers in the multi-mode network and calculates the transport cost, environmental emission, and construction investment. The solution algorithm to the model is proposed. In the numerical study, scenario analyses are done to evaluate the schemes and determine the optimal one in the context of different traffic demands. The result shows that expanding the ship-lock is better than constructing pass-dam highway.
International Nuclear Information System (INIS)
Ribeiro, E.M.; Farias, M.A.; Dreyer, S.R.B.
1995-01-01
Considering the importance of the cost of material and equipment in the overall cost profile of an oil company, which in the case of Petrobras, represents approximately 23% of the total operational cost or 10% of the sales, an organization for the optimization of such costs has been established within Petrobras. Programs are developed aiming at: optimization of life-cycle cost of material and equipment; optimization of industrial processes costs through material development. This paper describes the methodology used in the management of the development programs and presents some examples of concluded and ongoing programs, which are conducted in permanent cooperation with suppliers, technical laboratories and research institutions and have been showing relevant results
International Nuclear Information System (INIS)
Kavousi-Fard, Abdollah; Abbasi, Alireza; Rostami, Mohammad-Amin; Khosravi, Abbas
2015-01-01
Appearance of PEVs (Plug-in Electric Vehicles) in future transportation sector brings forward opportunities and challenges from grid perspective. Increased utilization of PEVs will result in problems such as greater total loss, unbalanced load factor, feeder congestion and voltage drop. PEVs are mobile energy storages dispersed all over the network with benefits to both owners and utilities in case of V2G (Vehicle-to-Grid) possibility. The intelligent bidirectional power flow between grid and large number of vehicles adds complexity to the system and requires operative tools to schedule V2G energy and subdue PEV impacts. In this paper, DFR (Distribution Feeder Reconfiguration) is utilized to optimally coordinate PEV operation in a stochastic framework. Uncertainty in PEVs characteristics can be due to several sources from location and time of grid connection to driving pattern and battery SoC (State-of-Charge). The proposed stochastic problem is solved with a self-adaptive evolutionary swarm algorithm based on SSO (Social Spider Optimization) algorithm. Numerical studies verify the efficacy of the proposed DFR to improve the system performance and optimal dispatch of V2G. - Highlights: • Consideration effect of PEVS on the distribution feeder reconfiguration. • Increasing the penetration of PEVS. • Introducing a new artificial optimization algorithm. • Modeling the uncertainty in network. • Investigating the degradation cost of batteries in V2G technology.
Minimum cost connection networks
DEFF Research Database (Denmark)
Hougaard, Jens Leth; Tvede, Mich
2015-01-01
In the present paper we consider the allocation of costs in connection networks. Agents have connection demands in form of pairs of locations they want to have connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection...... demands. We use a few axioms to characterize allocation rules that truthfully implement cost minimizing networks satisfying all connection demands in a game where: (1) a central planner announces an allocation rule and a cost estimation rule; (2) every agent reports her own connection demand as well...... as all connection costs; (3) the central planner selects a cost minimizing network satisfying reported connection demands based on the estimated costs; and, (4) the planner allocates the true costs of the selected network. It turns out that an allocation rule satisfies the axioms if and only if relative...
Sequential Construction of Costly Networks
Energy Technology Data Exchange (ETDEWEB)
Gutfraind, Alexander [Los Alamos National Laboratory
2011-01-01
Natural disasters or attacks often disrupt infrastructure networks requiring a costly recovery. This motivates an optimization problem where the objecitve is to construct the nodes of a graph G(V;E), and the cost of each node is dependent on the number of its neighbors previously constructed, or more generally, any properties of the previously-completed subgraph. In this optimization problem the objective is to find a permutation of the nodes which results in the least construction cost. We prove that in the case where the cost of nodes is a convex function in the number of neighbors, the optimal construction sequence is to start at a single node and move outwards. We also introduce algorithms and heuristics for solving various instances of the problem. Those methods can be applied to help reduce the cost of recovering from disasters as well as to plan the deployment of new network infrastructure.
Minimum cost connection networks
DEFF Research Database (Denmark)
Hougaard, Jens Leth; Tvede, Mich
In the present paper we consider the allocation of cost in connection networks. Agents have connection demands in form of pairs of locations they want to be connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection demands...
Networks and Transaction Costs
DEFF Research Database (Denmark)
Henning, Christian; Henningsen, Geraldine; Henningsen, Arne
2011-01-01
Based on the well-known fact that social networks can provide effective mechanisms that help to increase the trust level between two trade partners, we apply a simple game-theoretical framework to derive transaction costs as a high risk of opportunistic behavior in a repeated trade relation...... determined by the density and size of trading networks. In the empirical part of the paper we apply a two stage procedure to estimate the impact of social network structures on farm’s transaction costs observed for different input and output markets. At a first stage we estimate a multiple input...... transaction cost functions for all traded farm inputs and outputs. Estimation results based on a sample of 315 Polish farms imply a significant influence of social network structures on farm’s transaction costs. Moreover, estimated transaction costs correspond to a reasonable amount of farm specific shadow...
Networks and Transaction Costs
DEFF Research Database (Denmark)
Henning, Christian; Henningsen, Geraldine; Henningsen, Arne
2011-01-01
determined by the density and size of trading networks. In the empirical part of the paper we apply a two stage procedure to estimate the impact of social network structures on farm’s transaction costs observed for different input and output markets. At a first stage we estimate a multiple input......Based on the well-known fact that social networks can provide effective mechanisms that help to increase the trust level between two trade partners, we apply a simple game-theoretical framework to derive transaction costs as a high risk of opportunistic behavior in a repeated trade relation...... transaction cost functions for all traded farm inputs and outputs. Estimation results based on a sample of 315 Polish farms imply a significant influence of social network structures on farm’s transaction costs. Moreover, estimated transaction costs correspond to a reasonable amount of farm specific shadow...
Toward Optimal Transport Networks
Alexandrov, Natalia; Kincaid, Rex K.; Vargo, Erik P.
2008-01-01
Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.
Towards Optimal Transport Networks
Directory of Open Access Journals (Sweden)
Erik P. Vargo
2010-08-01
Full Text Available Our ultimate goal is to design transportation net- works whose dynamic performance metrics (e.g. pas- senger throughput, passenger delay, and insensitivity to weather disturbances are optimized. Here the fo- cus is on optimizing static features of the network that are known to directly aﬀect the network dynamics. First, we present simulation results which support a connection between maximizing the ﬁrst non-trivial eigenvalue of a network's Laplacian and superior air- port network performance. Then, we explore the ef- fectiveness of a tabu search heuristic for optimizing this metric by comparing experimental results to the- oretical upper bounds. We also consider generating upper bounds on a network's algebraic connectivity via the solution of semideﬁnite programming (SDP relaxations. A modiﬁcation of an existing subgraph extraction algorithm is implemented to explore the underlying regional structures in the U.S. airport net- work, with the hope that the resulting localized struc- tures can be optimized independently and reconnected via a "backbone" network to achieve superior network performance.
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Optimal urban networks via mass transportation
Buttazzo, Giuseppe; Stepanov, Eugene; Solimini, Sergio
2009-01-01
Recently much attention has been devoted to the optimization of transportation networks in a given geographic area. One assumes the distributions of population and of services/workplaces (i.e. the network's sources and sinks) are known, as well as the costs of movement with/without the network, and the cost of constructing/maintaining it. Both the long-term optimization and the short-term, "who goes where" optimization are considered. These models can also be adapted for the optimization of other types of networks, such as telecommunications, pipeline or drainage networks. In the monograph we study the most general problem settings, namely, when neither the shape nor even the topology of the network to be constructed is known a priori.
Regulatory Holidays and Optimal Network Expansion
Willems, Bert; Zwart, Gijsbert
2016-01-01
We model the optimal regulation of continuous, irreversible, capacity expansion, in a model in which the regulated network firm has private information about its capacity costs, investments need to be financed out of the firm’s cash flows from selling network access and demand is stochastic. If
Optimization of administrative management costs
Podolchak, N.; Chepil, B.
2015-01-01
It is important to determine the optimal level of administrative costs in order to achieve main targets of any enterprise, to perform definite tasks, to implement these tasks and not to worsen condition and motivation of the workers. Also it is essential to remember about strategic goals in the area of HR on the long run. The refore, the main idea in using optimization model for assessing the effectiveness of management costs will be to find the minimum level of expenses within the given l...
Knoope, M. M. J.|info:eu-repo/dai/nl/364248149; Guijt, W.; Ramirez, A.|info:eu-repo/dai/nl/284852414; Faaij, A. P. C.
In this study, a new cost model is developed for CO2 pipeline transport, which starts with the physical properties of CO2 transport and includes different kinds of steel grades and up-to-date material and construction costs. This pipeline cost model is used for a new developed tool to determine the
Behrens, J
2000-03-01
The key reason for physicians networking in managed care is to get a better coping with uncertainty on action (treatment) decisions. The second reason for networking in managed care are financial benefits grounds. But this reason is very ambivalent. Three different action problems (role conflicts) in managed care network are to solved, which was also in single practices. In the lecture the decision strategies and decision resources has been compared. Observations are done using expert interviews, patient interviews and analysis of documents in USA, Germany and Switzerland. The first problem is the choosing of a cost reduction strategy which is not reducing the effectiveness. Such "ugly" solution strategies like exclusion of "expensive" patients and a rationing of necessary medical services in a kind of McDonalds network of physicians will fail the target. The optimost way is a saving of all unnecessary medical even injourious performances. The chosen cost reduction strategy is not real visible from outside but in fact limited cognizable and controllable. Evidence based health care can be a resource of treatment decisions and could train such decisions but it will not substitute these decisions. The second problem is the making of real family practitioners as gatekeepers. Knowledge about the care system is still not making a real family practitioner, even if this is the minimum condition of their work. Also contractual relationships between insurance and doctor as a gatekeeper or financial incentives for patients are still making not a real family practitioner as a gatekpeeper. Only throughout the trust of patients supported by second opinions is making the real family practitioner as a gatekeeper. "Doctor hopping" could be the reaction by scarcity of trustworthy family practitioners as gatekeepers. The third problem is the choosing of the optimal scale of a network due to the very different optimal size of networks regarding the requirement of risk spreeds, of the
Optimal hub location in pipeline networks
Energy Technology Data Exchange (ETDEWEB)
Dott, D.R.; Wirasinghe, S.C.; Chakma, A. [Univ. of Calgary, Alberta (Canada)
1996-12-31
This paper discusses optimization strategies and techniques for the location of natural gas marketing hubs in the North American gas pipeline network. A hub is a facility at which inbound and outbound network links meet and freight is redirected towards their destinations. Common examples of hubs used in the gas pipeline industry include gas plants, interconnects and market centers. Characteristics of the gas pipeline industry which are relevant to the optimization of transportation costs using hubs are presented. Allocation techniques for solving location-allocation problems are discussed. An outline of the research in process by the authors in the field of optimal gas hub location concludes the paper.
Energy optimization in mobile sensor networks
Yu, Shengwei
consuming negligible amount of energy for mobility cost. For the second problem, the problem is extended to accommodate mobile robotic nodes with energy harvesting capability, which makes it a non-convex optimization problem. The non-convexity issue is tackled by using the existing sequential convex approximation method, based on which we propose a novel procedure of modified sequential convex approximation that has fast convergence speed. For the third problem, the proposed procedure is used to solve another challenging non-convex problem, which results in utilizing mobility and routing simultaneously in mobile robotic sensor networks to prolong the network lifetime. The results indicate that joint design of mobility and routing has an edge over other methods in prolonging network lifetime, which is also the justification for the use of mobility in mobile sensor networks for energy efficiency purpose. For the fourth problem, we include the dynamics of the robotic nodes in the problem by modeling the networked robotic system using hybrid systems theory. A novel distributed method for the networked hybrid system is used to solve the optimal moving trajectories for robotic nodes and optimal network links, which are not answered by previous approaches. Finally, the fact that mobility is more effective in prolonging network lifetime for a data-intensive network leads us to apply our methods to study mobile visual sensor networks, which are useful in many applications. We investigate the joint design of mobility, data routing, and encoding power to help improving the video quality while maximizing the network lifetime. This study leads to a better understanding of the role mobility can play in data-intensive surveillance sensor networks.
Airborne Network Optimization with Dynamic Network Update
2015-03-26
source si and a target ti . For each commodity (si, ki) the commodity specifies a non- negative demand di [5]. The objective of the multi-commodity...queue predictions, and network con- gestion [15]. The implementation of the DRQC uses the Kalman filter to predict the state of the network and optimize
Optimal Fragile Financial Networks
Castiglionesi, F.; Navarro, N.
2007-01-01
We study a financial network characterized by the presence of depositors, banks and their shareholders. Belonging to a financial network is beneficial for both the depositors and banks' shareholders since the return to investment increases with the number of banks connected. However, the network is
On cost-effective communication network designing
Zhang, Guo-Qiang
2010-02-01
How to efficiently design a communication network is a paramount task for network designing and engineering. It is, however, not a single objective optimization process as perceived by most previous researches, i.e., to maximize its transmission capacity, but a multi-objective optimization process, with lowering its cost to be another important objective. These two objectives are often contradictive in that optimizing one objective may deteriorate the other. After a deep investigation of the impact that network topology, node capability scheme and routing algorithm as well as their interplays have on the two objectives, this letter presents a systematic approach to achieve a cost-effective design by carefully choosing the three designing aspects. Only when routing algorithm and node capability scheme are elegantly chosen can BA-like scale-free networks have the potential of achieving good tradeoff between the two objectives. Random networks, on the other hand, have the built-in character for a cost-effective design, especially when other aspects cannot be determined beforehand.
Modeling and optimization of potable water network
Energy Technology Data Exchange (ETDEWEB)
Djebedjian, B.; Rayan, M.A. [Mansoura Univ., El-Mansoura (Egypt); Herrick, A. [Suez Canal Authority, Ismailia (Egypt)
2000-07-01
Software was developed in order to optimize the design of water distribution systems and pipe networks. While satisfying all the constraints imposed such as pipe diameter and nodal pressure, it was based on a mathematical model treating looped networks. The optimum network configuration and cost are determined considering parameters like pipe diameter, flow rate, corresponding pressure and hydraulic losses. It must be understood that minimum cost is relative to the different objective functions selected. The determination of the proper objective function often depends on the operating policies of a particular company. The solution for the optimization technique was obtained by using a non-linear technique. To solve the optimal design of network, the model was derived using the sequential unconstrained minimization technique (SUMT) of Fiacco and McCormick, which decreased the number of iterations required. The pipe diameters initially assumed were successively adjusted to correspond to the existing commercial pipe diameters. The technique was then applied to a two-loop network without pumps or valves. Fed by gravity, it comprised eight pipes, 1000 m long each. The first evaluation of the method proved satisfactory. As with other methods, it failed to find the global optimum. In the future, research efforts will be directed to the optimization of networks with pumps and reservoirs. 24 refs., 3 tabs., 1 fig.
Optimal Brain Surgeon on Artificial Neural Networks in
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine
2012-01-01
It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...
Combinatorial optimization networks and matroids
Lawler, Eugene
2011-01-01
Perceptively written text examines optimization problems that can be formulated in terms of networks and algebraic structures called matroids. Chapters cover shortest paths, network flows, bipartite matching, nonbipartite matching, matroids and the greedy algorithm, matroid intersections, and the matroid parity problems. A suitable text or reference for courses in combinatorial computing and concrete computational complexity in departments of computer science and mathematics.
Sharing cost in social community networks
DEFF Research Database (Denmark)
Pal, Ranjan; Elango, Divya; Wardana, Satya Ardhy
2012-01-01
their deployment in a residential locality. Our proposed mechanism accounts for heterogeneous user preferences towards different router features and comes up with the optimal (feature-set, user costs) router blueprint that satisﬁes each user in a locality, in turn motivating them to buy routers and thereby improve......Wireless social community networks (WSCNs) is an emerging technology that operate in the unlicensed spectrum and have been created as an alternative to cellular wireless networks for providing low-cost, high speed wireless data access in urban areas. WSCNs is an upcoming idea that is starting...... reﬂect their slow progress in capturing the WiFi router market. In this paper, we look at a router design and cost sharing problem in WSCNs to improve deployment. We devise a simple to implement, successful, budget-balanced, ex-post efﬁcient, and individually rational auction-based mechanism...
Location based Network Optimizations for Mobile Wireless Networks
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen
selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...
Fair Optimization and Networks: A Survey
Directory of Open Access Journals (Sweden)
Wlodzimierz Ogryczak
2014-01-01
Full Text Available Optimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be provided with an adequate share of the system’s services. Still, due to system operations-dependant constraints, fair treatment of the entities does not directly imply that each of them is assigned equal amount of the services. That leads to concepts of fair optimization expressed by the equitable models that represent inequality averse optimization rather than strict inequality minimization; a particular widely applied example of that concept is the so-called lexicographic maximin optimization (max-min fairness. The fair optimization methodology delivers a variety of techniques to generate fair and efficient solutions. This paper reviews fair optimization models and methods applied to systems that are based on some kind of network of connections and dependencies, especially, fair optimization methods for the location problems and for the resource allocation problems in communication networks.
Cost-Optimal ATCs in Zonal Electricity Markets
DEFF Research Database (Denmark)
Jensen, Tue Vissing; Kazempour, Jalal; Pinson, Pierre
2017-01-01
from the physical ATCs based on security indices only typically used in zonal electricity markets today. Determining cost-optimal ATCs requires viewing ATCs as an endogenous market construct, and leads naturally to the definition of a market entity whose responsibility is to optimize ATCs....... The optimization problem which this entity solves is a stochastic bilevel problem, which we decompose to yield a computationally tractable formulation. We show that cost-optimal ATCs depend non-trivially on the underlying network structure, and the problem of finding a setof cost-optimal ATCs is in general non...... by a factor of 2 or more, and ATCs which are zero between well-connected areas.Our results indicate that the perceived efficiency gap between zonal and nodal markets may be exagerrated if non-optimal ATCs are used....
Optimization in a Networked Economy
Directory of Open Access Journals (Sweden)
Ahmet Sekreter
2017-10-01
Full Text Available An age of network has been living for the last decades. The information technologies have been used by hundreds of millions of users. These technologies are enabling to connect businesses and economic activities. One of the characteristics of the networked economy is the amount of data that produced due to the interlinking of firms, individuals, processes by businesses, and economic activities. Another issue with the networked economy is the complexity of the data. Extraction of the knowledge from the networked economy has challenges by the traditional approach since data is large scale, second decentralized, and third they connect many heterogeneous agents. The challenges can be overcome by the new optimization methods including human element or the social interactions with technological infrastructure.
Cost effective campaigning in social networks
Kotnis, Bhushan; Kuri, Joy
2016-05-01
Campaigners are increasingly using online social networking platforms for promoting products, ideas and information. A popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts, cash backs, or social recognition. Due to budget constraints on scarce resources such as money and manpower, it may not be possible to provide incentives for the entire population, and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size. We aim to do the same by formulating and solving an optimization problem using percolation theory. In particular, we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size. We also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget. The optimization problem turns out to be non trivial; it involves quantities that need to be computed by numerically solving a fixed point equation. Our primary contribution is, that for a fairly general cost structure, we show that the optimization problems can be solved by solving a simple linear program. We believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind.
Phase transitions in Pareto optimal complex networks.
Seoane, Luís F; Solé, Ricard
2015-09-01
The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.
Optimization of Actuating Origami Networks
Buskohl, Philip; Fuchi, Kazuko; Bazzan, Giorgio; Joo, James; Gregory, Reich; Vaia, Richard
2015-03-01
Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form, function and mobility of the structure. By leveraging design concepts from action origami, a subset of origami art focused on kinematic mechanisms, reversible folding patterns for applications such as solar array packaging, tunable antennae, and deployable sensing platforms may be designed. However, the enormity of the design space and the need to identify the requisite actuation forces within the structure places a severe limitation on design strategies based on intuition and geometry alone. The present work proposes a topology optimization method, using truss and frame element analysis, to distribute foldline mechanical properties within a reference crease pattern. Known actuating patterns are placed within a reference grid and the optimizer adjusts the fold stiffness of the network to optimally connect them. Design objectives may include a target motion, stress level, or mechanical energy distribution. Results include the validation of known action origami structures and their optimal connectivity within a larger network. This design suite offers an important step toward systematic incorporation of origami design concepts into new, novel and reconfigurable engineering devices. This research is supported under the Air Force Office of Scientific Research (AFOSR) funding, LRIR 13RQ02COR.
UMTS network planning, optimization, and inter-operation with GSM
Rahnema, Moe
2008-01-01
UMTS Network Planning, Optimization, and Inter-Operation with GSM is an accessible, one-stop reference to help engineers effectively reduce the time and costs involved in UMTS deployment and optimization. Rahnema includes detailed coverage from both a theoretical and practical perspective on the planning and optimization aspects of UMTS, and a number of other new techniques to help operators get the most out of their networks. Provides an end-to-end perspective, from network design to optimizationIncorporates the hands-on experiences of numerous researchersSingle
Optimization of investments in gas networks
International Nuclear Information System (INIS)
Andre, J.
2010-09-01
The natural gas networks require very important investments to cope with a still growing demand and to satisfy the new regulatory constraints. The gas market deregulation imposed to the gas network operators, first, transparency rules of a natural monopoly to justify their costs and ultimately their tariffs, and, second, market fluidity objectives in order to facilitate access for competition to the end-users. These major investments are the main reasons for the use of optimization techniques aiming at reducing the costs. Due to the discrete choices (investment location, limited choice of additional capacities, timing) crossed with physical non linear constraints (flow/pressures relations in the pipe or operating ranges of compressors), the programs to solve are Large Mixed Non Linear Programs (MINLP). As these types of programs are known to be hard to solve exactly in polynomial times (NP-hard), advanced optimization methods have to be implemented to obtain realistic results. The objectives of this thesis are threefold. First, one states several investment problems modeling of natural gas networks from industrial world motivations. Second, one identifies the most suitable methods and algorithms to the formulated problems. Third, one exposes the main advantages and drawbacks of these methods with the help of numerical applications on real cases. (author)
Optimizations in Heterogeneous Mobile Networks
DEFF Research Database (Denmark)
Popovska Avramova, Andrijana
nodes. The independent control of the user’s transmit power at each node may cause degradation of the overall performance. In this line, a dedicated study of power distribution among the carriers is performed. An optimization of the power allocation is proposed and evaluated. The results show...... significant performance improvement to the achieved user throughput in low as well as in high loads in the cell. The flow control of the data between the nodes is another challenge for effective aggregation of the resources in case of dual connectivity. As such, this thesis discusses the challenges...... with the densification of the base stations, bring into a very complex network management and operation control for the mobile operators. Furthermore, the need to provide always best connection and service with high quality demands for a joint overall network resource management. This thesis addresses this challenge...
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2013-01-01
In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer...... and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...... and it is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature...
Protecting infrastructure networks from cost-based attacks
International Nuclear Information System (INIS)
Wang Xingang; Guan Shuguang; Lai, Choy Heng
2009-01-01
It is well known that heterogeneous networks are vulnerable to the intentional removal of a small fraction of highly connected or loaded nodes, implying that to protect the network effectively, the important nodes should be allocated more defense resource than the others. However, if too much resource is allocated to the few important nodes, the numerous less-important nodes will be less protected, which if attacked together can still lead to devastating damage. A natural question is therefore how to efficiently distribute the limited defense resource among the network nodes such that the network damage is minimized against any attack strategy. In this paper, taking into account the factor of attack cost, the problem of network security is reconsidered in terms of efficient network defense against cost-based attacks. The results show that, for a general complex network, there exists an optimal distribution of the defense resource with which the network is best protected from cost-based attacks. Furthermore, it is found that the configuration of the optimal defense is dependent on the network parameters. Specifically, networks of larger size, sparser connection and more heterogeneous structure will more likely benefit from the defense optimization.
Resilience-based optimal design of water distribution network
Suribabu, C. R.
2017-11-01
Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.
Quantized hopfield networks for reliability optimization
International Nuclear Information System (INIS)
Nourelfath, Mustapha; Nahas, Nabil
2003-01-01
The use of neural networks in the reliability optimization field is rare. This paper presents an application of a recent kind of neural networks in a reliability optimization problem for a series system with multiple-choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget. The problem is formulated as a nonlinear binary integer programming problem and characterized as an NP-hard problem. Our design of neural network to solve efficiently this problem is based on a quantized Hopfield network. This network allows us to obtain optimal design solutions very frequently and much more quickly than others Hopfield networks
Cost Overrun Optimism: Fact or Fiction
2016-02-29
Base, OH. Homgren, C. T. (1990). In G. Foster (Ed.), Cost accounting : A managerial emphasis (7th ed.). Englewood Cliffs, NJ: Prentice Hall. Morrison... Accounting Office. Gansler, J. S. (1989). Affording defense. Cambridge, MA: The MIT Press. Heise, S. R. (1991). A review of cost performance index...Image designed by Diane Fleischer Cost Overrun Optimism: FACT or FICTION? Maj David D. Christensen, USAF Program managers are advocates by
Optimal Control of Interdependent Epidemics in Complex Networks
Chen, Juntao; Zhang, Rui; Zhu, Quanyan
2017-01-01
Optimal control of interdependent epidemics spreading over complex networks is a critical issue. We first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed control strategy globally optimizes the trade-off between the control cost and the severity of epidemics in the network. A gradient descent algorithm based on a fixed point itera...
AS Migration and Optimization of the Power Integrated Data Network
Zhou, Junjie; Ke, Yue
2018-03-01
In the transformation process of data integration network, the impact on the business has always been the most important reference factor to measure the quality of network transformation. With the importance of the data network carrying business, we must put forward specific design proposals during the transformation, and conduct a large number of demonstration and practice to ensure that the transformation program meets the requirements of the enterprise data network. This paper mainly demonstrates the scheme of over-migrating point-to-point access equipment in the reconstruction project of power data comprehensive network to migrate the BGP autonomous domain to the specified domain defined in the industrial standard, and to smooth the intranet OSPF protocol Migration into ISIS agreement. Through the optimization design, eventually making electric power data network performance was improved on traffic forwarding, traffic forwarding path optimized, extensibility, get larger, lower risk of potential loop, the network stability was improved, and operational cost savings, etc.
SOCIAL NETWORK OPTIMIZATION A NEW METHAHEURISTIC FOR GENERAL OPTIMIZATION PROBLEMS
Directory of Open Access Journals (Sweden)
Hassan Sherafat
2017-12-01
Full Text Available In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.
Cost Optimization of Product Families using Analytic Cost Models
DEFF Research Database (Denmark)
Brunø, Thomas Ditlev; Nielsen, Peter
2012-01-01
This paper presents a new method for analysing the cost structure of a mass customized product family. The method uses linear regression and backwards selection to reduce the complexity of a data set describing a number of historical product configurations and incurred costs. By reducing the data...... set, the configuration variables which best describe the variation in product costs are identified. The method is tested using data from a Danish manufacturing company and the results indicate that the method is able to identify the most critical configuration variables. The method can be applied...... in product family redesign projects focusing on cost reduction to identify which modules contribute the most to cost variation and should thus be optimized....
Designing optimal greenhouse gas monitoring networks for Australia
Ziehn, T.; Law, R. M.; Rayner, P. J.; Roff, G.
2016-01-01
Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Optimal topologies for maximizing network transmission capacity
Chen, Zhenhao; Wu, Jiajing; Rong, Zhihai; Tse, Chi K.
2018-04-01
It has been widely demonstrated that the structure of a network is a major factor that affects its traffic dynamics. In this work, we try to identify the optimal topologies for maximizing the network transmission capacity, as well as to build a clear relationship between structural features of a network and the transmission performance in terms of traffic delivery. We propose an approach for designing optimal network topologies against traffic congestion by link rewiring and apply them on the Barabási-Albert scale-free, static scale-free and Internet Autonomous System-level networks. Furthermore, we analyze the optimized networks using complex network parameters that characterize the structure of networks, and our simulation results suggest that an optimal network for traffic transmission is more likely to have a core-periphery structure. However, assortative mixing and the rich-club phenomenon may have negative impacts on network performance. Based on the observations of the optimized networks, we propose an efficient method to improve the transmission capacity of large-scale networks.
Optimizing the next generation optical access networks
DEFF Research Database (Denmark)
Amaya Fernández, Ferney Orlando; Soto, Ana Cardenas; Tafur Monroy, Idelfonso
2009-01-01
Several issues in the design and optimization of the next generation optical access network (NG-OAN) are presented. The noise, the distortion and the fiber optic nonlinearities are considered to optimize the video distribution link in a passive optical network (PON). A discussion of the effect...
Wang, Hui; Liu, Chunyue; Rong, Luge; Wang, Xiaoxu; Sun, Lina; Luo, Qing; Wu, Hao
2018-01-09
River monitoring networks play an important role in water environmental management and assessment, and it is critical to develop an appropriate method to optimize the monitoring network. In this study, an effective method was proposed based on the attainment rate of National Grade III water quality, optimal partition analysis and Euclidean distance, and Hun River was taken as a method validation case. There were 7 sampling sites in the monitoring network of the Hun River, and 17 monitoring items were analyzed once a month during January 2009 to December 2010. The results showed that the main monitoring items in the surface water of Hun River were ammonia nitrogen (NH 4 + -N), chemical oxygen demand, and biochemical oxygen demand. After optimization, the required number of monitoring sites was reduced from seven to three, and 57% of the cost was saved. In addition, there were no significant differences between non-optimized and optimized monitoring networks, and the optimized monitoring networks could correctly represent the original monitoring network. The duplicate setting degree of monitoring sites decreased after optimization, and the rationality of the monitoring network was improved. Therefore, the optimal method was identified as feasible, efficient, and economic.
Optimization of temporal networks under uncertainty
Wiesemann, Wolfram
2012-01-01
Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization probl
Cost optimal levels for energy performance requirements
DEFF Research Database (Denmark)
Thomsen, Kirsten Engelund; Aggerholm, Søren; Kluttig-Erhorn, Heike
This report summarises the work done within the Concerted Action EPBD from December 2010 to April 2011 in order to feed into the European Commission's proposal for a common European procedure for a Cost-Optimal methodology under the Directive on the Energy Performance of Buildings (recast) 2010/3...
Directory of Open Access Journals (Sweden)
J. Trdlicka
2010-12-01
Full Text Available This work proposes a distributed algorithm for energy optimal routing in a wireless sensor network. The routing problem is described as a mathematical problem by the minimum-cost multi-commodity network flow problem. Due to the separability of the problem, we use the duality theorem to derive the distributed algorithm. The algorithm computes the energy optimal routing in the network without any central node or knowledge of the whole network structure. Each node only needs to know the flow which is supposed to send or receive and the costs and capacities of the neighboring links. An evaluation of the presented algorithm on benchmarks for the energy optimal data flow routing in sensor networks with up to 100 nodes is presented.
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Optimal Design of Gravitational Sewer Networks with General Cellular Automata
Directory of Open Access Journals (Sweden)
Mohammad Hadi Afshar
2014-05-01
Full Text Available In this paper, a Cellular Automata method is applied for the optimal design of sewer networks. The solution of sewer network optimization problems requires the determination of pipe diameters and average pipe cover depths, minimizing the total cost of the sewer network subject to operational constraints. In this paper, the network nodes and upstream and downstream pipe cover depths are considered as CA cells and cell states, respectively, and the links around each cell are taken into account as neighborhood. The proposed method is a general and flexible method for the optimization of sewer networks as it can be used to optimally design both gravity and pumped network due to the use of pipe nodal cover depths as the decision variables. The proposed method is tested against two gravitational sewer networks and the comparison of results with other methods such as Genetic algorithm, Cellular Automata, Ant Colony Optimization Algorithm and Particle Swarm Optimization show the efficiency and effectiveness of the proposed method.
Lange, Christoph; Hülsermann, Ralf; Kosiankowski, Dirk; Geilhardt, Frank; Gladisch, Andreas
2010-01-01
The increasing demand for higher bit rates in access networks requires fiber deployment closer to the subscriber resulting in fiber-to-the-home (FTTH) access networks. Besides higher access bit rates optical access network infrastructure and related technologies enable the network operator to establish larger service areas resulting in a simplified network structure with a lower number of network nodes. By changing the network structure network operators want to benefit from a changed network cost structure by decreasing in short and mid term the upfront investments for network equipment due to concentration effects as well as by reducing the energy costs due to a higher energy efficiency of large network sites housing a high amount of network equipment. In long term also savings in operational expenditures (OpEx) due to the closing of central office (CO) sites are expected. In this paper different architectures for optical access networks basing on state-of-the-art technology are analyzed with respect to network installation costs and power consumption in the context of access node consolidation. Network planning and dimensioning results are calculated for a realistic network scenario of Germany. All node consolidation scenarios are compared against a gigabit capable passive optical network (GPON) based FTTH access network operated from the conventional CO sites. The results show that a moderate reduction of the number of access nodes may be beneficial since in that case the capital expenditures (CapEx) do not rise extraordinarily and savings in OpEx related to the access nodes are expected. The total power consumption does not change significantly with decreasing number of access nodes but clustering effects enable a more energyefficient network operation and optimized power purchase order quantities leading to benefits in energy costs.
Optimizing Teleportation Cost in Distributed Quantum Circuits
Zomorodi-Moghadam, Mariam; Houshmand, Mahboobeh; Houshmand, Monireh
2018-03-01
The presented work provides a procedure for optimizing the communication cost of a distributed quantum circuit (DQC) in terms of the number of qubit teleportations. Because of technology limitations which do not allow large quantum computers to work as a single processing element, distributed quantum computation is an appropriate solution to overcome this difficulty. Previous studies have applied ad-hoc solutions to distribute a quantum system for special cases and applications. In this study, a general approach is proposed to optimize the number of teleportations for a DQC consisting of two spatially separated and long-distance quantum subsystems. To this end, different configurations of locations for executing gates whose qubits are in distinct subsystems are considered and for each of these configurations, the proposed algorithm is run to find the minimum number of required teleportations. Finally, the configuration which leads to the minimum number of teleportations is reported. The proposed method can be used as an automated procedure to find the configuration with the optimal communication cost for the DQC. This cost can be used as a basic measure of the communication cost for future works in the distributed quantum circuits.
Optimization model for the design of distributed wastewater treatment networks
Directory of Open Access Journals (Sweden)
Ibrić Nidret
2012-01-01
Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.
WiMAX network performance monitoring & optimization
DEFF Research Database (Denmark)
Zhang, Qi; Dam, H
2008-01-01
frequency reuse, capacity planning, proper network dimensioning, multi-class data services and so on. Furthermore, as a small operator we also want to reduce the demand for sophisticated technicians and man labour hours. To meet these critical demands, we design a generic integrated network performance......In this paper we present our WiMAX (worldwide interoperability for microwave access) network performance monitoring and optimization solution. As a new and small WiMAX network operator, there are many demanding issues that we have to deal with, such as limited available frequency resource, tight...... this integrated network performance monitoring and optimization system in our WiMAX networks. This integrated monitoring and optimization system has such good flexibility and scalability that individual function component can be used by other operators with special needs and more advanced function components can...
A probabilistic computational framework for bridge network optimal maintenance scheduling
International Nuclear Information System (INIS)
Bocchini, Paolo; Frangopol, Dan M.
2011-01-01
This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.
Cost optimization model and its heuristic genetic algorithms
International Nuclear Information System (INIS)
Liu Wei; Wang Yongqing; Guo Jilin
1999-01-01
Interest and escalation are large quantity in proportion to the cost of nuclear power plant construction. In order to optimize the cost, the mathematics model of cost optimization for nuclear power plant construction was proposed, which takes the maximum net present value as the optimization goal. The model is based on the activity networks of the project and is an NP problem. A heuristic genetic algorithms (HGAs) for the model was introduced. In the algorithms, a solution is represented with a string of numbers each of which denotes the priority of each activity for assigned resources. The HGAs with this encoding method can overcome the difficulty which is harder to get feasible solutions when using the traditional GAs to solve the model. The critical path of the activity networks is figured out with the concept of predecessor matrix. An example was computed with the HGAP programmed in C language. The results indicate that the model is suitable for the objectiveness, the algorithms is effective to solve the model
Optimal control of epidemic information dissemination over networks.
Chen, Pin-Yu; Cheng, Shin-Ming; Chen, Kwang-Cheng
2014-12-01
Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.
International Nuclear Information System (INIS)
Hwangbo, Soonho; Lee, In-Beum; Han, Jeehoon
2014-01-01
Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network
Optimization of robustness of interdependent network controllability by redundant design.
Directory of Open Access Journals (Sweden)
Zenghu Zhang
Full Text Available Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy or DBS (degree based strategy for node backup and HDF(high degree first for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability.
Robust Optimization of Fourth Party Logistics Network Design under Disruptions
Directory of Open Access Journals (Sweden)
Jia Li
2015-01-01
Full Text Available The Fourth Party Logistics (4PL network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA and the genetic algorithm (GA are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.
CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Smita K Magdum
2017-10-01
Full Text Available Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Power consumption optimization strategy for wireless networks
DEFF Research Database (Denmark)
Cornean, Horia; Kumar, Sanjay; Marchetti, Nicola
2011-01-01
in order to reduce the total power consumption in a multi cellular network. We present an algorithm for power optimization under no interference and in presence of interference conditions, targeting to maximize the network capacity. The convergence of the algorithm is guaranteed if the interference...
Brocade: Optimal flow placement in SDN networks
CERN. Geneva
2015-01-01
Today' network poses several challanges to network providers. These challanges fall in to a variety of areas ranging from determining efficient utilization of network bandwidth to finding out which user applications consume majority of network resources. Also, how to protect a given network from volumetric and botnet attacks. Optimal placement of flows deal with identifying network issues and addressing them in a real-time. The overall solution helps in building new services where a network is more secure and more efficient. Benefits derived as a result are increased network efficiency due to better capacity and resource planning, better security with real-time threat mitigation, and improved user experience as a result of increased service velocity.
GASIFICATION PLANT COST AND PERFORMANCE OPTIMIZATION
Energy Technology Data Exchange (ETDEWEB)
Samuel S. Tam
2002-05-01
The goal of this series of design and estimating efforts was to start from the as-built design and actual operating data from the DOE sponsored Wabash River Coal Gasification Repowering Project and to develop optimized designs for several coal and petroleum coke IGCC power and coproduction projects. First, the team developed a design for a grass-roots plant equivalent to the Wabash River Coal Gasification Repowering Project to provide a starting point and a detailed mid-year 2000 cost estimate based on the actual as-built plant design and subsequent modifications (Subtask 1.1). This unoptimized plant has a thermal efficiency of 38.3% (HHV) and a mid-year 2000 EPC cost of 1,681 $/kW. This design was enlarged and modified to become a Petroleum Coke IGCC Coproduction Plant (Subtask 1.2) that produces hydrogen, industrial grade steam, and fuel gas for an adjacent Gulf Coast petroleum refinery in addition to export power. A structured Value Improving Practices (VIP) approach was applied to reduce costs and improve performance. The base case (Subtask 1.3) Optimized Petroleum Coke IGCC Coproduction Plant increased the power output by 16% and reduced the plant cost by 23%. The study looked at several options for gasifier sparing to enhance availability. Subtask 1.9 produced a detailed report on this availability analyses study. The Subtask 1.3 Next Plant, which retains the preferred spare gasification train approach, only reduced the cost by about 21%, but it has the highest availability (94.6%) and produces power at 30 $/MW-hr (at a 12% ROI). Thus, such a coke-fueled IGCC coproduction plant could fill a near term niche market. In all cases, the emissions performance of these plants is superior to the Wabash River project. Subtasks 1.5A and B developed designs for single-train coal and coke-fueled power plants. This side-by-side comparison of these plants, which contain the Subtask 1.3 VIP enhancements, showed their similarity both in design and cost (1,318 $/kW for the
Network resilience against intelligent attacks constrained by the degree-dependent node removal cost
International Nuclear Information System (INIS)
Annibale, A; Coolen, A C C; Bianconi, G
2010-01-01
We study the resilience of complex networks against attacks in which nodes are targeted intelligently, but where disabling a node has a cost to the attacker which depends on its degree. Attackers have to meet these costs with limited resources, which constrains their actions. A network's integrity is quantified in terms of the efficacy of the process that it supports. We calculate how the optimal attack strategy and the most attack-resistant network degree statistics depend on the node removal cost function and the attack resources. The resilience of networks against intelligent attacks is found to depend strongly on the node removal cost function faced by the attacker. In particular, if node removal costs increase sufficiently fast with the node degree, power law networks are found to be more resilient than Poissonian ones, even against optimized intelligent attacks. For cost functions increasing quadratically in the node degrees, intelligent attackers cannot damage the network more than random damages would.
Small cell networks deployment, management, and optimization
Claussen, Holger; Ho, Lester; Razavi, Rouzbeh; Kucera, Stepan
2018-01-01
Small Cell Networks: Deployment, Management, and Optimization addresses key problems of the cellular network evolution towards HetNets. It focuses on the latest developments in heterogeneous and small cell networks, as well as their deployment, operation, and maintenance. It also covers the full spectrum of the topic, from academic, research, and business to the practice of HetNets in a coherent manner. Additionally, it provides complete and practical guidelines to vendors and operators interested in deploying small cells. The first comprehensive book written by well-known researchers and engineers from Nokia Bell Labs, Small Cell Networks begins with an introduction to the subject--offering chapters on capacity scaling and key requirements of future networks. It then moves on to sections on coverage and capacity optimization, and interference management. From there, the book covers mobility management, energy efficiency, and small cell deployment, ending with a section devoted to future trends and applicat...
Optimization of hot water transport and distribution networks by analytical method: OPTAL program
International Nuclear Information System (INIS)
Barreau, Alain; Caizergues, Robert; Moret-Bailly, Jean
1977-06-01
This report presents optimization studies of hot water transport and distribution network by minimizing operating cost. Analytical optimization is used: Lagrange's method of undetermined multipliers. Optimum diameter of each pipe is calculated for minimum network operating cost. The characteristics of the computer program used for calculations, OPTAL, are given in this report. An example of network is calculated and described: 52 branches and 27 customers. Results are discussed [fr
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
. The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...
Deterministic network interdiction optimization via an evolutionary approach
International Nuclear Information System (INIS)
Rocco S, Claudio M.; Ramirez-Marquez, Jose Emmanuel
2009-01-01
This paper introduces an evolutionary optimization approach that can be readily applied to solve deterministic network interdiction problems. The network interdiction problem solved considers the minimization of the maximum flow that can be transmitted between a source node and a sink node for a fixed network design when there is a limited amount of resources available to interdict network links. Furthermore, the model assumes that the nominal capacity of each network link and the cost associated with their interdiction can change from link to link. For this problem, the solution approach developed is based on three steps that use: (1) Monte Carlo simulation, to generate potential network interdiction strategies, (2) Ford-Fulkerson algorithm for maximum s-t flow, to analyze strategies' maximum source-sink flow and, (3) an evolutionary optimization technique to define, in probabilistic terms, how likely a link is to appear in the final interdiction strategy. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate the approach. In terms of computational effort, the results illustrate that solutions are obtained from a significantly restricted solution search space. Finally, the authors discuss the need for a reliability perspective to network interdiction, so that solutions developed address more realistic scenarios of such problem
Costs and benefits of realism and optimism.
Bortolotti, Lisa; Antrobus, Magdalena
2015-03-01
What is the relationship between rationality and mental health? By considering the psychological literature on depressive realism and unrealistic optimism, it was hypothesized that, in the context of judgments about the self, accurate cognitions are psychologically maladaptive and inaccurate cognitions are psychologically adaptive. Recent studies recommend being cautious in drawing any general conclusion about the style of thinking and mental health. Recent investigations suggest that people with depressive symptoms are more accurate than controls in tasks involving time perception and estimates of personal circumstances, but not in other tasks. Unrealistic optimism remains a robust phenomenon across a variety of tasks and domains, and researchers are starting to explore its neural bases. However, the challenge is to determine to what extent and in what way unrealistic optimism is beneficial. We should revisit the hypothesis that optimistic cognitions are psychologically adaptive, whereas realistic thinking is not. Realistic beliefs and expectations can be conducive to wellbeing and good functioning, and wildly optimistic cognitions have considerable psychological costs.
Costs and benefits of realism and optimism
Bortolotti, Lisa; Antrobus, Magdalena
2015-01-01
Purpose of review What is the relationship between rationality and mental health? By considering the psychological literature on depressive realism and unrealistic optimism, it was hypothesized that, in the context of judgments about the self, accurate cognitions are psychologically maladaptive and inaccurate cognitions are psychologically adaptive. Recent studies recommend being cautious in drawing any general conclusion about the style of thinking and mental health. Recent findings Recent investigations suggest that people with depressive symptoms are more accurate than controls in tasks involving time perception and estimates of personal circumstances, but not in other tasks. Unrealistic optimism remains a robust phenomenon across a variety of tasks and domains, and researchers are starting to explore its neural bases. However, the challenge is to determine to what extent and in what way unrealistic optimism is beneficial. Summary We should revisit the hypothesis that optimistic cognitions are psychologically adaptive, whereas realistic thinking is not. Realistic beliefs and expectations can be conducive to wellbeing and good functioning, and wildly optimistic cognitions have considerable psychological costs. PMID:25594418
Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks
Directory of Open Access Journals (Sweden)
M. Hadi Amini
2018-01-01
Full Text Available Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. The EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs’ charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e., optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks.
Multi-Objective Design Of Optimal Greenhouse Gas Observation Networks
Lucas, D. D.; Bergmann, D. J.; Cameron-Smith, P. J.; Gard, E.; Guilderson, T. P.; Rotman, D.; Stolaroff, J. K.
2010-12-01
One of the primary scientific functions of a Greenhouse Gas Information System (GHGIS) is to infer GHG source emission rates and their uncertainties by combining measurements from an observational network with atmospheric transport modeling. Certain features of the observational networks that serve as inputs to a GHGIS --for example, sampling location and frequency-- can greatly impact the accuracy of the retrieved GHG emissions. Observation System Simulation Experiments (OSSEs) provide a framework to characterize emission uncertainties associated with a given network configuration. By minimizing these uncertainties, OSSEs can be used to determine optimal sampling strategies. Designing a real-world GHGIS observing network, however, will involve multiple, conflicting objectives; there will be trade-offs between sampling density, coverage and measurement costs. To address these issues, we have added multi-objective optimization capabilities to OSSEs. We demonstrate these capabilities by quantifying the trade-offs between retrieval error and measurement costs for a prototype GHGIS, and deriving GHG observing networks that are Pareto optimal. [LLNL-ABS-452333: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Kolosionis, Konstantinos; Papadopoulou, Maria P.
2017-04-01
Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.
Optimal Information Processing in Biochemical Networks
Wiggins, Chris
2012-02-01
A variety of experimental results over the past decades provide examples of near-optimal information processing in biological networks, including in biochemical and transcriptional regulatory networks. Computing information-theoretic quantities requires first choosing or computing the joint probability distribution describing multiple nodes in such a network --- for example, representing the probability distribution of finding an integer copy number of each of two interacting reactants or gene products while respecting the `intrinsic' small copy number noise constraining information transmission at the scale of the cell. I'll given an overview of some recent analytic and numerical work facilitating calculation of such joint distributions and the associated information, which in turn makes possible numerical optimization of information flow in models of noisy regulatory and biochemical networks. Illustrating cases include quantification of form-function relations, ideal design of regulatory cascades, and response to oscillatory driving.
Optimization and Control of Communication Networks
Chiang, Mung; Low, Steven
2005-01-01
Recently, there has been a surge in research activities that utilize the power of recent developments in nonlinear optimization to tackle a wide scope of work in the analysis and design of communication systems, touching every layer of the layered network architecture, and resulting in both intellectual and practical impacts significantly beyond the earlier frameworks. These research activities are driven by both new demands in the areas of communications and networking, and n...
Directory of Open Access Journals (Sweden)
Nur Faziera Napis
2018-05-01
Full Text Available The presence of optimized distributed generation (DG with suitable distribution network reconfiguration (DNR in the electrical distribution network has an advantage for voltage support, power losses reduction, deferment of new transmission line and distribution structure and system stability improvement. However, installation of a DG unit at non-optimal size with non-optimal DNR may lead to higher power losses, power quality problem, voltage instability and incremental of operational cost. Thus, an appropriate DG and DNR planning are essential and are considered as an objective of this research. An effective heuristic optimization technique named as improved evolutionary particle swarm optimization (IEPSO is proposed in this research. The objective function is formulated to minimize the total power losses (TPL and to improve the voltage stability index (VSI. The voltage stability index is determined for three load demand levels namely light load, nominal load, and heavy load with proper optimal DNR and DG sizing. The performance of the proposed technique is compared with other optimization techniques, namely particle swarm optimization (PSO and iteration particle swarm optimization (IPSO. Four case studies on IEEE 33-bus and IEEE 69-bus distribution systems have been conducted to validate the effectiveness of the proposed IEPSO. The optimization results show that, the best achievement is done by IEPSO technique with power losses reduction up to 79.26%, and 58.41% improvement in the voltage stability index. Moreover, IEPSO has the fastest computational time for all load conditions as compared to other algorithms.
Management and optimization of the CPCU network working
Energy Technology Data Exchange (ETDEWEB)
Silvain, D. (Compagnie Parisienne de Chauffage Urbain, 75 - Paris (FR))
1991-10-01
The CPCU steam distribution network is supplemented by a return network for the condensation water. The data system installed in 1988 provides, for the real time, management of the function of the two networks and a reduction in production costs. For the steam, data required in the network, the boiler houses and from external sources are processed by local network of five microprocessors and permit: - with time delay: technical and economic production optimizing calculations, or forecasts, for the following day, of the total required output and the procedure necessary for supplying this at the lowest cost; - in real time: on the basis of the forecasts for the previous day, creating the production instructions for the boiler houses and the instructions for the network remote control elements; - in case of an unexpected occurrence: immediate creation of new operating forecasts for the boiler houses for the establishing management data in real time. For the water, the system forecasts the volume to be returned to the boiler depending on the quantity of steam to be produced. Subsequently, an analysis is carried out in real time of pressures and outputs measured in the network for deriving valve movements and the pump stop/start procedure for guaranteeing the return of the water. The architecture, basic principles and software developed for this application can be used in other steam or water networks and, in a general manner, are adaptable for the management of any complex multi-supplier or multicustomer systems.
Cost Optimal System Identification Experiment Design
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
A structural system identification experiment design method is formulated in the light of decision theory, structural reliability theory and optimization theory. The experiment design is based on a preposterior analysis, well-known from the classical decision theory. I.e. the decisions concerning...... reflecting the cost of the experiment and the value of obtained additional information. An example concerning design of an experiment for parametric identification of a single degree of freedom structural system shows the applicability of the experiment design method....... the experiment design are not based on obtained experimental data. Instead the decisions are based on the expected experimental data assumed to be obtained from the measurements, estimated based on prior information and engineering judgement. The design method provides a system identification experiment design...
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
Shahriari, Mohammadreza
2016-06-01
The time-cost tradeoff problem is one of the most important and applicable problems in project scheduling area. There are many factors that force the mangers to crash the time. This factor could be early utilization, early commissioning and operation, improving the project cash flow, avoiding unfavorable weather conditions, compensating the delays, and so on. Since there is a need to allocate extra resources to short the finishing time of project and the project managers are intended to spend the lowest possible amount of money and achieve the maximum crashing time, as a result, both direct and indirect costs will be influenced in the project, and here, we are facing into the time value of money. It means that when we crash the starting activities in a project, the extra investment will be tied in until the end date of the project; however, when we crash the final activities, the extra investment will be tied in for a much shorter period. This study is presenting a two-objective mathematical model for balancing compressing the project time with activities delay to prepare a suitable tool for decision makers caught in available facilities and due to the time of projects. Also drawing the scheduling problem to real world conditions by considering nonlinear objective function and the time value of money are considered. The presented problem was solved using NSGA-II, and the effect of time compressing reports on the non-dominant set.
WiMax network planning and optimization
Zhang, Yan
2009-01-01
This book offers a comprehensive explanation on how to dimension, plan, and optimize WiMAX networks. The first part of the text introduces WiMAX networks architecture, physical layer, standard, protocols, security mechanisms, and highly related radio access technologies. It covers system framework, topology, capacity, mobility management, handoff management, congestion control, medium access control (MAC), scheduling, Quality of Service (QoS), and WiMAX mesh networks and security. Enabling easy understanding of key concepts and technologies, the second part presents practical examples and illu
Accelerator optimization using a network control and acquisition system
International Nuclear Information System (INIS)
Geddes, Cameron G.R.; Catravas, P.E.; Faure, Jerome; Toth, Csaba; Tilborg, J. van; Leemans, Wim P.
2002-01-01
Accelerator optimization requires detailed study of many parameters, indicating the need for remote control and automated data acquisition systems. A control and data acquisition system based on a network of commodity PCs and applications with standards based inter-application communication is being built for the l'OASIS accelerator facility. This system allows synchronous acquisition of data at high (> 1 Hz) rates and remote control of the accelerator at low cost, allowing detailed study of the acceleration process
Optimal Joint Expected Delay Forwarding in Delay Tolerant Networks
Jia Xu; Xin Feng; Wen Jun Yang; Ru Chuan Wang; Bing Qing Han
2013-01-01
Multicopy forwarding schemes have been employed in delay tolerant network (DTN) to improve the delivery delay and delivery rate. Much effort has been focused on reducing the routing cost while retaining high performance. This paper aims to provide an optimal joint expected delay forwarding (OJEDF) protocol which minimizes the expected delay while satisfying a certain constant on the number of forwardings per message. We propose a comprehensive forwarding metric called joint expected delay (JE...
Optimal transport on supply-demand networks.
Chen, Yu-Han; Wang, Bing-Hong; Zhao, Li-Chao; Zhou, Changsong; Zhou, Tao
2010-06-01
In the literature, transport networks are usually treated as homogeneous networks, that is, every node has the same function, simultaneously providing and requiring resources. However, some real networks, such as power grids and supply chain networks, show a far different scenario in which nodes are classified into two categories: supply nodes provide some kinds of services, while demand nodes require them. In this paper, we propose a general transport model for these supply-demand networks, associated with a criterion to quantify their transport capacities. In a supply-demand network with heterogeneous degree distribution, its transport capacity strongly depends on the locations of supply nodes. We therefore design a simulated annealing algorithm to find the near optimal configuration of supply nodes, which remarkably enhances the transport capacity compared with a random configuration and outperforms the degree target algorithm, the betweenness target algorithm, and the greedy method. This work provides a start point for systematically analyzing and optimizing transport dynamics on supply-demand networks.
A method of network topology optimization design considering application process characteristic
Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo
2018-03-01
Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.
Method of optimization onboard communication network
Platoshin, G. A.; Selvesuk, N. I.; Semenov, M. E.; Novikov, V. M.
2018-02-01
In this article the optimization levels of onboard communication network (OCN) are proposed. We defined the basic parameters, which are necessary for the evaluation and comparison of modern OCN, we identified also a set of initial data for possible modeling of the OCN. We also proposed a mathematical technique for implementing the OCN optimization procedure. This technique is based on the principles and ideas of binary programming. It is shown that the binary programming technique allows to obtain an inherently optimal solution for the avionics tasks. An example of the proposed approach implementation to the problem of devices assignment in OCN is considered.
On Optimal Policies for Network-Coded Cooperation
DEFF Research Database (Denmark)
Khamfroush, Hana; Roetter, Daniel Enrique Lucani; Pahlevani, Peyman
2015-01-01
Network-coded cooperative communication (NC-CC) has been proposed and evaluated as a powerful technology that can provide a better quality of service in the next-generation wireless systems, e.g., D2D communications. Previous contributions have focused on performance evaluation of NC-CC scenarios...... rather than searching for optimal policies that can minimize the total cost of reliable packet transmission. We break from this trend by initially analyzing the optimal design of NC-CC for a wireless network with one source, two receivers, and half-duplex erasure channels. The problem is modeled...... as a special case of Markov decision process (MDP), which is called stochastic shortest path (SSP), and is solved for any field size, arbitrary number of packets, and arbitrary erasure probabilities of the channels. The proposed MDP solution results in an optimal transmission policy per time slot, and we use...
Network inference via adaptive optimal design
Directory of Open Access Journals (Sweden)
Stigter Johannes D
2012-09-01
Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.
International Nuclear Information System (INIS)
Ramirez-Marquez, Jose Emmanuel; Rocco S, Claudio M.
2009-01-01
This paper introduces an evolutionary optimization approach that can be readily applied to solve stochastic network interdiction problems (SNIP). The network interdiction problem solved considers the minimization of the cost associated with an interdiction strategy such that the maximum flow that can be transmitted between a source node and a sink node for a fixed network design is greater than or equal to a given reliability requirement. Furthermore, the model assumes that the nominal capacity of each network link and the cost associated with their interdiction can change from link to link and that such interdiction has a probability of being successful. This version of the SNIP is for the first time modeled as a capacitated network reliability problem allowing for the implementation of computation and solution techniques previously unavailable. The solution process is based on an evolutionary algorithm that implements: (1) Monte-Carlo simulation, to generate potential network interdiction strategies, (2) capacitated network reliability techniques to analyze strategies' source-sink flow reliability and, (3) an evolutionary optimization technique to define, in probabilistic terms, how likely a link is to appear in the final interdiction strategy. Examples for different sizes of networks are used throughout the paper to illustrate the approach
A multiobjective optimization framework for multicontaminant industrial water network design.
Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge
2011-07-01
The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view. Copyright © 2011 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Krishan Kumar
2017-01-01
Full Text Available When a mobile network changes its point of attachments in Cognitive Radio (CR vehicular networks, the Mobile Router (MR requires spectrum handoff. Network Mobility (NEMO in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.
Implementing the cost-optimal methodology in EU countries
DEFF Research Database (Denmark)
Atanasiu, Bogdan; Kouloumpi, Ilektra; Thomsen, Kirsten Engelund
This study presents three cost-optimal calculations. The overall aim is to provide a deeper analysis and to provide additional guidance on how to properly implement the cost-optimality methodology in Member States. Without proper guidance and lessons from exemplary case studies using realistic...... input data (reflecting the likely future development), there is a risk that the cost-optimal methodology may be implemented at sub-optimal levels. This could lead to a misalignment between the defined cost-optimal levels and the long-term goals, leaving a significant energy saving potential unexploited....... Therefore, this study provides more evidence on the implementation of the cost-optimal methodology and highlights the implications of choosing different values for key factors (e.g. discount rates, simulation variants/packages, costs, energy prices) at national levels. The study demonstrates how existing...
Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.
Sohn, Insoo; Liu, Huaping; Ansari, Nirwan
2015-01-01
An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.
Optimization of power distribution networks; Optimierung von Energieverteilungsnetzen
Energy Technology Data Exchange (ETDEWEB)
Casteren, J. van [Digsilent GmbH (Netherlands); Chalmers Univ. of Technology, Goeteborg (Sweden)
2000-03-01
In a competition-oriented power market the optimization of distribution networks is more and more becoming a search for minimum investment and operating cost where all relevant cost factors should be taken into account. A so far neglected factor is the expectation of reliability-related cost. A new analytical calculation method permits flexible, realistic estimation of interruption costs to be expected. (orig.) [German] In einem wettbewerbsorientierten Strom-Markt wird die Optimierung der Verteilungsnetze mehr und mehr zu einer Suche nach den minimalen Investitions- und Betriebskosten, wobei moeglichst alle relevanten Kostenfaktoren beruecksichtigt werden muessen. Ein bisher vernachlaessigter Faktor ist dabei die Erwartung der zuverlaessigkeitsbedingten Kosten. Ein neues analytisches Berechnungsverfahren erlaubt nun flexibel eine realistische Abschaetzung der zu erwarteten Unterbrechungskosten. (orig.)
METHODS FOR DETERMINATION AND OPTIMIZATION OF LOGISTICS COSTS
Directory of Open Access Journals (Sweden)
Mihaela STET
2016-12-01
Full Text Available The paper is dealing with the problems of logistics costs, highlighting some methods for estimation and determination of specific costs for different transport modes in freight distribution. There are highlighted, besides costs of transports, the other costs in supply chain, as well as costing methods used in logistics activities. In this context, there are also revealed some optimization means of transport costs in logistics chain.
METHODS FOR DETERMINATION AND OPTIMIZATION OF LOGISTICS COSTS
Mihaela STET
2016-01-01
The paper is dealing with the problems of logistics costs, highlighting some methods for estimation and determination of specific costs for different transport modes in freight distribution. There are highlighted, besides costs of transports, the other costs in supply chain, as well as costing methods used in logistics activities. In this context, there are also revealed some optimization means of transport costs in logistics chain.
Algorithms for optimization of branching gravity-driven water networks
Dardani, Ian; Jones, Gerard F.
2018-05-01
The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.
Algorithms for optimization of branching gravity-driven water networks
Directory of Open Access Journals (Sweden)
I. Dardani
2018-05-01
Full Text Available The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs, this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011 to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel
System Approach of Logistic Costs Optimization Solution in Supply Chain
Majerčák, Peter; Masárová, Gabriela; Buc, Daniel; Majerčáková, Eva
2013-01-01
This paper is focused on the possibility of using the costs simulation in supply chain, which are on relative high level. Our goal is to determine the costs using logistic costs optimization which must necessarily be used in business activities in the supply chain management. The paper emphasizes the need to perform not isolated optimization in the whole supply chain. Our goal is to compare classic approach, when every part tracks its costs isolated, a try to minimize them, with the system (l...
Optimal skill distribution under convex skill costs
Directory of Open Access Journals (Sweden)
Tin Cheuk Leung
2018-03-01
Full Text Available This paper studies optimal distribution of skills in an optimal income tax framework with convex skill constraints. The problem is cast as a social planning problem where a redistributive planner chooses how to distribute a given amount of aggregate skills across people. We find that optimal skill distribution is either perfectly equal or perfectly unequal, but an interior level of skill inequality is never optimal.
Optimal Operations and Resilient Investments in Steam Networks
Energy Technology Data Exchange (ETDEWEB)
Bungener, Stéphane L., E-mail: stephane.bungener@a3.epfl.ch [Industrial Process and Energy Systems Engineering, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland); Van Eetvelde, Greet [Environmental and Spatial Management, Faculty of Engineering and Architecture, Ghent University, Ghent (Belgium); Maréchal, François [Industrial Process and Energy Systems Engineering, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland)
2016-01-20
Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network’s resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.
Optimal Operations and Resilient Investments in Steam Networks
International Nuclear Information System (INIS)
Bungener, Stéphane L.; Van Eetvelde, Greet; Maréchal, François
2016-01-01
Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network’s resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.
Cost optimization on example of hotel-restaurant complex enterprises
Directory of Open Access Journals (Sweden)
Volkovska I.V.
2017-08-01
Full Text Available Optimization of costs is important for increasing competitiveness and profitability of the enterprise, therefore, the purpose of the study is to establish and visualize the basis of cost optimization on the example of hotel-restaurant complex enterprises. The essence of cost optimization is investigated through the analysis of the views of various scholars for this purpose. It is established that cost optimization is the process of planning, accounting, analysis, cost control for searching and selecting of the most effective methods of managing of the conditions of limited resources. The author has developed the sequence of cost optimization on the example of enterprises of the hotel-restaurant complex, which helps to structure the process of cost management. In this sequence, there are areas where costs can be reduced, and the technical and economic conditions under which they can be changed. In addition, it is noted that such implementation is important in the cost management at the enterprise. It is also proposed to optimize costs using the simplex method to carry out a quantitative assessment of the quality of services by the qualimetric method. It is noted that it is necessary to form alternative ways of using resources for rational use of scarce resources. The article proposes cost grouping by the XYZ-analysis with individual approaches to cost management, namely, target costing, the theory of constrains, lean manufacturing. For this purpose, the author develops the table that should be filled in to compare which costs and ways can be reduced or replaced. Besides, the author has added recommendations for filling in the table and commented that with this analysis a transaction and unreasonable costs can be controlled. Thus, with such a sequence of actions, redistribution of funds is possible to optimize costs and save money, which can be directed to enterprise development. The conclusion is made of the need of system analysis to use
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Network optimization including gas lift and network parameters under subsurface uncertainty
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)
2013-08-01
Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182
Modeling and optimization of an electric power distribution network ...
African Journals Online (AJOL)
Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...
Optimization of neural network algorithm of the land market description
Directory of Open Access Journals (Sweden)
M. A. Karpovich
2016-01-01
Full Text Available The advantages of neural network technology is shown in comparison of traditional descriptions of dynamically changing systems, which include a modern land market. The basic difficulty arising in the practical implementation of neural network models of the land market and construction products is revealed It is the formation of a representative set of training and test examples. The requirements which are necessary for the correct description of the current economic situation has been determined, it consists in the fact that Train-paid-set in the feature space should not has the ranges with a low density of observations. The methods of optimization of empirical array, which allow to avoid the long-range extrapolation of data from range of concentration of the set of examples are formulated. It is shown that a radical method of optimization a set of training and test examples enclosing to collect supplemantary information, is associated with significant costs time and resources for the economic problems and the ratio of cost / efficiency is less efficient than an algorithm optimization neural network models the earth market fixed set of empirical data. Algorithm of optimization based on the transformation of arrays of information which represents the expansion of the ranges of concentration of the set of examples and compression the ranges of low density of observations is analyzed in details. The significant reduction in the relative error of land price description is demonstrated on the specific example of Voronezh region market of lands which intend for road construction, it makes the using of radical method of empirical optimization of the array costeffective with accounting the significant absolute value of the land. The high economic efficiency of the proposed algorithms is demonstrated.
Towards Optimal Event Detection and Localization in Acyclic Flow Networks
Agumbe Suresh, Mahima
2012-01-03
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.
Optimization of municipal pressure pumping station layout and sewage pipe network design
Tian, Jiandong; Cheng, Jilin; Gong, Yi
2018-03-01
Accelerated urbanization places extraordinary demands on sewer networks; thus optimization research to improve the design of these systems has practical significance. In this article, a subsystem nonlinear programming model is developed to optimize pumping station layout and sewage pipe network design. The subsystem model is expanded into a large-scale complex nonlinear programming system model to find the minimum total annual cost of the pumping station and network of all pipe segments. A comparative analysis is conducted using the sewage network in Taizhou City, China, as an example. The proposed method demonstrated that significant cost savings could have been realized if the studied system had been optimized using the techniques described in this article. Therefore, the method has practical value for optimizing urban sewage projects and provides a reference for theoretical research on optimization of urban drainage pumping station layouts.
Cost optimization for buildings with hybrid ventilation systems
Ji, Kun; Lu, Yan
2018-02-13
A method including: computing a total cost for a first zone in a building, wherein the total cost is equal to an actual energy cost of the first zone plus a thermal discomfort cost of the first zone; and heuristically optimizing the total cost to identify temperature setpoints for a mechanical heating/cooling system and a start time and an end time of the mechanical heating/cooling system, based on external weather data and occupancy data of the first zone.
Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access
Zafar, Ammar; Alouini, Mohamed-Slim; Chen, Yunfei; Radaydeh, Redha M.
2012-01-01
Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources. © 2012 Ammar Zafar et al.
Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access
Zafar, Ammar
2012-09-16
Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources. © 2012 Ammar Zafar et al.
Gasification Plant Cost and Performance Optimization
Energy Technology Data Exchange (ETDEWEB)
Samuel Tam; Alan Nizamoff; Sheldon Kramer; Scott Olson; Francis Lau; Mike Roberts; David Stopek; Robert Zabransky; Jeffrey Hoffmann; Erik Shuster; Nelson Zhan
2005-05-01
As part of an ongoing effort of the U.S. Department of Energy (DOE) to investigate the feasibility of gasification on a broader level, Nexant, Inc. was contracted to perform a comprehensive study to provide a set of gasification alternatives for consideration by the DOE. Nexant completed the first two tasks (Tasks 1 and 2) of the ''Gasification Plant Cost and Performance Optimization Study'' for the DOE's National Energy Technology Laboratory (NETL) in 2003. These tasks evaluated the use of the E-GAS{trademark} gasification technology (now owned by ConocoPhillips) for the production of power either alone or with polygeneration of industrial grade steam, fuel gas, hydrocarbon liquids, or hydrogen. NETL expanded this effort in Task 3 to evaluate Gas Technology Institute's (GTI) fluidized bed U-GAS{reg_sign} gasifier. The Task 3 study had three main objectives. The first was to examine the application of the gasifier at an industrial application in upstate New York using a Southeastern Ohio coal. The second was to investigate the GTI gasifier in a stand-alone lignite-fueled IGCC power plant application, sited in North Dakota. The final goal was to train NETL personnel in the methods of process design and systems analysis. These objectives were divided into five subtasks. Subtasks 3.2 through 3.4 covered the technical analyses for the different design cases. Subtask 3.1 covered management activities, and Subtask 3.5 covered reporting. Conceptual designs were developed for several coal gasification facilities based on the fluidized bed U-GAS{reg_sign} gasifier. Subtask 3.2 developed two base case designs for industrial combined heat and power facilities using Southeastern Ohio coal that will be located at an upstate New York location. One base case design used an air-blown gasifier, and the other used an oxygen-blown gasifier in order to evaluate their relative economics. Subtask 3.3 developed an advanced design for an air
Cost benefit analysis for optimization of radiation protection
International Nuclear Information System (INIS)
Lindell, B.
1984-01-01
ICRP recommends three basic principles for radiation protection. One is the justification of the source. Any use of radiation should be justified with regard to its benefit. The second is the optimization of radiation protection, i.e. all radiation exposure should be kept as low as resonably achievable. And the third principle is that there should be a limit for the radiation dose that any individual receives. Cost benefit assessment or cost benefit analysis is one tool to achieve the optimization, but the optimization is not identical with cost benefit analysis. Basically, in principle, the cost benefit analysis for the optimization of radiation protection is to find the minimum sum of the cost of protection and some cost of detriment. (Mori, K.)
Effects of cost metric on cost-effectiveness of protected-area network design in urban landscapes.
Burkhalter, J C; Lockwood, J L; Maslo, B; Fenn, K H; Leu, K
2016-04-01
A common goal in conservation planning is to acquire areas that are critical to realizing biodiversity goals in the most cost-effective manner. The way monetary acquisition costs are represented in such planning is an understudied but vital component to realizing cost efficiencies. We sought to design a protected-area network within a forested urban region that would protect 17 birds of conservation concern. We compared the total costs and spatial structure of the optimal protected-area networks produced using three acquisition-cost surrogates (area, agricultural land value, and tax-assessed land value). Using the tax-assessed land values there was a 73% and 78% cost savings relative to networks derived using area or agricultural land value, respectively. This cost reduction was due to the considerable heterogeneity in acquisition costs revealed in tax-assessed land values, especially for small land parcels, and the corresponding ability of the optimization algorithm to identify lower-cost parcels for inclusion that had equal value to our target species. Tax-assessed land values also reflected the strong spatial differences in acquisition costs (US$0.33/m(2)-$55/m(2)) and thus allowed the algorithm to avoid inclusion of high-cost parcels when possible. Our results add to a nascent but growing literature that suggests conservation planners must consider the cost surrogate they use when designing protected-area networks. We suggest that choosing cost surrogates that capture spatial- and size-dependent heterogeneity in acquisition costs may be relevant to establishing protected areas in urbanizing ecosystems. © 2015 Society for Conservation Biology.
Optimal Joint Liability Lending and with Costly Peer Monitoring
Carli, Francesco; Uras, R.B.
2014-01-01
This paper characterizes an optimal group loan contract with costly peer monitoring. Using a fairly standard moral hazard framework, we show that the optimal group lending contract could exhibit a joint-liability scheme. However, optimality of joint-liability requires the involvement of a group
Design and Optimization of Capacitated Supply Chain Networks Including Quality Measures
Directory of Open Access Journals (Sweden)
Krystel K. Castillo-Villar
2014-01-01
Full Text Available This paper presents (1 a novel capacitated model for supply chain network design which considers manufacturing, distribution, and quality costs (named SCND-COQ model and (2 five combinatorial optimization methods, based on nonlinear optimization, heuristic, and metaheuristic approaches, which are used to solve realistic instances of practical size. The SCND-COQ model is a mixed-integer nonlinear problem which can be used at a strategic planning level to design a supply chain network that maximizes the total profit subject to meeting an overall quality level of the final product at minimum costs. The SCND-COQ model computes the quality-related costs for the whole supply chain network considering the interdependencies among business entities. The effectiveness of the proposed solution approaches is shown using numerical experiments. These methods allow solving more realistic (capacitated supply chain network design problems including quality-related costs (inspections, rework, opportunity costs, and others within a reasonable computational time.
Directory of Open Access Journals (Sweden)
Haichao Wang
2017-07-01
Full Text Available A district heating (DH system is one of the most important components of infrastructures in cold areas. Proper DH network design should balance the initial investment and the heat distribution cost of the DH network. Currently, this design is often based on a recommended value for specific pressure loss (R = ∆P/L in the main lines. This will result in a feasible network design, but probably not be optimal in most cases. The paper develops a novel optimization model to facilitate the design by considering the initial investment in the pipes and the heat distribution costs. The model will generate all possible network scenarios consisting of different series of diameters for each pipe in the flow direction of the network. Then, the annuity on the initial investment, the heat distribution cost, and the total annual cost will be calculated for each network scenario, taking into account the uncertainties of the material prices and the yearly operating time levels. The model is applied to a sample DH network and the results indicate that the model works quite well, clearly identifying the optimal network design and demonstrating that the heat distribution cost is more important than the initial investment in DH network design.
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Directory of Open Access Journals (Sweden)
Koichi Kobayashi
2017-02-01
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Loop optimization for tensor network renormalization
Yang, Shuo; Gu, Zheng-Cheng; Wen, Xiao-Gang
We introduce a tensor renormalization group scheme for coarse-graining a two-dimensional tensor network, which can be successfully applied to both classical and quantum systems on and off criticality. The key idea of our scheme is to deform a 2D tensor network into small loops and then optimize tensors on each loop. In this way we remove short-range entanglement at each iteration step, and significantly improve the accuracy and stability of the renormalization flow. We demonstrate our algorithm in the classical Ising model and a frustrated 2D quantum model. NSF Grant No. DMR-1005541 and NSFC 11274192, BMO Financial Group, John Templeton Foundation, Government of Canada through Industry Canada, Province of Ontario through the Ministry of Economic Development & Innovation.
Optimization of life cycle management costs
International Nuclear Information System (INIS)
Banerjee, A.K.
1994-01-01
As can be seen from the case studies, a LCM program needs to address and integrate, in the decision process, technical, political, licensing, remaining plant life, component replacement cycles, and financial issues. As part of the LCM evaluations, existing plant programs, ongoing replacement projects, short and long-term operation and maintenance issues, and life extension strategies must be considered. The development of the LCM evaluations and the cost benefit analysis identifies critical technical and life cycle cost parameters. These open-quotes discoveriesclose quotes result from the detailed and effective use of a consistent, quantifiable, and well documented methodology. The systematic development and implementation of a plant-wide LCM program provides for an integrated and structured process that leads to the most practical and effective recommendations. Through the implementation of these recommendations and cost effective decisions, the overall power production costs can be controlled and ultimately lowered
Distributed Robust Optimization in Networked System.
Wang, Shengnan; Li, Chunguang
2016-10-11
In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem. We prove that the primal and dual optimal solutions of the problem are restricted in some specific sets, and we give a method to construct these sets. Then, we propose a DRO algorithm to find the primal-dual optimal solutions of the Lagrangian function, which consists of a subgradient step, a projection step, and a diffusion step, and in the projection step of the algorithm, the optimized variables are projected onto the specific sets to guarantee the boundedness of the subgradients. Convergence analysis and numerical simulations verifying the performance of the proposed algorithm are then provided. Further, for nonconvex DRO problem, the corresponding approach and algorithm framework are also provided.
Directory of Open Access Journals (Sweden)
محسن نفیسی
2014-10-01
Full Text Available Lack of an efficient sewer network in urban areas threatens public health and may give rise to contagious diseases. Various optimization methods have been developed for use in designing sewers networks in response to a number of requirements such as the high costs of constructing sewer networks, financial limitations, the presence of both discrete and continuous decision variables, and the nonlinear time complexity of such design problems. In this study, the particle swarm optimization algorithm (PSO with the capability of “fly-back” mechanism equipped with the harmony search (HPSO is used for the optimization of sewers network designs. The objective function consists of minimizing the excavation and embedding costs of commercial pipes. The fly-back mechanism and the harmony memory method are used to prevent leaving out variables from the feasible space of the problem in an attempt to enhance model efficiency. Model constraints are satisfied at two levels, which leads to the desirable convergence of the PSO algorithm as compared to the conventional penalty methods in alternative evolutionary algorithms. In order to determine the admissible decision variables, the Manning equation is used as a hydraulic model. The performance of the proposed algorithm is shown by presenting two examples of sewer networks. Compared to the PSO algorithm used in sewer network optimization models, the proposed model exhibits a tangible improvement in cost reduction and a higher computational stability.
Network ownership and optimal tariffs for natural gas transport
International Nuclear Information System (INIS)
Hagen, Kaare P.; Kind, Hans Jarle; Sannarnes, Jan Gaute
2004-11-01
This paper addresses the issue of national optimal tariffs for transportation of natural gas in a setting where national gas production in its entirety is exported to end-user markets abroad. In a situation where the transportation network is owned altogether by a vertically integrated national gas producer, it is shown that the optimal tariff depends on the ownership structure in the integrated transportation company as well as in the non-facility based gas company. There are two reasons why it is possibly optimal with a mark-up on marginal transportation costs. First, there is a premium on public revenue if domestic taxation is distorting. Second, with incomplete national taxation of rents from the gas sector, the transportation tariffs can serve as a second best way of appropriating rents accruing to foreigners. In a situation where the network is run as a separate entity subject to a rate of return regulation, it will be optimal to discriminate the tariffs between shippers for the usual Ramseyean reasons. (Author)
Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery
International Nuclear Information System (INIS)
Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel
2009-01-01
The reliability of ad-hoc networks is gaining popularity in two areas: as a topic of academic interest and as a key performance parameter for defense systems employing this type of network. The ad-hoc network is dynamic and scalable and these descriptions are what attract its users. However, these descriptions are also synonymous for undefined and unpredictable when considering the impacts to the reliability of the system. The configuration of an ad-hoc network changes continuously and this fact implies that no single mathematical expression or graphical depiction can describe the system reliability-wise. Previous research has used mobility and stochastic models to address this challenge successfully. In this paper, the authors leverage the stochastic approach and build upon it a probabilistic solution discovery (PSD) algorithm to optimize the topology for a cluster-based mobile ad-hoc wireless network (MAWN). Specifically, the membership of nodes within the back-bone network or networks will be assigned in such as way as to maximize reliability subject to a constraint on cost. The constraint may also be considered as a non-monetary cost, such as weight, volume, power, or the like. When a cost is assigned to each component, a maximum cost threshold is assigned to the network, and the method is run; the result is an optimized allocation of the radios enabling back-bone network(s) to provide the most reliable network possible without exceeding the allowable cost. The method is intended for use directly as part of the architectural design process of a cluster-based MAWN to efficiently determine an optimal or near-optimal design solution. It is capable of optimizing the topology based upon all-terminal reliability (ATR), all-operating terminal reliability (AoTR), or two-terminal reliability (2TR)
An optimization planning technique for Suez Canal Network in Egypt
Energy Technology Data Exchange (ETDEWEB)
Abou El-Ela, A.A.; El-Zeftawy, A.A.; Allam, S.M.; Atta, Gasir M. [Electrical Engineering Dept., Faculty of Eng., Shebin El-Kom (Egypt)
2010-02-15
This paper introduces a proposed optimization technique POT for predicting the peak load demand and planning of transmission line systems. Many of traditional methods have been presented for long-term load forecasting of electrical power systems. But, the results of these methods are approximated. Therefore, the artificial neural network (ANN) technique for long-term peak load forecasting is modified and discussed as a modern technique in long-term load forecasting. The modified technique is applied on the Egyptian electrical network dependent on its historical data to predict the electrical peak load demand forecasting up to year 2017. This technique is compared with extrapolation of trend curves as a traditional method. The POT is applied also to obtain the optimal planning of transmission lines for the 220 kV of Suez Canal Network (SCN) using the ANN technique. The minimization of the transmission network costs are considered as an objective function, while the transmission lines (TL) planning constraints are satisfied. Zafarana site on the Red Sea coast is considered as an optimal site for installing big wind farm (WF) units in Egypt. So, the POT is applied to plan both the peak load and the electrical transmission of SCN with and without considering WF to develop the impact of WF units on the electrical transmission system of Egypt, considering the reliability constraints which were taken as a separate model in the previous techniques. The application on SCN shows the capability and the efficiently of the proposed techniques to obtain the predicting peak load demand and the optimal planning of transmission lines of SCN up to year 2017. (author)
NLP model and stochastic multi-start optimization approach for heat exchanger networks
International Nuclear Information System (INIS)
Núñez-Serna, Rosa I.; Zamora, Juan M.
2016-01-01
Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.
Optimizing Data Centre Energy and Environmental Costs
Aikema, David Hendrik
Data centres use an estimated 2% of US electrical power which accounts for much of their total cost of ownership. This consumption continues to grow, further straining power grids attempting to integrate more renewable energy. This dissertation focuses on assessing and reducing data centre environmental and financial costs. Emissions of projects undertaken to lower the data centre environmental footprints can be assessed and the emission reduction projects compared using an ISO-14064-2-compliant greenhouse gas reduction protocol outlined herein. I was closely involved with the development of the protocol. Full lifecycle analysis and verifying that projects exceed business-as-usual expectations are addressed, and a test project is described. Consuming power when it is low cost or when renewable energy is available can be used to reduce the financial and environmental costs of computing. Adaptation based on the power price showed 10--50% potential savings in typical cases, and local renewable energy use could be increased by 10--80%. Allowing a fraction of high-priority tasks to proceed unimpeded still allows significant savings. Power grid operators use mechanisms called ancillary services to address variation and system failures, paying organizations to alter power consumption on request. By bidding to offer these services, data centres may be able to lower their energy costs while reducing their environmental impact. If providing contingency reserves which require only infrequent action, savings of up to 12% were seen in simulations. Greater power cost savings are possible for those ceding more control to the power grid operator. Coordinating multiple data centres adds overhead, and altering at which data centre requests are processed based on changes in the financial or environmental costs of power is likely to increase this overhead. Tests of virtual machine migrations showed that in some cases there was no visible increase in power use while in others power use
Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm
Directory of Open Access Journals (Sweden)
D.B. Prakash
2017-12-01
Full Text Available In present days, continuous effort is being made in bringing down the line losses of the electrical distribution networks. Therefore proper allocation of capacitors is of utmost importance because, it will help in reducing the line losses and maintaining the bus voltage. This in turn results in improving the stability and reliability of the system. In this paper Whale Optimization Algorithm (WOA is used to find optimal sizing and placement of capacitors for a typical radial distribution system. Multi objectives such as operating cost reduction and power loss minimization with inequality constraints on voltage limits are considered and the proposed algorithm is validated by applying it on standard radial systems: IEEE-34 bus and IEEE-85 bus radial distribution test systems. The results obtained are compared with those of existing algorithms. The results show that the proposed algorithm is more effective in bringing down the operating costs and in maintaining better voltage profile. Keywords: Whale Optimization Algorithm (WOA, Optimal allocation and sizing of capacitors, Power loss reduction and voltage stability improvement, Radial distribution system, Operating cost minimization
International Nuclear Information System (INIS)
Huang, Y.-J.; Chen, K.-H.; Yang, C.-H.
2010-01-01
This paper analyzes the cost efficiency and optimal scale of Taiwan's electricity distribution industry. Due to the substantial difference in network density, firms may differ widely in production technology. We employ the stochastic metafrontier approach to estimate the cost efficiency of 24 distribution units during the period 1997-2002. Empirical results find that the average cost efficiency is overestimated using the traditional stochastic frontier model, especially for low density regions. The average cost efficiency of the high density group is significantly higher than that of the low density group as it benefits from network economies. This study also calculates both short-term and long-term optimal scales of electricity distribution firms, lending policy implications for the deregulation of the electricity distribution industry.
Estimation of optimal educational cost per medical student.
Yang, Eunbae B; Lee, Seunghee
2009-09-01
This study aims to estimate the optimal educational cost per medical student. A private medical college in Seoul was targeted by the study, and its 2006 learning environment and data from the 2003~2006 budget and settlement were carefully analyzed. Through interviews with 3 medical professors and 2 experts in the economics of education, the study attempted to establish the educational cost estimation model, which yields an empirically computed estimate of the optimal cost per student in medical college. The estimation model was based primarily upon the educational cost which consisted of direct educational costs (47.25%), support costs (36.44%), fixed asset purchases (11.18%) and costs for student affairs (5.14%). These results indicate that the optimal cost per student is approximately 20,367,000 won each semester; thus, training a doctor costs 162,936,000 won over 4 years. Consequently, we inferred that the tuition levels of a local medical college or professional medical graduate school cover one quarter or one-half of the per- student cost. The findings of this study do not necessarily imply an increase in medical college tuition; the estimation of the per-student cost for training to be a doctor is one matter, and the issue of who should bear this burden is another. For further study, we should consider the college type and its location for general application of the estimation method, in addition to living expenses and opportunity costs.
Site specific optimization of wind turbines energy cost: Iterative approach
International Nuclear Information System (INIS)
Rezaei Mirghaed, Mohammad; Roshandel, Ramin
2013-01-01
Highlights: • Optimization model of wind turbine parameters plus rectangular farm layout is developed. • Results show that levelized cost for single turbine fluctuates between 46.6 and 54.5 $/MW h. • Modeling results for two specific farms reported optimal sizing and farm layout. • Results show that levelized cost of the wind farms fluctuates between 45.8 and 67.2 $/MW h. - Abstract: The present study was aimed at developing a model to optimize the sizing parameters and farm layout of wind turbines according to the wind resource and economic aspects. The proposed model, including aerodynamic, economic and optimization sub-models, is used to achieve minimum levelized cost of electricity. The blade element momentum theory is utilized for aerodynamic modeling of pitch-regulated horizontal axis wind turbines. Also, a comprehensive cost model including capital costs of all turbine components is considered. An iterative approach is used to develop the optimization model. The modeling results are presented for three potential regions in Iran: Khaf, Ahar and Manjil. The optimum configurations and sizing for a single turbine with minimum levelized cost of electricity are presented. The optimal cost of energy for one turbine is calculated about 46.7, 54.5 and 46.6 dollars per MW h in the studied sites, respectively. In addition, optimal size of turbines, annual electricity production, capital cost, and wind farm layout for two different rectangular and square shaped farms in the proposed areas have been recognized. According to the results, optimal system configuration corresponds to minimum levelized cost of electricity about 45.8 to 67.2 dollars per MW h in the studied wind farms
RO-75, Reverse Osmosis Plant Design Optimization and Cost Optimization
International Nuclear Information System (INIS)
Glueckstern, P.; Reed, S.A.; Wilson, J.V.
1999-01-01
1 - Description of problem or function: RO75 is a program for the optimization of the design and economics of one- or two-stage seawater reverse osmosis plants. 2 - Method of solution: RO75 evaluates the performance of the applied membrane module (productivity and salt rejection) at assumed operating conditions. These conditions include the site parameters - seawater salinity and temperature, the membrane module operating parameters - pressure and product recovery, and the membrane module predicted long-term performance parameters - lifetime and long flux decline. RO75 calculates the number of first and second stage (if applied) membrane modules needed to obtain the required product capacity and quality and evaluates the required pumping units and the power recovery turbine (if applied). 3 - Restrictions on the complexity of the problem: The program does not optimize or design the membrane properties and the internal structure and flow characteristics of the membrane modules; it assumes operating characteristics defined by the membrane manufacturers
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Huang, Can
2018-01-01
–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master...... optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods....
Capital Cost Optimization for Prefabrication: A Factor Analysis Evaluation Model
Directory of Open Access Journals (Sweden)
Hong Xue
2018-01-01
Full Text Available High capital cost is a significant hindrance to the promotion of prefabrication. In order to optimize cost management and reduce capital cost, this study aims to explore the latent factors and factor analysis evaluation model. Semi-structured interviews were conducted to explore potential variables and then questionnaire survey was employed to collect professionals’ views on their effects. After data collection, exploratory factor analysis was adopted to explore the latent factors. Seven latent factors were identified, including “Management Index”, “Construction Dissipation Index”, “Productivity Index”, “Design Efficiency Index”, “Transport Dissipation Index”, “Material increment Index” and “Depreciation amortization Index”. With these latent factors, a factor analysis evaluation model (FAEM, divided into factor analysis model (FAM and comprehensive evaluation model (CEM, was established. The FAM was used to explore the effect of observed variables on the high capital cost of prefabrication, while the CEM was used to evaluate comprehensive cost management level on prefabrication projects. Case studies were conducted to verify the models. The results revealed that collaborative management had a positive effect on capital cost of prefabrication. Material increment costs and labor costs had significant impacts on production cost. This study demonstrated the potential of on-site management and standardization design to reduce capital cost. Hence, collaborative management is necessary for cost management of prefabrication. Innovation and detailed design were needed to improve cost performance. The new form of precast component factories can be explored to reduce transportation cost. Meanwhile, targeted strategies can be adopted for different prefabrication projects. The findings optimized the capital cost and improved the cost performance through providing an evaluation and optimization model, which helps managers to
International Nuclear Information System (INIS)
Pombo, A. Vieira; Murta-Pina, João; Pires, V. Fernão
2015-01-01
A multi-objective planning approach for the reliability of electric distribution networks using a memetic optimization is presented. In this reliability optimization, the type of the equipment (switches or reclosers) and their location are optimized. The multiple objectives considered to find the optimal values for these planning variables are the minimization of the total equipment cost and at the same time the minimization of two distribution network reliability indexes. The reliability indexes are the system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI). To solve this problem a memetic evolutionary algorithm is proposed, which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with a local search algorithm. The obtained Pareto-optimal front contains solutions of different trade-offs with respect to the three objectives. A real distribution network is used to test the proposed algorithm. The obtained results show that this approach allows the utility to obtain the optimal type and location of the equipments to achieve the best reliability with the lower cost. - Highlights: • Reliability indexes SAIFI and SAIDI and Equipment Cost are optimized. • Optimization of equipment type, number and location on a MV network. • Memetic evolutionary algorithm with a local search algorithm is proposed. • Pareto optimal front solutions with respect to the three objective functions
Optimizing the spatial pattern of networks for monitoring radioactive releases
Melles, S.J.; Heuvelink, G.B.M.; Twenhofel, C.J.W.; Dijk, van A.; Hiemstra, P.H.; Baume, O.P.; Stohlker, U.
2011-01-01
This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines
Optimizing the Heat Exchanger Network of a Steam Reforming System
DEFF Research Database (Denmark)
Nielsen, Mads Pagh; Korsgaard, Anders Risum; Kær, Søren Knudsen
2004-01-01
Proton Exchange Membrane (PEM) based combined heat and power production systems are highly integrated energy systems. They may include a hydrogen production system and fuel cell stacks along with post combustion units optionally coupled with gas turbines. The considered system is based on a natural...... stationary numerical system model was used and process integration techniques for optimizing the heat exchanger network for the reforming unit are proposed. Objective is to minimize the system cost. Keywords: Fuel cells; Steam Reforming; Heat Exchanger Network (HEN) Synthesis; MINLP....... gas steam reformer along with gas purification reactors to generate clean hydrogen suited for a PEM stack. The temperatures in the various reactors in the fuel processing system vary from around 1000°C to the stack temperature at 80°C. Furthermore, external heating must be supplied to the endothermic...
Refrigerator Optimal Scheduling to Minimise the Cost of Operation
Directory of Open Access Journals (Sweden)
Bálint Roland
2016-12-01
Full Text Available The cost optimal scheduling of a household refrigerator is presented in this work. The fundamental approach is the model predictive control methodology applied to the piecewise affine model of the refrigerator.
Road networks as collections of minimum cost paths
Wegner, Jan Dirk; Montoya-Zegarra, Javier Alexander; Schindler, Konrad
2015-10-01
We present a probabilistic representation of network structures in images. Our target application is the extraction of urban roads from aerial images. Roads appear as thin, elongated, partially curved structures forming a loopy graph, and this complex layout requires a prior that goes beyond standard smoothness and co-occurrence assumptions. In the proposed model the network is represented as a union of 1D paths connecting distant (super-)pixels. A large set of putative candidate paths is constructed in such a way that they include the true network as much as possible, by searching for minimum cost paths in the foreground (road) likelihood. Selecting the optimal subset of candidate paths is posed as MAP inference in a higher-order conditional random field. Each path forms a higher-order clique with a type of clique potential, which attracts the member nodes of cliques with high cumulative road evidence to the foreground label. That formulation induces a robust PN -Potts model, for which a global MAP solution can be found efficiently with graph cuts. Experiments with two road data sets show that the proposed model significantly improves per-pixel accuracies as well as the overall topological network quality with respect to several baselines.
Solving Minimum Cost Multi-Commodity Network Flow Problem ...
African Journals Online (AJOL)
ADOWIE PERE
2018-03-23
Mar 23, 2018 ... network-based modeling framework for integrated fixed and mobile ... Minimum Cost Network Flow Problem (MCNFP) and some ..... Unmanned Aerial Vehicle Routing in Traffic. Incident ... Ph.D. Thesis, Dept. of Surveying &.
Costs and benefits of realism and optimism
Bortolotti, Lisa; Antrobus, Magdalena
2015-01-01
Purpose of review What is the relationship between rationality and mental health? By considering the psychological literature on depressive realism and unrealistic optimism, it was hypothesized that, in the context of judgments about the self, accurate cognitions are psychologically maladaptive and inaccurate cognitions are psychologically adaptive. Recent studies recommend being cautious in drawing any general conclusion about the style of thinking and mental health. Recent findings Recent i...
Cost Optimization Through Open Source Software
Directory of Open Access Journals (Sweden)
Mark VonFange
2010-12-01
Full Text Available The cost of information technology (IT as a percentage of overall operating and capital expenditures is growing as companies modernize their operations and as IT becomes an increasingly indispensable part of company resources. The price tag associated with IT infrastructure is a heavy one, and, in today's economy, companies need to look for ways to reduce overhead while maintaining quality operations and staying current with technology. With its advancements in availability, usability, functionality, choice, and power, free/libre open source software (F/LOSS provides a cost-effective means for the modern enterprise to streamline its operations. iXsystems wanted to quantify the benefits associated with the use of open source software at their company headquarters. This article is the outgrowth of our internal analysis of using open source software instead of commercial software in all aspects of company operations.
Energy Cost Minimization in Heterogeneous Cellular Networks with Hybrid Energy Supplies
Directory of Open Access Journals (Sweden)
Bang Wang
2016-01-01
Full Text Available The ever increasing data demand has led to the significant increase of energy consumption in cellular mobile networks. Recent advancements in heterogeneous cellular networks and green energy supplied base stations provide promising solutions for cellular communications industry. In this article, we first review the motivations and challenges as well as approaches to address the energy cost minimization problem for such green heterogeneous networks. Owing to the diversities of mobile traffic and renewable energy, the energy cost minimization problem involves both temporal and spatial optimization of resource allocation. We next present a new solution to illustrate how to combine the optimization of the temporal green energy allocation and spatial mobile traffic distribution. The whole optimization problem is decomposed into four subproblems, and correspondingly our proposed solution is divided into four parts: energy consumption estimation, green energy allocation, user association, and green energy reallocation. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost.
Searching for Cost-Optimized Interstellar Beacons
Benford, Gregory; Benford, James; Benford, Dominic
2010-06-01
What would SETI beacon transmitters be like if built by civilizations that had a variety of motives but cared about cost? In a companion paper, we presented how, for fixed power density in the far field, a cost-optimum interstellar beacon system could be built. Here, we consider how we should search for a beacon if it were produced by a civilization similar to ours. High-power transmitters could be built for a wide variety of motives other than the need for two-way communication; this would include beacons built to be seen over thousands of light-years. Extraterrestrial beacon builders would likely have to contend with economic pressures just as their terrestrial counterparts do. Cost, spectral lines near 1 GHz, and interstellar scintillation favor radiating frequencies substantially above the classic "water hole." Therefore, the transmission strategy for a distant, cost-conscious beacon would be a rapid scan of the galactic plane with the intent to cover the angular space. Such pulses would be infrequent events for the receiver. Such beacons built by distant, advanced, wealthy societies would have very different characteristics from what SETI researchers seek. Future searches should pay special attention to areas along the galactic disk where SETI searches have seen coherent signals that have not recurred on the limited listening time intervals we have used. We will need to wait for recurring events that may arriarrive in intermittent bursts. Several new SETI search strategies have emerged from these ideas. We propose a new test for beacons that is based on the Life Plane hypotheses.
Optimal investment with fixed refinancing costs
Jason G. Cummins; Ingmar Nyman
2001-01-01
Case studies show that corporate managers seek financial independence to avoid interference by outside financiers. We incorporate this financial xenophobia as a fixed cost in a simple dynamic model of financing and investment. To avoid refinancing in the future, the firm alters its behavior depending on the extent of its financial xenophobia and the realization of a revenue shock. With a sufficiently adverse shock, the firm holds no liquidity. Otherwise, the firm precautionarily saves and hol...
Optimal Bidding Strategy for Renewable Microgrid with Active Network Management
Directory of Open Access Journals (Sweden)
Seung Wan Kim
2016-01-01
Full Text Available Active Network Management (ANM enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG and Battery Energy Storage System (BESS units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.
Design and optimization of all-optical networks
Xiao, Gaoxi
1999-10-01
In this thesis, we present our research results on the design and optimization of all-optical networks. We divide our results into the following four parts: 1.In the first part, we consider broadcast-and-select networks. In our research, we propose an alternative and cheaper network configuration to hide the tuning time. In addition, we derive lower bounds on the optimal schedule lengths and prove that they are tighter than the best existing bounds. 2.In the second part, we consider all-optical wide area networks. We propose a set of algorithms for allocating a given number of WCs to the nodes. We adopt a simulation-based optimization approach, in which we collect utilization statistics of WCs from computer simulation and then perform optimization to allocate the WCs. Therefore, our algorithms are widely applicable and they are not restricted to any particular model and assumption. We have conducted extensive computer simulation on regular and irregular networks under both uniform and non-uniform traffic. We see that our method can get nearly the same performance as that of full wavelength conversion by using a much smaller number of WCs. Compared with the best existing method, the results show that our algorithms can significantly reduce (1)the overall blocking probability (i.e., better mean quality of service) and (2)the maximum of the blocking probabilities experienced at all the source nodes (i.e., better fairness). Equivalently, for a given performance requirement on blocking probability, our algorithms can significantly reduce the number of WCs required. 3.In the third part, we design and optimize the physical topology of all-optical wide area networks. We show that the design problem is NP-complete and we propose a heuristic algorithm called two-stage cut saturation algorithm for this problem. Simulation results show that (1)the proposed algorithm can efficiently design networks with low cost and high utilization, and (2)if wavelength converters are
Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process
Directory of Open Access Journals (Sweden)
Gelayol Golkarnarenji
2018-03-01
Full Text Available To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR and Artificial Neural Network (ANN, were studied and compared, with a limited dataset obtained to predict physical property (density of oxidative stabilized PAN fiber (OPF in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.
Global Optimization for Transport Network Expansion and Signal Setting
Liu, Haoxiang; Wang, David Z. W.; Yue, Hao
2015-01-01
This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two pr...
A Study of Joint Cost Inclusion in Linear Programming Optimization
Directory of Open Access Journals (Sweden)
P. Armaos
2013-08-01
Full Text Available The concept of Structural Optimization has been a topic or research over the past century. Linear Programming Optimization has proved being the most reliable method of structural optimization. Global advances in linear programming optimization have been recently powered by University of Sheffield researchers, to include joint cost, self-weight and buckling considerations. A joint cost inclusion scopes to reduce the number of joints existing in an optimized structural solution, transforming it to a practically viable solution. The topic of the current paper is to investigate the effects of joint cost inclusion, as this is currently implemented in the optimization code. An extended literature review on this subject was conducted prior to familiarization with small scale optimization software. Using IntelliFORM software, a structured series of problems were set and analyzed. The joint cost tests examined benchmark problems and their consequent changes in the member topology, as the design domain was expanding. The findings of the analyses were remarkable and are being commented further on. The distinct topologies of solutions created by optimization processes are also recognized. Finally an alternative strategy of penalizing joints is presented.
Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm
Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven
2010-05-01
Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.
Synchronization-optimized networks for coupled nearly identical ...
Indian Academy of Sciences (India)
From the stability criteria of the MSF, we construct optimal networks ... of intense research in physical, biological, chemical, technological and social sci- ..... In figure 3a, a sample of initial network of 32 coupled nearly identical Rössler oscilla-.
Optimal Retrofit Scheme for Highway Network under Seismic Hazards
Directory of Open Access Journals (Sweden)
Yongxi Huang
2014-06-01
Full Text Available Many older highway bridges in the United States (US are inadequate for seismic loads and could be severely damaged or collapsed in a relatively small earthquake. According to the most recent American Society of Civil Engineers’ infrastructure report card, one-third of the bridges in the US are rated as structurally deficient and many of these structurally deficient bridges are located in seismic zones. To improve this situation, at-risk bridges must be identified and evaluated and effective retrofitting programs should be in place to reduce their seismic vulnerabilities. In this study, a new retrofit strategy decision scheme for highway bridges under seismic hazards is developed and seamlessly integrate the scenario-based seismic analysis of bridges and the traffic network into the proposed optimization modeling framework. A full spectrum of bridge retrofit strategies is considered based on explicit structural assessment for each seismic damage state. As an empirical case study, the proposed retrofit strategy decision scheme is utilized to evaluate the bridge network in one of the active seismic zones in the US, Charleston, South Carolina. The developed modeling framework, on average, will help increase network throughput traffic capacity by 45% with a cost increase of only $15million for the Mw 5.5 event and increase the capacity fourfold with a cost of only $32m for the Mw 7.0 event.
Optimizing Seismic Monitoring Networks for EGS and Conventional Geothermal Projects
Kraft, Toni; Herrmann, Marcus; Bethmann, Falko; Stefan, Wiemer
2013-04-01
location problem. Optimization for additional criteria (e.g., focal mechanism determination or installation costs) can be included. We consider a 3D seismic velocity model, an European ambient seismic noise model derived from high-resolution land-use data, and existing seismic stations in the vicinity of the geotechnical site. Additionally, we account for the attenuation of the seismic signal with travel time and ambient seismic noise with depth to be able to correctly deal with borehole station networks. Using this algorithm we are able to find the optimal geometry and size of the seismic monitoring network that meets the predefined application-oriented performance criteria. This talk will focus on optimal network geometries for deep geothermal projects of the EGS and hydrothermal type, and discuss the requirements for basic seismic surveillance and high-resolution reservoir monitoring and characterization.
Parallel Evolutionary Optimization for Neuromorphic Network Training
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Disney, Adam [University of Tennessee (UT); Singh, Susheela [North Carolina State University (NCSU), Raleigh; Bruer, Grant [University of Tennessee (UT); Mitchell, John Parker [University of Tennessee (UT); Klibisz, Aleksander [University of Tennessee (UT); Plank, James [University of Tennessee (UT)
2016-01-01
One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.
Cost-optimized climate stabilisation (OPTIKS)
Energy Technology Data Exchange (ETDEWEB)
Leimbach, Marian; Bauer, Nico; Baumstark, Lavinia; Edenhofer, Ottmar [Potsdam Institut fuer Klimafolgenforschung, Potsdam (Germany)
2009-11-15
This study analyses the implications of suggestions for the design of post-2012 climate policy regimes on the basis of model simulations. The focus of the analysis, the determination of regional mitigation costs and the technological development in the energy sector, also considers the feedbacks of investment and trade decisions of the regions that are linked by different global markets for emission permits, goods and resources. The analysed policy regimes are primarily differentiated by their allocation of emission rights. Moreover, they represent alternative designs of an international cap and trade system that is geared to meet the 2 C climate target. The present study analyses ambitious climate protection scenarios that require drastic reduction policies (reductions of 60%-80% globally until 2050). Immediate and multilateral action is needed in such scenarios. Given the rather small variance of mitigation costs in major regions like UCA, Europe, MEA and China, a policy regime should be chosen that provides high incentives to join an international agreement for the remaining regions. From this perspective either the C and C scenario (incentive for Russia) is preferable or the multi-stage approach (incentive for Africa and India). (orig.)
Optimizing quality, service, and cost through innovation.
Walker, Kathleen; Allen, Jennifer; Andrews, Richard
2011-01-01
With dramatic increases in health care costs and growing concerns about the quality of health care services, nurse executives are seeking ways to transform their organizations to improve operational and financial performance while enhancing quality care and patient safety. Nurse leaders are challenged to meet new cost, quality and service imperatives, and change cannot be achieved by traditional approaches, it must occur through innovation. Imagine an organization that can mitigate a $56 million loss in revenue and claim the following successes: Increase admissions by a 8 day and a $5.5 million annualized increase by repurposing existing space. Decrease emergency department holding hours by an average of 174 hours a day, with a labor savings of $502,000 annually. Reduce overall inpatient length of stay by 0.5 day with total compensation running $4.2 million less than the budget for first quarter of 2010. Grow emergency department volume 272 visits greater than budgeted for first quarter of 2010. Complete admission assessments and diagnostics in 90 minutes. This article will address how these outcomes were achieved by transforming care delivery, creating a patient transition center, enhancing outreach referrals, and revising admission processes through collaboration and innovation.
BWR fuel cycle optimization using neural networks
International Nuclear Information System (INIS)
Ortiz-Servin, Juan Jose; Castillo, Jose Alejandro; Pelta, David Alejandro
2011-01-01
Highlights: → OCONN a new system to optimize all nuclear fuel management steps in a coupled way. → OCON is based on an artificial recurrent neural network to find the best combination of partial solutions to each fuel management step. → OCONN works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. → Results show OCONN is able to find good combinations according the global objective function. - Abstract: In nuclear fuel management activities for BWRs, four combinatorial optimization problems are solved: fuel lattice design, axial fuel bundle design, fuel reload design and control rod patterns design. Traditionally, these problems have been solved in separated ways due to their complexity and the required computational resources. In the specialized literature there are some attempts to solve fuel reloads and control rod patterns design or fuel lattice and axial fuel bundle design in a coupled way. In this paper, the system OCONN to solve all of these problems in a coupled way is shown. This system is based on an artificial recurrent neural network to find the best combination of partial solutions to each problem, in order to maximize a global objective function. The new system works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. The system was tested to design an equilibrium cycle with a cycle length of 18 months. Results show that the new system is able to find good combinations. Cycle length is reached and safety parameters are fulfilled.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
DEFF Research Database (Denmark)
Ngo, Trung Dung
2012-01-01
of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...... optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm...
Cost-Optimal Analysis for Nearly Zero Energy Buildings Design and Optimization: A Critical Review
Directory of Open Access Journals (Sweden)
Maria Ferrara
2018-06-01
Full Text Available Since the introduction of the recast of the EPBD European Directive 2010/31/EU, many studies on the cost-effective feasibility of nearly zero-energy buildings (NZEBs were carried out either by academic research bodies and by national bodies. In particular, the introduction of the cost-optimal methodology has given a strong impulse to research in this field. This paper presents a comprehensive and significant review on scientific works based on the application of cost-optimal analysis applications in Europe since the EPBD recast entered into force, pointing out the differences in the analyzed studies and comparing their outcomes before the new recast of EPBD enters into force in 2018. The analysis is conducted with special regard to the methods used for the energy performance assessment, the global cost calculation, and for the selection of the energy efficiency measures leading to design optimization. A critical discussion about the assumptions on which the studies are based and the resulting gaps between the resulting cost-optimal performance and the zero energy target is provided together with a summary of the resulting cost-optimal set of technologies to be used for cost-optimal NZEB design in different contexts. It is shown that the cost-optimal approach results as an effective method for delineating the future of NZEB design throughout Europe while emerging criticalities and open research issues are presented.
Discounted cost model for condition-based maintenance optimization
International Nuclear Information System (INIS)
Weide, J.A.M. van der; Pandey, M.D.; Noortwijk, J.M. van
2010-01-01
This paper presents methods to evaluate the reliability and optimize the maintenance of engineering systems that are damaged by shocks or transients arriving randomly in time and overall degradation is modeled as a cumulative stochastic point process. The paper presents a conceptually clear and comprehensive derivation of formulas for computing the discounted cost associated with a maintenance policy combining both condition-based and age-based criteria for preventive maintenance. The proposed discounted cost model provides a more realistic basis for optimizing the maintenance policies than those based on the asymptotic, non-discounted cost rate criterion.
Value management: optimizing quality, service, and cost.
Makadon, Harvey J; Bharucha, Farzan; Gavin, Michael; Oliveira, Jason; Wietecha, Mark
2010-01-01
Hospitals have wrestled with balancing quality, service, and cost for years--and the visibility and urgency around measuring and communicating real metrics has grown exponentially in the last decade. However, even today, most hospital leaders cannot articulate or demonstrate the "value" they provide to patients and payers. Instead of developing a strategic direction that is based around a core value proposition, they focus their strategic efforts on tactical decisions like physician recruitment, facility expansion, and physician alignment. In the healthcare paradigm of the next decade, alignment of various tactical initiatives will require a more coherent understanding of the hospital's core value positioning. The authors draw on their experience in a variety of healthcare settings to suggest that for most hospitals, quality (i.e., clinical outcomes and patient safety) will become the most visible indicator of value, and introduce a framework to help healthcare providers influence their value positioning based on this variable.
Gunes, Ersin Fatih
Turkey is located between Europe, which has increasing demand for natural gas and the geographies of Middle East, Asia and Russia, which have rich and strong natural gas supply. Because of the geographical location, Turkey has strategic importance according to energy sources. To supply this demand, a pipeline network configuration with the optimal and efficient lengths, pressures, diameters and number of compressor stations is extremely needed. Because, Turkey has a currently working and constructed network topology, obtaining an optimal configuration of the pipelines, including an optimal number of compressor stations with optimal locations, is the focus of this study. Identifying a network design with lowest costs is important because of the high maintenance and set-up costs. The quantity of compressor stations, the pipeline segments' lengths, the diameter sizes and pressures at compressor stations, are considered to be decision variables in this study. Two existing optimization models were selected and applied to the case study of Turkey. Because of the fixed cost of investment, both models are formulated as mixed integer nonlinear programs, which require branch and bound combined with the nonlinear programming solution methods. The differences between these two models are related to some factors that can affect the network system of natural gas such as wall thickness, material balance compressor isentropic head and amount of gas to be delivered. The results obtained by these two techniques are compared with each other and with the current system. Major differences between results are costs, pressures and flow rates. These solution techniques are able to find a solution with minimum cost for each model both of which are less than the current cost of the system while satisfying all the constraints on diameter, length, flow rate and pressure. These results give the big picture of an ideal configuration for the future state network for the country of Turkey.
Practical synchronization on complex dynamical networks via optimal pinning control
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.
Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions
Mahayni, Malek A.
2011-07-01
Finding optimal paths in directed graphs is a wide area of research that has received much of attention in theoretical computer science due to its importance in many applications (e.g., computer networks and road maps). Many algorithms have been developed to solve the optimal paths problem with different kinds of graphs. An algorithm that solves the problem of paths’ optimization in directed graphs relative to different cost functions is described in [1]. It follows an approach extended from the dynamic programming approach as it solves the problem sequentially and works on directed graphs with positive weights and no loop edges. The aim of this thesis is to implement and evaluate that algorithm to find the optimal paths in directed graphs relative to two different cost functions ( , ). A possible interpretation of a directed graph is a network of roads so the weights for the function represent the length of roads, whereas the weights for the function represent a constraint of the width or weight of a vehicle. The optimization aim for those two functions is to minimize the cost relative to the function and maximize the constraint value associated with the function. This thesis also includes finding and proving the relation between the two different cost functions ( , ). When given a value of one function, we can find the best possible value for the other function. This relation is proven theoretically and also implemented and experimented using Matlab®[2].
PSO Based Optimization of Testing and Maintenance Cost in NPPs
Directory of Open Access Journals (Sweden)
Qiang Chou
2014-01-01
Full Text Available Testing and maintenance activities of safety equipment have drawn much attention in Nuclear Power Plant (NPP to risk and cost control. The testing and maintenance activities are often implemented in compliance with the technical specification and maintenance requirements. Technical specification and maintenance-related parameters, that is, allowed outage time (AOT, maintenance period and duration, and so forth, in NPP are associated with controlling risk level and operating cost which need to be minimized. The above problems can be formulated by a constrained multiobjective optimization model, which is widely used in many other engineering problems. Particle swarm optimizations (PSOs have proved their capability to solve these kinds of problems. In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Numerical results have demonstrated the efficiency of our proposed algorithm.
Optimal Micropatterns in 2D Transport Networks and Their Relation to Image Inpainting
Brancolini, Alessio; Rossmanith, Carolin; Wirth, Benedikt
2018-04-01
We consider two different variational models of transport networks: the so-called branched transport problem and the urban planning problem. Based on a novel relation to Mumford-Shah image inpainting and techniques developed in that field, we show for a two-dimensional situation that both highly non-convex network optimization tasks can be transformed into a convex variational problem, which may be very useful from analytical and numerical perspectives. As applications of the convex formulation, we use it to perform numerical simulations (to our knowledge this is the first numerical treatment of urban planning), and we prove a lower bound for the network cost that matches a known upper bound (in terms of how the cost scales in the model parameters) which helps better understand optimal networks and their minimal costs.
Optimization-based topology identification of complex networks
International Nuclear Information System (INIS)
Tang Sheng-Xue; Chen Li; He Yi-Gang
2011-01-01
In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)
Starship Sails Propelled by Cost-Optimized Directed Energy
Benford, J.
Microwave and laser-propelled sails are a new class of spacecraft using photon acceleration. It is the only method of interstellar flight that has no physics issues. Laboratory demonstrations of basic features of beam-driven propulsion, flight, stability (`beam-riding'), and induced spin, have been completed in the last decade, primarily in the microwave. It offers much lower cost probes after a substantial investment in the launcher. Engineering issues are being addressed by other applications: fusion (microwave, millimeter and laser sources) and astronomy (large aperture antennas). There are many candidate sail materials: carbon nanotubes and microtrusses, beryllium, graphene, etc. For acceleration of a sail, what is the cost-optimum high power system? Here the cost is used to constrain design parameters to estimate system power, aperture and elements of capital and operating cost. From general relations for cost-optimal transmitter aperture and power, system cost scales with kinetic energy and inversely with sail diameter and frequency. So optimal sails will be larger, lower in mass and driven by higher frequency beams. Estimated costs include economies of scale. We present several starship point concepts. Systems based on microwave, millimeter wave and laser technologies are of equal cost at today's costs. The frequency advantage of lasers is cancelled by the high cost of both the laser and the radiating optic. Cost of interstellar sailships is very high, driven by current costs for radiation source, antennas and especially electrical power. The high speeds necessary for fast interstellar missions make the operating cost exceed the capital cost. Such sailcraft will not be flown until the cost of electrical power in space is reduced orders of magnitude below current levels.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Directory of Open Access Journals (Sweden)
Huan Chen
Full Text Available This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN. Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong
2015-01-01
This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.
Software defined network inference with evolutionary optimal observation matrices
Malboubi, M; Gong, Y; Yang, Z; Wang, X; Chuah, CN; Sharma, P
2017-01-01
© 2017 Elsevier B.V. A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the scarcity of network measurement resources and the hard constraints that this imposes. This paper proposes a new framework, called SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can lead t...
Optimizing cost-efficiency in mean exposure assessment--cost functions reconsidered.
Mathiassen, Svend Erik; Bolin, Kristian
2011-05-21
Reliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios. Statistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components. Explicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods.For many of the 225 scenarios, the optimal strategy consisted in measuring on only one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set. The analysis procedures developed in the present study can be used
Allocation base of general production costs as optimization of prime costs
Directory of Open Access Journals (Sweden)
Levytska I.O.
2017-03-01
Full Text Available Qualified management aimed at optimizing financial results is the key factor in today's society. Effective management decisions depend on the necessary information about the costs of production process in all its aspects – their structure, types, accounting policies of reflecting costs. General production costs, the so-called indirect costs that are not directly related to the production process, but provide its functioning in terms of supporting structural divisions and create the necessary conditions of production, play a significant role in calculating prime costs of goods (works, services. However, the accurate estimate of prime costs of goods (works, services should be determined with the value of indirect costs (in other words, general production costs, and properly determined with the base of their allocation. The choice of allocation base of general production costs is the significant moment, depending on the nature of business, which must guarantee fair distribution regarding to the largest share of direct expenses in the total structure of production costs. The study finds the essence of general production costs based on the analysis of key definitions of leading Ukrainian economists. The optimal allocation approach of general production costs is to calculate these costs as direct production costs within each subsidiary division (department separately without selecting a base as the main one to the their total amount.
Network formation under heterogeneous costs: The multiple group model
Kamphorst, J.J.A.; van der Laan, G.
2007-01-01
It is widely recognized that the shape of networks influences both individual and aggregate behavior. This raises the question which types of networks are likely to arise. In this paper we investigate a model of network formation, where players are divided into groups and the costs of a link between
Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
Directory of Open Access Journals (Sweden)
Jay Krishna Thakur
2015-08-01
Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
Energy Technology Data Exchange (ETDEWEB)
Niknam, Taher; Meymand, Hamed Zeinoddini; Nayeripour, Majid [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran)
2010-08-15
Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders. (author)
Supply disruption cost for power network planning
International Nuclear Information System (INIS)
Kjoelle, G.H.
1992-09-01
A description is given of the method of approach to calculate the total annual socio-economic cost of power supply disruption and non-supplied energy, included the utilities' cost for planning. The total socio-economic supply disruption cost is the sum of the customers' disruption cost and the utilities' cost for failure and disruption. The mean weighted disruption cost for Norway for one hour disruption is NOK 19 per kWh. The customers' annual disruption cost is calculated with basis in the specific disruption cost referred to heavy load (January) and dimensioning maximum loads. The loads are reduced by factors taking into account the time variations of the failure frequency, duration, the loads and the disruption cost. 6 refs
GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE
Directory of Open Access Journals (Sweden)
Ashish Jain
2012-07-01
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
Self-Optimization of LTE Networks Utilizing Celnet Xplorer
Buvaneswari, A; Polakos, Paul; Buvaneswari, Arumugam
2010-01-01
In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performan...
Information spread in networks: Games, optimal control, and stabilization
Khanafer, Ali
, that can be checked in a distributed fashion. Moreover, we investigate the problem of stabilizing the network when the curing rates of a limited number of nodes can be controlled. In particular, we characterize the number of controllers required for a class of undirected graphs. We also design optimal controllers capable of minimizing the total infection in the network at minimum cost. Finally, we outline a set of open problems in the area of information spread control.
Context-Aware Local Optimization of Sensor Network Deployment
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Meysam Argany
2015-07-01
Full Text Available Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.
Minimisation of a heat exchanger networks' cost over its lifetime
International Nuclear Information System (INIS)
Nemet, Andreja; Klemeš, Jiří Jaromír; Kravanja, Zdravko
2012-01-01
The optimal design of heat exchanger networks (HENs) has a great influence on the profitability of a plant. The optimisation is based on trade-offs between investment and operational cost. The full lifetime of the HEN and future utility prices have to be considered rather than optimising HEN on a yearly basis using current utility prices. Single-period optimisation and synthesis models for HENs reflect current utility prices only. These prices can fluctuate rather quickly and the optimal solution may be very different from a year to year. Deterministic and stochastic multi-period mixed-integer nonlinear programming (MINLP) models for HEN synthesis have been developed to account for future price projections, where the utility cost coefficients are forecasted for the lifetime of the process. An optimal design is then determined for each projection and these designs are compared against a design with fixed current prices by applying the Incremental Net Present Value and other economic measures. In case studies the difference between utility consumption, using previous optimisation methods and new, were significant; e.g. in Case Study 2 the utility savings were 18.4% for hot and 32.6% for cold utility yielding an increase of the Net Present Value (NPV) by 7.8%. Highlights: ► Optimisation using forecasted utility prices can lead to higher energy recovery. ► Incremental Net Present Value when using future versus current prices is positive. ► The reduction of utilities increases with the process lifetime. ► Developed multi-period MINLP models for HEN account for future utility prices.
The ASAC Flight Segment and Network Cost Models
Kaplan, Bruce J.; Lee, David A.; Retina, Nusrat; Wingrove, Earl R., III; Malone, Brett; Hall, Stephen G.; Houser, Scott A.
1997-01-01
To assist NASA in identifying research art, with the greatest potential for improving the air transportation system, two models were developed as part of its Aviation System Analysis Capability (ASAC). The ASAC Flight Segment Cost Model (FSCM) is used to predict aircraft trajectories, resource consumption, and variable operating costs for one or more flight segments. The Network Cost Model can either summarize the costs for a network of flight segments processed by the FSCM or can be used to independently estimate the variable operating costs of flying a fleet of equipment given the number of departures and average flight stage lengths.
Optimizing online social networks for information propagation.
Directory of Open Access Journals (Sweden)
Duan-Bing Chen
Full Text Available Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
Optimizing online social networks for information propagation.
Chen, Duan-Bing; Wang, Guan-Nan; Zeng, An; Fu, Yan; Zhang, Yi-Cheng
2014-01-01
Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
Need for Cost Optimization of Space Life Support Systems
Jones, Harry W.; Anderson, Grant
2017-01-01
As the nation plans manned missions that go far beyond Earth orbit to Mars, there is an urgent need for a robust, disciplined systems engineering methodology that can identify an optimized Environmental Control and Life Support (ECLSS) architecture for long duration deep space missions. But unlike the previously used Equivalent System Mass (ESM), the method must be inclusive of all driving parameters and emphasize the economic analysis of life support system design. The key parameter for this analysis is Life Cycle Cost (LCC). LCC takes into account the cost for development and qualification of the system, launch costs, operational costs, maintenance costs and all other relevant and associated costs. Additionally, an effective methodology must consider system technical performance, safety, reliability, maintainability, crew time, and other factors that could affect the overall merit of the life support system.
Finding the optimal Bayesian network given a constraint graph
Directory of Open Access Journals (Sweden)
Jacob M. Schreiber
2017-07-01
Full Text Available Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.
Cost-optimal levels for energy performance requirements
DEFF Research Database (Denmark)
Thomsen, Kirsten Engelund; Aggerholm, Søren; Kluttig-Erhorn, Heike
2011-01-01
The CA conducted a study on experiences and challenges for setting cost optimal levels for energy performance requirements. The results were used as input by the EU Commission in their work of establishing the Regulation on a comparative methodology framework for calculating cost optimal levels...... of minimum energy performance requirements. In addition to the summary report released in August 2011, the full detailed report on this study is now also made available, just as the EC is about to publish its proposed Regulation for MS to apply in their process to update national building requirements....
LinkMind: link optimization in swarming mobile sensor networks.
Ngo, Trung Dung
2011-01-01
A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
Directory of Open Access Journals (Sweden)
Trung Dung Ngo
2011-08-01
Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
An Optimal Design Model for New Water Distribution Networks in ...
African Journals Online (AJOL)
The mathematical formulation is a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price of pipes, the hydraulic gradient and the loss of pressure. The objective function minimizes the cost of the network which is computed as the sum ...
Transaction costs and social networks in productivity measurement
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
2015-01-01
. Hence, both the absolute productivity measures and, more importantly, the productivity ranking will be distorted. A major driver of transaction costs is poor access to information and contract enforcement assistance. Social networks often catalyse information exchange as well as generate trust...... and support. Hence, we use measures of a firm’s access to social networks as a proxy for the transaction costs the firm faces. We develop a microeconomic production model that takes into account transaction costs and networks. Using a data set of 384 Polish farms, we empirically estimate this model...... and compare different parametric, semiparametric, and nonparametric model specifications. Our results generally support our hypothesis. Especially, large trading networks and dense household networks have a positive influence on a farm’s productivity. Furthermore, our results indicate that transaction costs...
Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing
2017-07-19
Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.
Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search
Directory of Open Access Journals (Sweden)
Zong Woo Geem
2015-07-01
Full Text Available Thus far, various phenomenon-mimicking algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping, ant colony optimization, harmony search, cross entropy, scatter search, and honey-bee mating, have been proposed to optimally design the water distribution networks with respect to design cost. However, flow velocity constraint, which is critical for structural robustness against water hammer or flow circulation against substance sedimentation, was seldom considered in the optimization formulation because of computational complexity. Thus, this study proposes a novel fuzzy-based velocity reliability index, which is to be maximized while the design cost is simultaneously minimized. The velocity reliability index is included in the existing cost optimization formulation and this extended multiobjective formulation is applied to two bench-mark problems. Results show that the model successfully found a Pareto set of multiobjective design solutions in terms of cost minimization and reliability maximization.
Minimizing communication cost among distributed controllers in software defined networks
Arlimatti, Shivaleela; Elbreiki, Walid; Hassan, Suhaidi; Habbal, Adib; Elshaikh, Mohamed
2016-08-01
Software Defined Networking (SDN) is a new paradigm to increase the flexibility of today's network by promising for a programmable network. The fundamental idea behind this new architecture is to simplify network complexity by decoupling control plane and data plane of the network devices, and by making the control plane centralized. Recently controllers have distributed to solve the problem of single point of failure, and to increase scalability and flexibility during workload distribution. Even though, controllers are flexible and scalable to accommodate more number of network switches, yet the problem of intercommunication cost between distributed controllers is still challenging issue in the Software Defined Network environment. This paper, aims to fill the gap by proposing a new mechanism, which minimizes intercommunication cost with graph partitioning algorithm, an NP hard problem. The methodology proposed in this paper is, swapping of network elements between controller domains to minimize communication cost by calculating communication gain. The swapping of elements minimizes inter and intra communication cost among network domains. We validate our work with the OMNeT++ simulation environment tool. Simulation results show that the proposed mechanism minimizes the inter domain communication cost among controllers compared to traditional distributed controllers.
Cost Minimization Model of Gas Transmission Line for Indonesian SIJ Pipeline Network
Directory of Open Access Journals (Sweden)
Septoratno Siregar
2003-05-01
Full Text Available Optimization of Indonesian SIJ gas pipeline network is being discussed here. Optimum pipe diameters together with the corresponding pressure distribution are obtained from minimization of total cost function consisting of investment and operating costs and subjects to some physical (Panhandle A and Panhandle B equations constraints. Iteration technique based on Generalized Steepest-Descent and fourth order Runge-Kutta method are used here. The resulting diameters from this continuous optimization are then rounded to the closest available discrete sizes. We have also calculated toll fee along each segment and safety factor of the network by determining the pipe wall thickness, using ANSI B31.8 standard. Sensitivity analysis of toll fee for variation of flow rates is shown here. The result will gives the diameter and compressor size and compressor location that feasible to use for the SIJ pipeline project. The Result also indicates that the east route cost relatively less expensive than the west cost.
Optimal placement of distributed generation in distribution networks ...
African Journals Online (AJOL)
This paper proposes the application of Particle Swarm Optimization (PSO) technique to find the optimal size and optimum location for the placement of DG in the radial distribution networks for active power compensation by reduction in real power losses and enhancement in voltage profile. In the first segment, the optimal ...
Wang, Wu; Huang, Wei; Zhang, Yongjun
2018-03-01
The grid-integration of Photovoltaic-Storage System brings some undefined factors to the network. In order to make full use of the adjusting ability of Photovoltaic-Storage System (PSS), this paper puts forward a reactive power optimization model, which are used to construct the objective function based on power loss and the device adjusting cost, including energy storage adjusting cost. By using Cataclysmic Genetic Algorithm to solve this optimization problem, and comparing with other optimization method, the result proved that: the method of dynamic extended reactive power optimization this article puts forward, can enhance the effect of reactive power optimization, including reducing power loss and device adjusting cost, meanwhile, it gives consideration to the safety of voltage.
Cost-optimization of the IPv4 zeroconf protocol
Bohnenkamp, H.C.; van der Stok, Peter; Hermanns, H.; Vaandrager, Frits
2003-01-01
This paper investigates the tradeoff between reliability and effectiveness for the IPv4 Zeroconf protocol, proposed by Cheshire/Adoba/Guttman in 2002, dedicated to the selfconfiguration of IP network interfaces. We develop a simple stochastic cost model of the protocol, where reliability is measured
Robinson, Y Harold; Rajaram, M
2015-01-01
Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.
International Nuclear Information System (INIS)
Levin, Todd; Thomas, Valerie M.
2012-01-01
The choice between centralized and decentralized electricity generation is examined for 150 countries as a function of population distribution, electricity consumption, transmission cost, and the cost difference between decentralized and centralized electricity generation. A network algorithm is developed to find the shortest centralized transmission network that spans a given fraction of the population in a country. The least-cost combination of centralized and decentralized electricity that serves the country is determined. Case studies of Botswana, Uganda, and Bangladesh illustrate situations that are more and less suited for decentralized electrification. Specific maps for centralized and decentralized generation are presented to show how the least-cost option varies with the relative costs of centralized and decentralized generation and transmission cost. Centralized and decentralized fractions are calculated for 150 countries. For most of the world's population, centralized electricity is the least-cost option. For a number of countries, particularly in Africa, substantial populations and regions may be most cost-effectively served by decentralized electricity. - Highlights: ► Centralized and decentralized electrification are compared for 150 countries. ► A cost-optimized network algorithm finds the least-cost electrification system. ► Least-cost infrastructures combine centralized and decentralized portions. ► For most people, centralized electricity is cheapest option. ► In much of Africa, decentralized electricity may be cheaper than centralized.
Distributed Optimization based Dynamic Tariff for Congestion Management in Distribution Networks
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei; Zhao, Haoran
2017-01-01
This paper proposes a distributed optimization based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles (EVs) and heat pumps (HPs). The DDT method employs a decomposition based optimization method to have aggregators explicitly...... is able to minimize the overall energy consumption cost and line loss cost, which is different from previous decomposition-based methods such as multiagent system methods. In addition, a reconditioning method and an integral controller are introduced to improve convergence of the distributed optimization...... where challenges arise due to multiple congestion points, multiple types of flexible demands and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks....
A Grooming Nodes Optimal Allocation Method for Multicast in WDM Networks
Directory of Open Access Journals (Sweden)
Chengying Wei
2016-01-01
Full Text Available The grooming node has the capability of grooming multicast traffic with the small granularity into established light at high cost of complexity and node architecture. In the paper, a grooming nodes optimal allocation (GNOA method is proposed to optimize the allocation of the grooming nodes constraint by the blocking probability for multicast traffic in sparse WDM networks. In the proposed GNOA method, the location of each grooming node is determined by the SCLD strategy. The improved smallest cost largest degree (SCLD strategy is designed to select the nongrooming nodes in the proposed GNOA method. The simulation results show that the proposed GNOA method can reduce the required number of grooming nodes and decrease the cost of constructing a network to guarantee a certain request blocking probability when the wavelengths per fiber and transmitter/receiver ports per node are sufficient for the optical multicast in WDM networks.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
NJD
Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.
Optimization of the Compensation of a Meshed MV Network by a Modified Genetic Algorithm
DEFF Research Database (Denmark)
Nielsen, Hans; Paar, M.; Toman, P.
2007-01-01
The article discusses the utilization of a modified genetic algorithm (GA) for the optimization of the shunt compensation in meshed and radial MV distribution networks. The algorithm looks for minimum costs of the network power losses and minimum capital and operating costs of applied capacitors......, all of this under limitations specified by a multicriteria penalization function. The parallel evolution branches in the GA are used for the purpose of the optimization accelaration. The application of this GA has been implemented in Matlab. The evaluation part of the GA implementation is based...... on the steady-state analysis using a linear one-line diagram model of a power network. The results of steady-state solutions are compared with the results from the DIgSILENT PowerFactory program. Its practical applicability is demonstrated on examples of 22 kV and meshed overhead distribution networks....
Network synchronization: optimal and pessimal scale-free topologies
Energy Technology Data Exchange (ETDEWEB)
Donetti, Luca [Departamento de Electronica y Tecnologia de Computadores and Instituto de Fisica Teorica y Computacional Carlos I, Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain); Hurtado, Pablo I; Munoz, Miguel A [Departamento de Electromagnetismo y Fisica de la Materia and Instituto Carlos I de Fisica Teorica y Computacional Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain)], E-mail: mamunoz@onsager.ugr.es
2008-06-06
By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability.
Network synchronization: optimal and pessimal scale-free topologies
International Nuclear Information System (INIS)
Donetti, Luca; Hurtado, Pablo I; Munoz, Miguel A
2008-01-01
By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability
Optimal treatment cost allocation methods in pollution control
International Nuclear Information System (INIS)
Chen Wenying; Fang Dong; Xue Dazhi
1999-01-01
Total emission control is an effective pollution control strategy. However, Chinese application of total emission control lacks reasonable and fair methods for optimal treatment cost allocation, a critical issue in total emission control. The author considers four approaches to allocate treatment costs. The first approach is to set up a multiple-objective planning model and to solve the model using the shortest distance ideal point method. The second approach is to define degree of satisfaction for cost allocation results for each polluter and to establish a method based on this concept. The third is to apply bargaining and arbitration theory to develop a model. The fourth is to establish a cooperative N-person game model which can be solved using the Shapley value method, the core method, the Cost Gap Allocation method or the Minimum Costs-Remaining Savings method. These approaches are compared using a practicable case study
Optimal defense resource allocation in scale-free networks
Zhang, Xuejun; Xu, Guoqiang; Xia, Yongxiang
2018-02-01
The robustness research of networked systems has drawn widespread attention in the past decade, and one of the central topics is to protect the network from external attacks through allocating appropriate defense resource to different nodes. In this paper, we apply a specific particle swarm optimization (PSO) algorithm to optimize the defense resource allocation in scale-free networks. Results reveal that PSO based resource allocation shows a higher robustness than other resource allocation strategies such as uniform, degree-proportional, and betweenness-proportional allocation strategies. Furthermore, we find that assigning less resource to middle-degree nodes under small-scale attack while more resource to low-degree nodes under large-scale attack is conductive to improving the network robustness. Our work provides an insight into the optimal defense resource allocation pattern in scale-free networks and is helpful for designing a more robust network.
Optimization of costs versus radiation exposures in decommissioning
International Nuclear Information System (INIS)
Konzek, G.J.
1979-01-01
The estimated worth of decommissioning optimization planning during each phase of the reactor's life cycle is dependent on many variables. The major variables are tabulated and relatively ranked. For each phase, optimization qualitative values (i.e., cost, safety, maintainability, ALARA, and decommissioning considerations) are estimated and ranked according to their short-term and long-term potential benefits. These estimates depend on the quality of the input data, interpretation of that data, and engineering judgment. Once identified and ranked, these considerations form an integral part of the information data base from which estimates, decisions, and alternatives are derived. The optimization of costs and the amount of occupational radiation exposure reductions are strongly interrelated during decommissioning. Realizing that building the necessary infrastructure for decommissioning will take time is an important first step in any decommissioning plan. In addition, the following conclusions are established to achieve optimization of costs and reduced occupational radiation exposures: the assignment of cost versus man-rem is item-specific and sensitive to the expertise of many interrelated disciplines; a commitment to long-term decommissioning planning by management will provide the conditions needed to achieve optimization; and, to be most effective, costs and exposure reduction are sensitive to the nearness of the decommissioning operation. For a new plant, it is best to start at the beginning of the cycle, update continually, consider innovations, and realize full potential and benefits of this concept. For an older plant, the life cycle methodology permits a comprehensive review of the plant history and the formulation of an orderly decommissioning program based on planning, organization, and effort
Wireless Sensor Network Optimization: Multi-Objective Paradigm.
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-07-20
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
Wireless Sensor Network Optimization: Multi-Objective Paradigm
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-01-01
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271
Cost and Demand Characteristics of Telecom Networks
DEFF Research Database (Denmark)
Falch, Morten
1997-01-01
The Chapter presents the general characteristics of the cost and demand profiles for telecommunication services and identifies the barriers towards a free competitive market for telecommunication services related to these characteristics.......The Chapter presents the general characteristics of the cost and demand profiles for telecommunication services and identifies the barriers towards a free competitive market for telecommunication services related to these characteristics....
Cost-optimal power system extension under flow-based market coupling
Energy Technology Data Exchange (ETDEWEB)
Hagspiel, Simeon; Jaegemann, Cosima; Lindenberger, Dietmar [Koeln Univ. (Germany). Energiewirtschaftliches Inst.; Brown, Tom; Cherevatskiy, Stanislav; Troester, Eckehard [Energynautics GmbH, Langen (Germany)
2013-05-15
Electricity market models, implemented as dynamic programming problems, have been applied widely to identify possible pathways towards a cost-optimal and low carbon electricity system. However, the joint optimization of generation and transmission remains challenging, mainly due to the fact that different characteristics and rules apply to commercial and physical exchanges of electricity in meshed networks. This paper presents a methodology that allows to optimize power generation and transmission infrastructures jointly through an iterative approach based on power transfer distribution factors (PTDFs). As PTDFs are linear representations of the physical load flow equations, they can be implemented in a linear programming environment suitable for large scale problems. The algorithm iteratively updates PTDFs when grid infrastructures are modified due to cost-optimal extension and thus yields an optimal solution with a consistent representation of physical load flows. The method is first demonstrated on a simplified three-node model where it is found to be robust and convergent. It is then applied to the European power system in order to find its cost-optimal development under the prescription of strongly decreasing CO{sub 2} emissions until 2050.
Simultaneous optimization of water and heat exchange networks
Energy Technology Data Exchange (ETDEWEB)
Chen, Zhiyou; Hou, Yanlong; Li, Xiaoduan; Wang, Jingtao [Tianjin University, Tianjin (China)
2014-04-15
This paper focuses on the simultaneous optimization of the heat-integrated water allocation networks. A mathematic model is established to illustrate the modified state-space representation of this problem. An easy logical method is employed to help identify the streams of hot or cold ones. In this model, the water exchange networks (WEN), heat exchange networks (HEN), and the interactions between the WEN and HEN combine together as one unity. Thus, the whole network can be solved at one time, which enhances the possibility to get a global optimal result. Examples from the literature and a PVC plant are analyzed to illustrate the accuracy and applicability of this method.
Design optimization for cost and quality: The robust design approach
Unal, Resit
1990-01-01
Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.
ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
T. SHANKAR
2014-04-01
Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.
Stochastic network optimization with application to communication and queueing systems
Neely, Michael
2010-01-01
This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are prov
Transaction costs and social networks in productivity measurement
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
2015-01-01
and support. Hence, we use measures of a firm’s access to social networks as a proxy for the transaction costs the firm faces. We develop a microeconomic production model that takes into account transaction costs and networks. Using a data set of 384 Polish farms, we empirically estimate this model......We argue that in the presence of transaction costs, observed productivity measures may in many cases understate the true productivity, as production data seldom distinguish between resources entering the production process and resources of a similar type that are sacrificed for transaction costs....... Hence, both the absolute productivity measures and, more importantly, the productivity ranking will be distorted. A major driver of transaction costs is poor access to information and contract enforcement assistance. Social networks often catalyse information exchange as well as generate trust...
A mathematical model for optimization of an integrated network logistic design
Directory of Open Access Journals (Sweden)
Lida Tafaghodi
2011-10-01
Full Text Available In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage, multi-product logistics network design is proposed by formulating a generalized logistics network problem into a mixed-integer nonlinear programming model (MINLP for minimizing the total cost of the closed-loop supply chain network. Moreover, the proposed model is solved by using optimization solver, which provides the decisions related to the facility location problem, optimum quantity of shipped product, and facility capacity. Numerical results show the power of the proposed MINLP model to avoid th sub-optimality caused by separate design of forward and reverse logistics networks and to handle various transportation modes and periodic demand.
Cost-effectiveness analysis of optimal strategy for tumor treatment
International Nuclear Information System (INIS)
Pang, Liuyong; Zhao, Zhong; Song, Xinyu
2016-01-01
We propose and analyze an antitumor model with combined immunotherapy and chemotherapy. Firstly, we explore the treatment effects of single immunotherapy and single chemotherapy, respectively. Results indicate that neither immunotherapy nor chemotherapy alone are adequate to cure a tumor. Hence, we apply optimal theory to investigate how the combination of immunotherapy and chemotherapy should be implemented, for a certain time period, in order to reduce the number of tumor cells, while minimizing the implementation cost of the treatment strategy. Secondly, we establish the existence of the optimality system and use Pontryagin’s Maximum Principle to characterize the optimal levels of the two treatment measures. Furthermore, we calculate the incremental cost-effectiveness ratios to analyze the cost-effectiveness of all possible combinations of the two treatment measures. Finally, numerical results show that the combination of immunotherapy and chemotherapy is the most cost-effective strategy for tumor treatment, and able to eliminate the entire tumor with size 4.470 × 10"8 in a year.
Medical Optimization Network for Space Telemedicine Resources
Shah, R. V.; Mulcahy, R.; Rubin, D.; Antonsen, E. L.; Kerstman, E. L.; Reyes, D.
2017-01-01
INTRODUCTION: Long-duration missions beyond low Earth orbit introduce new constraints to the space medical system such as the inability to evacuate to Earth, communication delays, and limitations in clinical skillsets. NASA recognizes the need to improve capabilities for autonomous care on such missions. As the medical system is developed, it is important to have an ability to evaluate the trade space of what resources will be most important. The Medical Optimization Network for Space Telemedicine Resources was developed for this reason, and is now a system to gauge the relative importance of medical resources in addressing medical conditions. METHODS: A list of medical conditions of potential concern for an exploration mission was referenced from the Integrated Medical Model, a probabilistic model designed to quantify in-flight medical risk. The diagnostic and treatment modalities required to address best and worst-case scenarios of each medical condition, at the terrestrial standard of care, were entered into a database. This list included tangible assets (e.g. medications) and intangible assets (e.g. clinical skills to perform a procedure). A team of physicians working within the Exploration Medical Capability Element of NASA's Human Research Program ranked each of the items listed according to its criticality. Data was then obtained from the IMM for the probability of occurrence of the medical conditions, including a breakdown of best case and worst case, during a Mars reference mission. The probability of occurrence information and criticality for each resource were taken into account during analytics performed using Tableau software. RESULTS: A database and weighting system to evaluate all the diagnostic and treatment modalities was created by combining the probability of condition occurrence data with the criticalities assigned by the physician team. DISCUSSION: Exploration Medical Capabilities research at NASA is focused on providing a medical system to
Topologically determined optimal stochastic resonance responses of spatially embedded networks
International Nuclear Information System (INIS)
Gosak, Marko; Marhl, Marko; Korosak, Dean
2011-01-01
We have analyzed the stochastic resonance phenomenon on spatial networks of bistable and excitable oscillators, which are connected according to their location and the amplitude of external forcing. By smoothly altering the network topology from a scale-free (SF) network with dominating long-range connections to a network where principally only adjacent oscillators are connected, we reveal that besides an optimal noise intensity, there is also a most favorable interaction topology at which the best correlation between the response of the network and the imposed weak external forcing is achieved. For various distributions of the amplitudes of external forcing, the optimal topology is always found in the intermediate regime between the highly heterogeneous SF network and the strong geometric regime. Our findings thus indicate that a suitable number of hubs and with that an optimal ratio between short- and long-range connections is necessary in order to obtain the best global response of a spatial network. Furthermore, we link the existence of the optimal interaction topology to a critical point indicating the transition from a long-range interactions-dominated network to a more lattice-like network structure.
Optimal Cost-Analysis and Design of Circular Footings
Directory of Open Access Journals (Sweden)
Prabir K. Basudhar
2012-10-01
Full Text Available The study pertains to the optimal cost-analysis and design of a circular footing subjected to generalized loadings using sequential unconstrained minimization technique (SUMT in conjunction with Powell’s conjugate direction method for multidimensional search and quadratic interpolation method for one dimensional minimization. The cost of the footing is minimized satisfying all the structural and geotechnical engineering design considerations. As extended penalty function method has been used to convert the constrained problem into an unconstrained one, the developed technique is capable of handling both feasible and infeasible initial design vector. The net saving in cost starting from the best possible manual design ranges from 10 to 20 %. For all practical purposes, the optimum cost is independent of the initial design point. It was observed that for better convergence, the transition parameter should be chosen at least 100 times the initial penalty parameter kr .
OPTIMIZATION OF THE POSITION OF THE LOCAL DISTRIBUTION CENTRE OF THE REGIONAL POST LOGISTICS NETWORK
Directory of Open Access Journals (Sweden)
Paweł DROŹDZIEL
2017-09-01
Full Text Available The phenomenon of the present postal services is the fact that, customers expect the lowest price while maintaining the availability, security and on time delivery of mail items. We can find that, the costs associated with transport of the postal substrate is one of the most important factors affecting the total cost of the postal services. These transport costs depend on various factors such as the investment in vehicles purchase, operational costs of the postal vehicles (costs of maintenance, repairs, fuel costs of the vehicle, etc. labour costs of the drivers and so on. For this reason, it is important to find such an operational - organizational solutions that can reduce the costs associated with the transportation of postal shipments, resulting in reducing the total cost of postal services. One option to do this is to minimize the length of postal transportation routes. This article presents the approach based on the application of graph theory to optimize existing connections of postal logistics network. Published results is oriented to revaluate existing position of local centre and find a location for the new local distribution centre potentially. New location of local distribution centre can to optimize (minimize the total transport costs of the local postal transportation network in area of the Lublin Province.
Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.
2015-05-01
This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network : Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected : Vehicle Program. ...
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
. Since tool life is critically affected by the tool wear, accurate prediction of this wear ... In their work, they established an improvement in the quality ... objective optimization of hard turning using neural network modelling and swarm intelligence ...
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Peralta, Richard C.; Forghani, Ali; Fayad, Hala
2014-04-01
Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.
Optimized Neural Network for Fault Diagnosis and Classification
International Nuclear Information System (INIS)
Elaraby, S.M.
2005-01-01
This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)
Directory of Open Access Journals (Sweden)
Wenliang Zhou
2015-01-01
Full Text Available For a better service level of a train operating plan, we propose an integrated optimization method of train planning and train scheduling, which generally are optimized, respectively. Based on the cost analysis of both passengers travelling and enterprises operation, and the constraint analysis of trains operation, we construct a multiobjective function and build an integrated optimization model with the aim of reducing both passenger travel costs and enterprise operating costs. Then, a solving algorithm is established based on the simulated annealing algorithm. Finally, using as an example the Changzhutan intercity rail network, as an example we analyze the optimized results and the influence of the model parameters on the results.
Behaviour in O of the Neural Networks Training Cost
DEFF Research Database (Denmark)
Goutte, Cyril
1998-01-01
We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number offirst, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location. These calc......We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number offirst, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location....... These calculations arerelated to practical and theoretical aspects of neural networks training....
Optimization approach for saddling cost of medical cyclotrons with fuzziness
International Nuclear Information System (INIS)
Abass, S.A.; Massoud, E.M.A.
2007-01-01
Most radiation fields are combinations of different kinds of radiation. The radiations of most significance are fast neutrons, thermal neutrons, primary gammas and secondary gammas. Thermos's composite shielding materials are designed to attenuate these types of radiation. The shielding design requires an accurate cost-benefit analysis based on uncertainty optimization technique. The theory of fuzzy sets has been employed to formulate and solve the problem of cost-benefit analysis of medical cyclotron. This medical radioisotope production cyclotron is based in Sydney, Australia
Influence maximization in complex networks through optimal percolation
Morone, Flaviano; Makse, Hernán A.
2015-08-01
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Despite the vast use of heuristic strategies to identify influential spreaders, the problem remains unsolved. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. These are topologically tagged as low-degree nodes surrounded by hierarchical coronas of hubs, and are uncovered only through the optimal collective interplay of all the influencers in the network. The present theoretical framework may hold a larger degree of universality, being applicable to other hard optimization problems exhibiting a continuous transition from a known phase.
Directory of Open Access Journals (Sweden)
Liang Tang
2010-01-01
Full Text Available A mathematical model for M/G/1-type queueing networks with multiple user applications and limited resources is established. The goal is to develop a dynamic distributed algorithm for this model, which supports all data traffic as efficiently as possible and makes optimally fair decisions about how to minimize the network performance cost. An online policy gradient optimization algorithm based on a single sample path is provided to avoid suffering from a “curse of dimensionality”. The asymptotic convergence properties of this algorithm are proved. Numerical examples provide valuable insights for bridging mathematical theory with engineering practice.
Genetic algorithm for neural networks optimization
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
Investigations of a Cost-Optimal Zero Energy Balance
DEFF Research Database (Denmark)
Marszal, Anna Joanna; Nørgaard, Jesper; Heiselberg, Per
2012-01-01
The Net Zero Energy Building (Net ZEB) concept is worldwide recognised as a promising solution for decreasing buildings’ energy use. Nevertheless, a consistent definition of the Net ZEB concept is constantly under discussion. One of the points on the Net ZEB agenda is the zero energy balance...... and taken a view point of private building owner to investigate what types of energy uses should be included in the cost-optimal zero energy balance. The analysis is conducted for five renewable energy supply systems and five user profiles with a study case of a multi-storey residential Net ZEB. The results...... have indicated that with current energy prices and technology, a cost-optimal Net ZEB zero energy balance accounts for only the building related energy use. Moreover, with high user related energy use is even more in favour of excluding appliances from the zero energy balance....
Optimization of costs for the DOEL 3 steam generator replacement
International Nuclear Information System (INIS)
Leblois, C.
1994-01-01
Several aspects of steam generator replacement economics are discussed on the basis of the recent replacement carried out in the Doel 3 unit. The choice between repair of replacement policies, as well as the selection of the intervention date were based on a comparison of costs in which various possible scenarios were examined. The contractual approach for the different works to be performed was also an important point, as well as the project organization in which CAD played an important role. This organization allowed to optimize the outage duration and to realize numerous interventions in the reactor building in parallel with the replacement itself. A last aspect of the optimization of costs is the possibility to uprate the plant power. In the case of Doel 3, the plant restarted with a nominal power increased by 10%, of which 5,7% were possible by the increase of the SG heat transfer area. (Author) 6 refs
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning; Canepa, Edward S.; Claudel, Christian G.
2014-01-01
of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can
Optimization-based Method for Automated Road Network Extraction
International Nuclear Information System (INIS)
Xiong, D
2001-01-01
Automated road information extraction has significant applicability in transportation. It provides a means for creating, maintaining, and updating transportation network databases that are needed for purposes ranging from traffic management to automated vehicle navigation and guidance. This paper is to review literature on the subject of road extraction and to describe a study of an optimization-based method for automated road network extraction
Optimal design of an IP/MPLS over DWDM network
Directory of Open Access Journals (Sweden)
Eduardo Canale
2014-04-01
Full Text Available Different approaches for deploying resilient optical networks of low cost constitute a traditional group of NP-Hard problems that have been widely studied. Most of them are based on the construction of low cost networks that fulfill connectivity constraints. However, recent trends to virtualize optical networks over the legacy fiber infrastructure, modified the nature of network design problems and turned inappropriate many of these models and algorithms. In this paper we study a design problem arising from the deployment of an IP/MPLS network over an existing DWDM infrastructure. Besides cost and resiliency, this problem integrates traffic and capacity constraints. We present: an integer programming formulation for the problem, theoretical results, and describe how several metaheuristics were applied in order to find good quality solutions, for a real application case of a telecommunications company.
Cost Vs. Redundancy in FTTH Access Networks
DEFF Research Database (Denmark)
Haraldsson, Gustav Helgi; Pedersen, Jens Myrup
2006-01-01
separate distribution nodes to NT's. The cost of the ear topology is kept down by reusing trenches making extra digging minimal. The results show however that the ear topology with the home-run method is not suitable compared to the tree topology due to the extra fibers needed. Further work could apply...
Optimal search strategies on complex networks
Di Patti, Francesca; Fanelli, Duccio; Piazza, Francesco
2014-01-01
Complex networks are ubiquitous in nature and play a role of paramount importance in many contexts. Internet and the cyberworld, which permeate our everyday life, are self-organized hierarchical graphs. Urban traffic flows on intricate road networks, which impact both transportation design and epidemic control. In the brain, neurons are cabled through heterogeneous connections, which support the propagation of electric signals. In all these cases, the true challenge is to unveil the mechanism...
Optimizing transport in a homogeneous network
WEAIRE, DENIS LAWRENCE
2004-01-01
PUBLISHED Many situations in physics, biology, and engineering consist of the transport of some physical quantity through a network of narrow channels. The ability of a network to transport such a quantity in every direction can be described by the average conductivity associated with it. When the flow through each channel is conserved and derives from a potential function, we show that there exists an upper bound of the average conductivity and explicitly give the expression f...
Optimal scope of supply chain network & operations design
Ma, N.
2014-01-01
The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are
Optimization with PDE constraints ESF networking program 'OPTPDE'
2014-01-01
This book on PDE Constrained Optimization contains contributions on the mathematical analysis and numerical solution of constrained optimal control and optimization problems where a partial differential equation (PDE) or a system of PDEs appears as an essential part of the constraints. The appropriate treatment of such problems requires a fundamental understanding of the subtle interplay between optimization in function spaces and numerical discretization techniques and relies on advanced methodologies from the theory of PDEs and numerical analysis as well as scientific computing. The contributions reflect the work of the European Science Foundation Networking Programme ’Optimization with PDEs’ (OPTPDE).
Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks
DEFF Research Database (Denmark)
Bode, Felix; Binning, Philip John; Nowak, Wolfgang
Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability...... and optimality of any suggested monitoring location or monitoring network. The goal of this project is to develop and establish a concept to assess, design, and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: (1) a high...... be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, wrapped up within the framework of formal multi-objective optimization. In order to gain insight into the flow and transport physics...
Study on Optimization of I and C Architecture for Research Reactors Using Bayesian Networks
Energy Technology Data Exchange (ETDEWEB)
Rahman, Khaili Ur; Shin, Jinsoo; Heo, Gyunyoung [Kyung Hee Univ., Yongin (Korea, Republic of)
2013-07-01
The optimization in terms of redundancy of modules and components in Instrumentation and Control (I and C) architecture is based on cost and availability assuming regulatory requirements are satisfied. The motive of this study is to find an optimized I and C architecture, either in hybrid formation, fully digital or analog, with respect to system availability and relative cost of architecture. The cost of research reactors I and C systems is prone to have effect on marketing competitiveness. As a demonstrative example, the reactor protection system of research reactors is selected. The four cases with different architecture formation were developed with single and double redundancy of bi-stable modules, coincidence processor module, and safety or protection circuit actuation logic. The architecture configurations are transformed to reliability block diagram (RBD) based on logical operation and function of modules. A Bayesian Network (BN) model is constructed from RBD to assess availability. The cost estimation was proposed and reliability cost index RI was suggested.
Study on Optimization of I and C Architecture for Research Reactors Using Bayesian Networks
International Nuclear Information System (INIS)
Rahman, Khaili Ur; Shin, Jinsoo; Heo, Gyunyoung
2013-01-01
The optimization in terms of redundancy of modules and components in Instrumentation and Control (I and C) architecture is based on cost and availability assuming regulatory requirements are satisfied. The motive of this study is to find an optimized I and C architecture, either in hybrid formation, fully digital or analog, with respect to system availability and relative cost of architecture. The cost of research reactors I and C systems is prone to have effect on marketing competitiveness. As a demonstrative example, the reactor protection system of research reactors is selected. The four cases with different architecture formation were developed with single and double redundancy of bi-stable modules, coincidence processor module, and safety or protection circuit actuation logic. The architecture configurations are transformed to reliability block diagram (RBD) based on logical operation and function of modules. A Bayesian Network (BN) model is constructed from RBD to assess availability. The cost estimation was proposed and reliability cost index RI was suggested
TRADING-OFF CONSTRAINTS IN THE PUMP SCHEDULING OPTIMIZATION OF WATER DISTRIBUTION NETWORKS
Directory of Open Access Journals (Sweden)
Gencer Genço\\u011Flu
2016-01-01
Full Text Available Pumps are one of the essential components of water supply systems. Depending of the topography, a water supply system may completely rely on pumping. They may consume non-negligible amount of water authorities' budgets during operation. Besides their energy costs, maintaining the healthiness of pumping systems is another concern for authorities. This study represents a multi-objective optimization method for pump scheduling problem. The optimization objective contains hydraulic and operational constraints. Switching of pumps and usage of electricity tariff are assumed to be key factors for operational reliability and energy consumption and costs of pumping systems. The local optimals for systems operational reliability, energy consumptions and energy costs are investigated resulting from trading-off pump switch and electricity tariff constraints within given set of boundary conditions. In the study, a custom made program is employed that combines genetic algorithm based optimization module with hydraulic network simulation software -EPANET. Developed method is applied on the case study network; N8-3 pressure zone of the Northern Supply of Ankara (Turkey Water Distribution Network. This work offers an efficient method for water authorities aiming to optimize pumping schedules considering expenditures and operational reliability mutually.
Control and Optimization of Network in Networked Control System
Directory of Open Access Journals (Sweden)
Wang Zhiwen
2014-01-01
Full Text Available In order to avoid quality of performance (QoP degradation resulting from quality of service (QoS, the solution to network congestion from the point of control theory, which marks departure of our results from the existing methods, is proposed in this paper. The congestion and bandwidth are regarded as state and control variables, respectively; then, the linear time-invariant (LTI model between congestion state and bandwidth of network is established. Consequently, linear quadratic method is used to eliminate the network congestion by allocating bandwidth dynamically. At last, numerical simulation results are given to illustrate the effectiveness of this modeling approach.
Chaotic Hopfield Neural Network Swarm Optimization and Its Application
Directory of Open Access Journals (Sweden)
Yanxia Sun
2013-01-01
Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.
Energy Cost Optimization in a Water Supply System Case Study
Directory of Open Access Journals (Sweden)
Daniel F. Moreira
2013-01-01
Full Text Available The majority of the life cycle costs (LCC of a pump are related to the energy spent in pumping, with the rest being related to the purchase and maintenance of the equipment. Any optimizations in the energy efficiency of the pumps result in a considerable reduction of the total operational cost. The Fátima water supply system in Portugal was analyzed in order to minimize its operational energy costs. Different pump characteristic curves were analyzed and modeled in order to achieve the most efficient operation point. To determine the best daily pumping operational scheduling pattern, genetic algorithm (GA optimization embedded in the modeling software was considered in contrast with a manual override (MO approach. The main goal was to determine which pumps and what daily scheduling allowed the best economical solution. At the end of the analysis it was possible to reduce the original daily energy costs by 43.7%. This was achieved by introducing more appropriate pumps and by intelligent programming of their operation. Given the heuristic nature of GAs, different approaches were employed and the most common errors were pinpointed, whereby this investigation can be used as a reference for similar future developments.
Cost optimization of induction linac drivers for linear colliders
International Nuclear Information System (INIS)
Barletta, W.A.
1986-01-01
Recent developments in high reliability components for linear induction accelerators (LIA) make possible the use of these devices as economical power drives for very high gradient linear colliders. A particularly attractive realization of this ''two-beam accelerator'' approach is to configure the LIA as a monolithic relativistic klystron operating at 10 to 12 GHz with induction cells providing periodic reacceleration of the high current beam. Based upon a recent engineering design of a state-of-the-art, 10- to 20-MeV LIA at Lawrence Livermore National Laboratory, this paper presents an algorithm for scaling the cost of the relativistic klystron to the parameter regime of interest for the next generation high energy physics machines. The algorithm allows optimization of the collider luminosity with respect to cost by varying the characteristics (pulse length, drive current, repetition rate, etc.) of the klystron. It also allows us to explore cost sensitivities as a guide to research strategies for developing advanced accelerator technologies
Flash memories economic principles of performance, cost and reliability optimization
Richter, Detlev
2014-01-01
The subject of this book is to introduce a model-based quantitative performance indicator methodology applicable for performance, cost and reliability optimization of non-volatile memories. The complex example of flash memories is used to introduce and apply the methodology. It has been developed by the author based on an industrial 2-bit to 4-bit per cell flash development project. For the first time, design and cost aspects of 3D integration of flash memory are treated in this book. Cell, array, performance and reliability effects of flash memories are introduced and analyzed. Key performance parameters are derived to handle the flash complexity. A performance and array memory model is developed and a set of performance indicators characterizing architecture, cost and durability is defined. Flash memories are selected to apply the Performance Indicator Methodology to quantify design and technology innovation. A graphical representation based on trend lines is introduced to support a requirement based pr...
A Generic Methodology for Superstructure Optimization of Different Processing Networks
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Frauzem, Rebecca; Zhang, Lei
2016-01-01
In this paper, we propose a generic computer-aided methodology for synthesis of different processing networks using superstructure optimization. The methodology can handle different network optimization problems of various application fields. It integrates databases with a common data architecture......, a generic model to represent the processing steps, and appropriate optimization tools. A special software interface has been created to automate the steps in the methodology workflow, allow the transfer of data between tools and obtain the mathematical representation of the problem as required...
PlayNCool: Opportunistic Network Coding for Local Optimization of Routing in Wireless Mesh Networks
DEFF Research Database (Denmark)
Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk
2013-01-01
This paper introduces PlayNCool, an opportunistic protocol with local optimization based on network coding to increase the throughput of a wireless mesh network (WMN). PlayNCool aims to enhance current routing protocols by (i) allowing random linear network coding transmissions end-to-end, (ii) r...
SOFTCOST - DEEP SPACE NETWORK SOFTWARE COST MODEL
Tausworthe, R. C.
1994-01-01
The early-on estimation of required resources and a schedule for the development and maintenance of software is usually the least precise aspect of the software life cycle. However, it is desirable to make some sort of an orderly and rational attempt at estimation in order to plan and organize an implementation effort. The Software Cost Estimation Model program, SOFTCOST, was developed to provide a consistent automated resource and schedule model which is more formalized than the often used guesswork model based on experience, intuition, and luck. SOFTCOST was developed after the evaluation of a number of existing cost estimation programs indicated that there was a need for a cost estimation program with a wide range of application and adaptability to diverse kinds of software. SOFTCOST combines several software cost models found in the open literature into one comprehensive set of algorithms that compensate for nearly fifty implementation factors relative to size of the task, inherited baseline, organizational and system environment, and difficulty of the task. SOFTCOST produces mean and variance estimates of software size, implementation productivity, recommended staff level, probable duration, amount of computer resources required, and amount and cost of software documentation. Since the confidence level for a project using mean estimates is small, the user is given the opportunity to enter risk-biased values for effort, duration, and staffing, to achieve higher confidence levels. SOFTCOST then produces a PERT/CPM file with subtask efforts, durations, and precedences defined so as to produce the Work Breakdown Structure (WBS) and schedule having the asked-for overall effort and duration. The SOFTCOST program operates in an interactive environment prompting the user for all of the required input. The program builds the supporting PERT data base in a file for later report generation or revision. The PERT schedule and the WBS schedule may be printed and stored in a
Genetic optimization of neural network architecture
International Nuclear Information System (INIS)
Harp, S.A.; Samad, T.
1994-03-01
Neural networks are now a popular technology for a broad variety of application domains, including the electric utility industry. Yet, as the technology continues to gain increasing acceptance, it is also increasingly apparent that the power that neural networks provide is not an unconditional blessing. Considerable care must be exercised during application development if the full benefit of the technology is to be realized. At present, no fully general theory or methodology for neural network design is available, and application development is a trial-and-error process that is time-consuming and expertise-intensive. Each application demands appropriate selections of the network input space, the network structure, and values of learning algorithm parameters-design choices that are closely coupled in ways that largely remain a mystery. This EPRI-funded exploratory research project was initiated to take the key next step in this research program: the validation of the approach on a realistic problem. We focused on the problem of modeling the thermal performance of the TVA Sequoyah nuclear power plant (units 1 and 2)
Directory of Open Access Journals (Sweden)
Qi Dawei
2010-01-01
Full Text Available A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.
Optimal satisfaction degree in energy harvesting cognitive radio networks
International Nuclear Information System (INIS)
Li Zan; Liu Bo-Yang; Si Jiang-Bo; Zhou Fu-Hui
2015-01-01
A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. (paper)
Optimal satisfaction degree in energy harvesting cognitive radio networks
Li, Zan; Liu, Bo-Yang; Si, Jiang-Bo; Zhou, Fu-Hui
2015-12-01
A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education of China (Grant No. 20110203110011), and the 111 Project (Grant No. B08038).
Optimal neural networks for protein-structure prediction
International Nuclear Information System (INIS)
Head-Gordon, T.; Stillinger, F.H.
1993-01-01
The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident
Localization of multilayer networks by optimized single-layer rewiring.
Jalan, Sarika; Pradhan, Priodyuti
2018-04-01
We study localization properties of principal eigenvectors (PEVs) of multilayer networks (MNs). Starting with a multilayer network corresponding to a delocalized PEV, we rewire the network edges using an optimization technique such that the PEV of the rewired multilayer network becomes more localized. The framework allows us to scrutinize structural and spectral properties of the networks at various localization points during the rewiring process. We show that rewiring only one layer is enough to attain a MN having a highly localized PEV. Our investigation reveals that a single edge rewiring of the optimized MN can lead to the complete delocalization of a highly localized PEV. This sensitivity in the localization behavior of PEVs is accompanied with the second largest eigenvalue lying very close to the largest one. This observation opens an avenue to gain a deeper insight into the origin of PEV localization of networks. Furthermore, analysis of multilayer networks constructed using real-world social and biological data shows that the localization properties of these real-world multilayer networks are in good agreement with the simulation results for the model multilayer network. This paper is relevant to applications that require understanding propagation of perturbation in multilayer networks.
Directory of Open Access Journals (Sweden)
Ahmed R. Abdelaziz
2015-08-01
Full Text Available This paper presents an application of Chaotic differential evolution optimization approach meta-heuristics in solving transmission network expansion planning TNEP using an AC model associated with reactive power planning RPP. The reliabilityredundancy of network analysis optimization problems implicate selection of components with multiple choices and redundancy levels that produce maximum benefits can be subject to the cost weight and volume constraints is presented in this paper. Classical mathematical methods have failed in handling non-convexities and non-smoothness in optimization problems. As an alternative to the classical optimization approaches the meta-heuristics have attracted lot of attention due to their ability to find an almost global optimal solution in reliabilityredundancy optimization problems. Evolutionary algorithms EAs paradigms of evolutionary computation field are stochastic and robust meta-heuristics useful to solve reliabilityredundancy optimization problems. EAs such as genetic algorithm evolutionary programming evolution strategies and differential evolution are being used to find global or near global optimal solution. The Differential Evolution Algorithm DEA population-based algorithm is an optimal algorithm with powerful global searching capability but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaotic Differential Evolution algorithm CDE based on the cat map is recommended which combines DE and chaotic searching algorithm. Simulation results and comparisons show that the chaotic differential evolution algorithm using Cat map is competitive and stable in performance with other optimization approaches and other maps.
A Low Cost Structurally Optimized Design for Diverse Filter Types
Kazmi, Majida; Aziz, Arshad; Akhtar, Pervez; Ikram, Nassar
2016-01-01
A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to 75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image
Topological Effects and Performance Optimization in Transportation Continuous Network Design
Directory of Open Access Journals (Sweden)
Jianjun Wu
2014-01-01
Full Text Available Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.
Cost Effectiveness Analysis of Optimal Malaria Control Strategies in Kenya
Directory of Open Access Journals (Sweden)
Gabriel Otieno
2016-03-01
Full Text Available Malaria remains a leading cause of mortality and morbidity among the children under five and pregnant women in sub-Saharan Africa, but it is preventable and controllable provided current recommended interventions are properly implemented. Better utilization of malaria intervention strategies will ensure the gain for the value for money and producing health improvements in the most cost effective way. The purpose of the value for money drive is to develop a better understanding (and better articulation of costs and results so that more informed, evidence-based choices could be made. Cost effectiveness analysis is carried out to inform decision makers on how to determine where to allocate resources for malaria interventions. This study carries out cost effective analysis of one or all possible combinations of the optimal malaria control strategies (Insecticide Treated Bednets—ITNs, Treatment, Indoor Residual Spray—IRS and Intermittent Preventive Treatment for Pregnant Women—IPTp for the four different transmission settings in order to assess the extent to which the intervention strategies are beneficial and cost effective. For the four different transmission settings in Kenya the optimal solution for the 15 strategies and their associated effectiveness are computed. Cost-effective analysis using Incremental Cost Effectiveness Ratio (ICER was done after ranking the strategies in order of the increasing effectiveness (total infections averted. The findings shows that for the endemic regions the combination of ITNs, IRS, and IPTp was the most cost-effective of all the combined strategies developed in this study for malaria disease control and prevention; for the epidemic prone areas is the combination of the treatment and IRS; for seasonal areas is the use of ITNs plus treatment; and for the low risk areas is the use of treatment only. Malaria transmission in Kenya can be minimized through tailor-made intervention strategies for malaria control
Optimization of deformation monitoring networks using finite element strain analysis
Alizadeh-Khameneh, M. Amin; Eshagh, Mehdi; Jensen, Anna B. O.
2018-04-01
An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point.
Singularities in minimax optimization of networks
DEFF Research Database (Denmark)
Madsen, Kaj; Schjær-Jacobsen, Hans
1976-01-01
A theoretical treatment of singularities in nonlinear minimax optimization problems, which allows for a classification in regular and singular problems, is presented. A theorem for determining a singularity that is present in a given problem is formulated. A group of problems often used in the li......A theoretical treatment of singularities in nonlinear minimax optimization problems, which allows for a classification in regular and singular problems, is presented. A theorem for determining a singularity that is present in a given problem is formulated. A group of problems often used...
Speed Optimization in Liner Shipping Network Design
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Karsten, Christian Vad; Pisinger, David
In the Liner Shipping Network Design Problem (LSNDP) services sail at a given speed throughout a round trip. In reality most services operate with a speed differentiated head- and back-haul, or even individual speeds on every sailing between two ports. The speed of a service is decisive...
Optimizing Key Updates in Sensor Networks
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
2011-01-01
Sensor networks offer the advantages of simple and low–resource communication. Nevertheless, security is of particular importance in many cases such as when sensitive data is communicated or tamper-resistance is required. Updating the security keys is one of the key points in security, which...
Energy Technology Data Exchange (ETDEWEB)
Schmidt, Philipp; Katzfey, Joerg [E-Bridge Consulting GmbH, Bonn (Germany)
2012-09-10
The assessment of business processes of a distribution network operator is often more cost-oriented. In order to optimize the own processes reasonably a holistic process management has to be used in order to measure the costs incurred, the quality of implementation and the quality of the fulfillment of the planning.
Vandevijvere, Stefanie; Annemans, Lieven; Van Oyen, Herman; Tafforeau, Jean; Moreno-Reyes, Rodrigo
2010-11-01
Several surveys in the last 50 years have repeatedly indicated that Belgium is affected by mild iodine deficiency. Within the framework of the national food and health plan in Belgium, a selective, progressive, and monitored strategy was proposed in 2009 to optimize iodine intake. The objective of the present study was to perform a health economic evaluation of the consequences of inadequate iodine intake in Belgium, focusing on undisputed and measurable health outcomes such as thyroid nodular disease and its associated morbidity (hyperthyroidism). For the estimation of direct, indirect, medical, and nonmedical costs related to thyroid nodular diseases in Belgium, data from the Federal Public Service of Public Health, Food Chain Safety and Environment, the National Institute for Disease and Disability Insurance (RIZIV/INAMI), the Information Network about the prescription of reimbursable medicines (FARMANET), Intercontinental Marketing Services, and expert opinions were used. These costs translate into savings after implementation of the iodization program and are defined as costs due to thyroid nodular disease throughout the article. Costs related to the iodization program are referred to as program costs. Only figures dating from before the start of the intervention were exploited. Only adult and elderly people (≥18 years) were taken into account in this study because thyroid nodular diseases predominantly affect this age group. The yearly costs due to thyroid nodular diseases caused by mild iodine deficiency in the Belgian adult population are ∼€38 million. It is expected that the iodization program will result in additional costs of ∼€54,000 per year and decrease the prevalence of thyroid nodular diseases by 38% after a 4-5-year period. The net savings after establishment of the program are therefore estimated to be at least €14 million a year. Optimization of iodine intake in Belgium should be quite cost effective, if only considering its impact on
Spatial prisoner's dilemma optimally played in small-world networks
International Nuclear Information System (INIS)
Masuda, Naoki; Aihara, Kazuyuki
2003-01-01
Cooperation is commonly found in ecological and social systems even when it apparently seems that individuals can benefit from selfish behavior. We investigate how cooperation emerges with the spatial prisoner's dilemma played in a class of networks ranging from regular lattices to random networks. We find that, among these networks, small-world topology is the optimal structure when we take into account the speed at which cooperative behavior propagates. Our results may explain why the small-world properties are self-organized in real networks
Optimization of multicast optical networks with genetic algorithm
Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng
2007-11-01
In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.
Prediction of Optimal Design and Deflection of Space Structures Using Neural Networks
Directory of Open Access Journals (Sweden)
Reza Kamyab Moghadas
2012-01-01
Full Text Available The main aim of the present work is to determine the optimal design and maximum deflection of double layer grids spending low computational cost using neural networks. The design variables of the optimization problem are cross-sectional area of the elements as well as the length of the span and height of the structures. In this paper, a number of double layer grids with various random values of length and height are selected and optimized by simultaneous perturbation stochastic approximation algorithm. Then, radial basis function (RBF and generalized regression (GR neural networks are trained to predict the optimal design and maximum deflection of the structures. The numerical results demonstrate the efficiency of the proposed methodology.
An Optimal Routing Algorithm in Service Customized 5G Networks
Directory of Open Access Journals (Sweden)
Haipeng Yao
2016-01-01
Full Text Available With the widespread use of Internet, the scale of mobile data traffic grows explosively, which makes 5G networks in cellular networks become a growing concern. Recently, the ideas related to future network, for example, Software Defined Networking (SDN, Content-Centric Networking (CCN, and Big Data, have drawn more and more attention. In this paper, we propose a service-customized 5G network architecture by introducing the ideas of separation between control plane and data plane, in-network caching, and Big Data processing and analysis to resolve the problems traditional cellular radio networks face. Moreover, we design an optimal routing algorithm for this architecture, which can minimize average response hops in the network. Simulation results reveal that, by introducing the cache, the network performance can be obviously improved in different network conditions compared to the scenario without a cache. In addition, we explore the change of cache hit rate and average response hops under different cache replacement policies, cache sizes, content popularity, and network topologies, respectively.
LTE-Advanced Radio and Network Optimization
DEFF Research Database (Denmark)
Velez, Fernando J.; Sousa, Sofia; Flores, Jessica Acevedo
2015-01-01
In cellular optimization, the UL and DL the values from carrier-to-noise-plus-interference ratio (CNIR) from/at the mobile station are very important parameters. From a detailed analysis of its variation with the coverage and reuse distances for different values of the Channel Quality Indicator (...
Optimal Sales Schemes for Network Goods
DEFF Research Database (Denmark)
Parakhonyak, Alexei; Vikander, Nick
consumers simultaneously, serve them all sequentially, or employ any intermediate scheme. We show that the optimal sales scheme is purely sequential, where each consumer observes all previous sales before choosing whether to buy himself. A sequential scheme maximizes the amount of information available...
Optimal Simulations by Butterfly Networks: Extended Abstract,
1987-11-01
Typescript , Univ. of Massachusetts; submitted for nublication. 1_2.2 Ll, - W 12. ifliU 1.8 UI1.25 . l i I 61 MICROCOPY RESOLUTION TEST CHART NATIONAL...1987): An optimal mapping of the FFT algorithm onto the tlypercube architecture. Typescript , Univ. of Massachusetts; submitted for publication. (HR I
Heterogeneous Deployment Analysis for Cost-Effective Mobile Network Evolution
DEFF Research Database (Denmark)
Coletti, Claudio
2013-01-01
network coverage and boosting network capacity in traffic hot-spot areas. The thesis deals with the deployment of both outdoor small cells and indoor femto cells. Amongst the outdoor solution, particular emphasis is put on relay base stations as backhaul costs can be reduced by utilizing LTE spectrum...... statistical models of deployment areas, the performance analysis is carried out in the form of operator case studies for large-scale deployment scenarios, including realistic macro network layouts and inhomogeneous spatial traffic distributions. Deployment of small cells is performed by means of proposed...... heuristic deployment algorithms, which combine network coverage and spatial user density information. As a secondary aspect, deployment solutions achieving the same coverage performance are compared in terms of Total Cost of Ownership (TCO), in order to investigate the viability of different deployment...
A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Shibo He
2010-01-01
Full Text Available In wireless sensor networks (WSNs, there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.
Optimal design of water supply networks for enhancing seismic reliability
International Nuclear Information System (INIS)
Yoo, Do Guen; Kang, Doosun; Kim, Joong Hoon
2016-01-01
The goal of the present study is to construct a reliability evaluation model of a water supply system taking seismic hazards and present techniques to enhance hydraulic reliability of the design into consideration. To maximize seismic reliability with limited budgets, an optimal design model is developed using an optimization technique called harmony search (HS). The model is applied to actual water supply systems to determine pipe diameters that can maximize seismic reliability. The reliabilities between the optimal design and existing designs were compared and analyzed. The optimal design would both enhance reliability by approximately 8.9% and have a construction cost of approximately 1.3% less than current pipe construction cost. In addition, the reinforcement of the durability of individual pipes without considering the system produced ineffective results in terms of both cost and reliability. Therefore, to increase the supply ability of the entire system, optimized pipe diameter combinations should be derived. Systems in which normal status hydraulic stability and abnormal status available demand could be maximally secured if configured through the optimal design. - Highlights: • We construct a seismic reliability evaluation model of water supply system. • We present technique to enhance hydraulic reliability in the aspect of design. • Harmony search algorithm is applied in optimal designs process. • The effects of the proposed optimal design are improved reliability about by 9%. • Optimized pipe diameter combinations should be derived indispensably.
Aging Cost Optimization for Planning and Management of Energy Storage Systems
Directory of Open Access Journals (Sweden)
Saman Korjani
2017-11-01
Full Text Available In recent years, many studies have proposed the use of energy storage systems (ESSs for the mitigation of renewable energy source (RES intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs, which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs. The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.
Linear versus quadratic portfolio optimization model with transaction cost
Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah
2014-06-01
Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.
Neural network for nonsmooth pseudoconvex optimization with general convex constraints.
Bian, Wei; Ma, Litao; Qin, Sitian; Xue, Xiaoping
2018-05-01
In this paper, a one-layer recurrent neural network is proposed for solving a class of nonsmooth, pseudoconvex optimization problems with general convex constraints. Based on the smoothing method, we construct a new regularization function, which does not depend on any information of the feasible region. Thanks to the special structure of the regularization function, we prove the global existence, uniqueness and "slow solution" character of the state of the proposed neural network. Moreover, the state solution of the proposed network is proved to be convergent to the feasible region in finite time and to the optimal solution set of the related optimization problem subsequently. In particular, the convergence of the state to an exact optimal solution is also considered in this paper. Numerical examples with simulation results are given to show the efficiency and good characteristics of the proposed network. In addition, some preliminary theoretical analysis and application of the proposed network for a wider class of dynamic portfolio optimization are included. Copyright © 2018 Elsevier Ltd. All rights reserved.
Cascade-robustness optimization of coupling preference in interconnected networks
International Nuclear Information System (INIS)
Zhang, Xue-Jun; Xu, Guo-Qiang; Zhu, Yan-Bo; Xia, Yong-Xiang
2016-01-01
Highlights: • A specific memetic algorithm was proposed to optimize coupling links. • A small toy model was investigated to examine the underlying mechanism. • The MA optimized strategy exhibits a moderate assortative pattern. • A novel coupling coefficient index was proposed to quantify coupling preference. - Abstract: Recently, the robustness of interconnected networks has attracted extensive attentions, one of which is to investigate the influence of coupling preference. In this paper, the memetic algorithm (MA) is employed to optimize the coupling links of interconnected networks. Afterwards, a comparison is made between MA optimized coupling strategy and traditional assortative, disassortative and random coupling preferences. It is found that the MA optimized coupling strategy with a moderate assortative value shows an outstanding performance against cascading failures on both synthetic scale-free interconnected networks and real-world networks. We then provide an explanation for this phenomenon from a micro-scope point of view and propose a coupling coefficient index to quantify the coupling preference. Our work is helpful for the design of robust interconnected networks.
Optimal Path Choice in Railway Passenger Travel Network Based on Residual Train Capacity
Directory of Open Access Journals (Sweden)
Fei Dou
2014-01-01
Full Text Available Passenger’s optimal path choice is one of the prominent research topics in the field of railway passenger transport organization. More and more different train types are available, increasing path choices from departure to destination for travelers are unstoppable. However, travelers cannot avoid being confused when they hope to choose a perfect travel plan based on various travel time and cost constraints before departure. In this study, railway passenger travel network is constructed based on train timetable. Both the generalized cost function we developed and the residual train capacity are considered to be the foundation of path searching procedure. The railway passenger travel network topology is analyzed based on residual train capacity. Considering the total travel time, the total travel cost, and the total number of passengers, we propose an optimal path searching algorithm based on residual train capacity in railway passenger travel network. Finally, the rationale of the railway passenger travel network and the optimal path generation algorithm are verified positively by case study.
Applying neural networks to optimize instrumentation performance
Energy Technology Data Exchange (ETDEWEB)
Start, S.E.; Peters, G.G.
1995-06-01
Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate.
Applying neural networks to optimize instrumentation performance
International Nuclear Information System (INIS)
Start, S.E.; Peters, G.G.
1995-01-01
Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate
Brain Network Analysis: Separating Cost from Topology Using Cost-Integration
Ginestet, Cedric E.; Nichols, Thomas E.; Bullmore, Ed T.; Simmons, Andrew
2011-01-01
A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration. PMID:21829437
Brain network analysis: separating cost from topology using cost-integration.
Directory of Open Access Journals (Sweden)
Cedric E Ginestet
Full Text Available A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i differences in weighted costs and (ii differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.
Optimization of hydrometric monitoring network in urban drainage systems using information theory.
Yazdi, J
2017-10-01
Regular and continuous monitoring of urban runoff in both quality and quantity aspects is of great importance for controlling and managing surface runoff. Due to the considerable costs of establishing new gauges, optimization of the monitoring network is essential. This research proposes an approach for site selection of new discharge stations in urban areas, based on entropy theory in conjunction with multi-objective optimization tools and numerical models. The modeling framework provides an optimal trade-off between the maximum possible information content and the minimum shared information among stations. This approach was applied to the main surface-water collection system in Tehran to determine new optimal monitoring points under the cost considerations. Experimental results on this drainage network show that the obtained cost-effective designs noticeably outperform the consulting engineers' proposal in terms of both information contents and shared information. The research also determined the highly frequent sites at the Pareto front which might be important for decision makers to give a priority for gauge installation on those locations of the network.
Directory of Open Access Journals (Sweden)
Zhengfeng Huang
2013-01-01
Full Text Available Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers’ expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link’s proportional flow eigenvalues (mean and covariance obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method.
Optimal Quantum Spatial Search on Random Temporal Networks.
Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser
2017-12-01
To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G(n,p), where p is the probability that any two given nodes are connected: After every time interval τ, a new graph G(n,p) replaces the previous one. We prove analytically that, for any given p, there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O(sqrt[n]), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.
Optimal Quantum Spatial Search on Random Temporal Networks
Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser
2017-12-01
To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.
Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.
Directory of Open Access Journals (Sweden)
Saket Navlakha
2015-07-01
Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.
Optimization of stochastic discrete systems and control on complex networks computational networks
Lozovanu, Dmitrii
2014-01-01
This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks. It includes the authors' new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational networks. First, the book studies the finite state space of Markov processes and reviews the existing methods and algorithms for determining the main characteristics in Markov chains, before proposing new approaches based on dynamic programming and combinatorial methods. Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems. In closing, the book's final chapter is devoted to finite horizon stochastic con...
Optimizing Human Input in Social Network Analysis
2018-01-23
7] T. L. Lai and H. Robbins, “Asymptotically efficient adaptive allocation rules,” Advances in applied mathematics , vol. 6, no. 1, pp. 4–22, 1985. [8...W. Whitt, “Heavy traffic limit theorems for queues: a survey,” in Mathematical Methods in Queueing Theory. Springer, 1974, pp. 307–350. [9] H...Regret lower bounds and optimal algorithms,” in Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer
Ren, Danping; Wu, Shanshan; Zhang, Lijing
2016-09-01
In view of the characteristics of the global control and flexible monitor of software-defined networks (SDN), we proposes a new optical access network architecture dedicated to Wavelength Division Multiplexing-Passive Optical Network (WDM-PON) systems based on SDN. The network coding (NC) technology is also applied into this architecture to enhance the utilization of wavelength resource and reduce the costs of light source. Simulation results show that this scheme can optimize the throughput of the WDM-PON network, greatly reduce the system time delay and energy consumption.
Optimal interdependence between networks for the evolution of cooperation.
Wang, Zhen; Szolnoki, Attila; Perc, Matjaž
2013-01-01
Recent research has identified interactions between networks as crucial for the outcome of evolutionary games taking place on them. While the consensus is that interdependence does promote cooperation by means of organizational complexity and enhanced reciprocity that is out of reach on isolated networks, we here address the question just how much interdependence there should be. Intuitively, one might assume the more the better. However, we show that in fact only an intermediate density of sufficiently strong interactions between networks warrants an optimal resolution of social dilemmas. This is due to an intricate interplay between the heterogeneity that causes an asymmetric strategy flow because of the additional links between the networks, and the independent formation of cooperative patterns on each individual network. Presented results are robust to variations of the strategy updating rule, the topology of interdependent networks, and the governing social dilemma, thus suggesting a high degree of universality.
Life Cycle Cost Optimization of a BOLIG+ Zero Energy Building
DEFF Research Database (Denmark)
Marszal, Anna Joanna
. However, before being fully implemented in the national building codes and international standards, the ZEB concept requires a clear understanding and a uniform definition. The ZEB concept is an energy-conservation solution, whose successful adaptation in real life depends significantly on private...... building owners’ approach to it. For this particular target group, the cost is often an obstacle when investing money in environmental or climate friendly products. Therefore, this PhD project took the perspective of a future private ZEB owner to investigate the cost-optimal Net ZEB definition applicable...... in the Danish context. The review of the various ZEB approaches indicated a general concept of a Zero Energy Building as a building with significantly reduced energy demand that is balanced by an equivalent energy generation from renewable sources. And, with this as a general framework, each ZEB definition...
Life Cycle Cost Optimization of a Bolig+ Zero Energy Building
DEFF Research Database (Denmark)
Marszal, Anna Joanna
. However, before being fully implemented in the national building codesand international standards, the ZEB concept requires a clear understanding and a uniform definition. The ZEB concept is an energy-conservation solution, whose successful adaptation in real life depends significantly on private building...... owners’ approach to it. For thisparticular target group, the cost is often an obstacle when investing money in environmental or climate friendly products. Therefore, this PhD project took theperspective of a future private ZEB owner to investigate the cost-optimal Net ZEB definition applicable...... in the Danish context. The review of the various ZEB approaches indicated a general concept of a Zero Energy Building as a building with significantly reduced energy demand that isbalanced by an equivalent energy generation from renewable sources. And, with this as a general framework, each ZEB definition...
A Decision Processing Algorithm for CDC Location Under Minimum Cost SCM Network
Park, N. K.; Kim, J. Y.; Choi, W. Y.; Tian, Z. M.; Kim, D. J.
Location of CDC in the matter of network on Supply Chain is becoming on the high concern these days. Present status of methods on CDC has been mainly based on the calculation manually by the spread sheet to achieve the goal of minimum logistics cost. This study is focused on the development of new processing algorithm to overcome the limit of present methods, and examination of the propriety of this algorithm by case study. The algorithm suggested by this study is based on the principle of optimization on the directive GRAPH of SCM model and suggest the algorithm utilizing the traditionally introduced MST, shortest paths finding methods, etc. By the aftermath of this study, it helps to assess suitability of the present on-going SCM network and could be the criterion on the decision-making process for the optimal SCM network building-up for the demand prospect in the future.
OPTIMIZATION OF DISJOINTS FOR MINIMIZATION OF FAILURE IN WDM OPTICAL NETWORK
Directory of Open Access Journals (Sweden)
A. Renugadevi
2015-06-01
Full Text Available In an optical network, the fiber optic cable is used for communication between the nodes in a network by passing lights. The main problem in optical network is finding the link disjoints as well as optimal solution for the disjoints. To tolerate a single link failure in the network, the enhanced active path first algorithm is used which computes the re-routed back-up path. The multiple link failure in a network called fibre span disjoint path problem is solved using integer linear programming algorithm. The loop back recovery is used to provide pre-planned recovery of link or node failures in a network which allows dynamic choice of routes over pre-planned directions. Considering reliability in a mesh networks, the reliability algorithm helps to achieve the maximum reliability in two-path protection. It addresses the multiple disjoint failures that arise in a network and discusses the best solution between paths shared nodes or links. The unified algorithm is used to generate the optimal results with minimum cost for multiple link failures. The heuristic algorithm namely maximum arbitrary double-link protection algorithm helps to pre-compute the back-up path for double-link failures. In all the above approaches the shortest optimized path must be improved. To find the best shortest path, link-disjoint lightpath algorithm is designed to compute the disjoint occurred in a network and it also satisfies the wavelength continuity constraint in wavelength division multiplexing. A polynomial time algorithm Wavelength Division Multiplexing – Passive Optical Networking is used to compute the disjoint happen in the network. The overall time efficiency is analyzed and performance is evaluated through simulations.
International Nuclear Information System (INIS)
Narvaez R, Paulo Cesar; Galeano P, Haiver
2002-01-01
Optimal design problem of liquid distribution systems has been viewed as the selection of pipe sizes and pumps, which will minimize overall costs, accomplishing the flow and pressure constraints. There is a set of methods for least cost design of liquids distribution networks (6). In the last years, some of them have been studied broadly: linear programming (1, 4, 5, 7], non-linear programming [8, 9], and genetic algorithms (3, 10, 13). This paper describes the development of a cost equation and the objective function for liquid distribution networks that together to the mathematical model and the solution method of the flow problem developed by Narvaez (11), were used by in a computer model that involves the application of an genetic algorithm to the problem of least cost design of liquids distribution networks
The optimal vertical structure in the electricity industry when the incumbent has a cost advantage
International Nuclear Information System (INIS)
Kurakawa, Yukihide
2013-01-01
This paper studies how the vertical structure of the electricity industry affects the social welfare when the incumbent has a cost advantage in generation relative to the entrants. The model consists of a generation sector and a transmission sector. In the generation sector the incumbent and entrants compete in a Cournot fashion taking as given the access charge to the transmission network set in advance by the regulator to maximize the social welfare. Two vertical structures, integration and separation, are considered. Under vertical separation the transmission network is established as an organization independent of every generator, whereas under vertical integration it is a part of the incumbent's organization. The optimal vertical structure is shown to depend on the number of entrants. If the number of entrants is smaller than a certain threshold, vertical separation is superior in welfare to vertical integration, and vice versa. This is because the choice of vertical structure produces a trade-off in the effects on competition promotion and production efficiency. If a break-even constraint is imposed in the transmission sector, however, vertical integration is shown to be always superior in welfare. - Highlights: • We examine the optimal vertical structure in the electricity industry. • We model a generation sector in which the incumbent has a cost advantage. • A trade-off between production efficiency and competition promotion occurs. • The optimal vertical structure depends on the number of entrants. • Vertical integration is always superior if a break-even constraint is imposed
Physical Protection System Upgrades - Optimizing for Performance and Cost
International Nuclear Information System (INIS)
Hicks, Mary Jane; Bouchard, Ann M.
1999-01-01
CPA--Cost and Performance Analysis--is an architecture that supports analysis of physical protection systems and upgrade options. ASSESS (Analytic System and Software for Evaluating Security Systems), a tool for evaluating performance of physical protection systems, currently forms the cornerstone for evaluating detection probabilities and delay times of the system. Cost and performance data are offered to the decision-maker at the systems level and to technologists at the path-element level. A new optimization engine has been attached to the CPA methodology to automate analyses of many combinations (portfolios) of technologies. That engine controls a new analysis sequencer that automatically modifies ASSESS PPS files (facility descriptions), automatically invokes ASSESS Outsider analysis and then saves results for post-processing. Users can constrain the search to an upper bound on total cost, to a lower bound on level of performance, or to include specific technologies or technology types. This process has been applied to a set of technology development proposals to identify those portfolios that provide the most improvement in physical security for the lowest cost to install, operate and maintain at a baseline facility
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
In this paper, the configuration of a district heating (DH) network which connects from the heating plant to the end users was optimized with emphasizing the network thermal performance. Each end user in the network represents a building block. The locations of the building blocks are fixed while...... the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... by multi factors as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding pressure and temperature limitation, as well as the corresponding network heat loss....
Optimal networks of future gravitational-wave telescopes
Raffai, Péter; Gondán, László; Heng, Ik Siong; Kelecsényi, Nándor; Logue, Josh; Márka, Zsuzsa; Márka, Szabolcs
2013-08-01
We aim to find the optimal site locations for a hypothetical network of 1-3 triangular gravitational-wave telescopes. We define the following N-telescope figures of merit (FoMs) and construct three corresponding metrics: (a) capability of reconstructing the signal polarization; (b) accuracy in source localization; and (c) accuracy in reconstructing the parameters of a standard binary source. We also define a combined metric that takes into account the three FoMs with practically equal weight. After constructing a geomap of possible telescope sites, we give the optimal 2-telescope networks for the four FoMs separately in example cases where the location of the first telescope has been predetermined. We found that based on the combined metric, placing the first telescope to Australia provides the most options for optimal site selection when extending the network with a second instrument. We suggest geographical regions where a potential second and third telescope could be placed to get optimal network performance in terms of our FoMs. Additionally, we use a similar approach to find the optimal location and orientation for the proposed LIGO-India detector within a five-detector network with Advanced LIGO (Hanford), Advanced LIGO (Livingston), Advanced Virgo, and KAGRA. We found that the FoMs do not change greatly in sites within India, though the network can suffer a significant loss in reconstructing signal polarizations if the orientation angle of an L-shaped LIGO-India is not set to the optimal value of ˜58.2°( + k × 90°) (measured counterclockwise from East to the bisector of the arms).
Optimal networks of future gravitational-wave telescopes
International Nuclear Information System (INIS)
Raffai, Péter; Márka, Zsuzsa; Márka, Szabolcs; Gondán, László; Kelecsényi, Nándor; Heng, Ik Siong; Logue, Josh
2013-01-01
We aim to find the optimal site locations for a hypothetical network of 1–3 triangular gravitational-wave telescopes. We define the following N-telescope figures of merit (FoMs) and construct three corresponding metrics: (a) capability of reconstructing the signal polarization; (b) accuracy in source localization; and (c) accuracy in reconstructing the parameters of a standard binary source. We also define a combined metric that takes into account the three FoMs with practically equal weight. After constructing a geomap of possible telescope sites, we give the optimal 2-telescope networks for the four FoMs separately in example cases where the location of the first telescope has been predetermined. We found that based on the combined metric, placing the first telescope to Australia provides the most options for optimal site selection when extending the network with a second instrument. We suggest geographical regions where a potential second and third telescope could be placed to get optimal network performance in terms of our FoMs. Additionally, we use a similar approach to find the optimal location and orientation for the proposed LIGO-India detector within a five-detector network with Advanced LIGO (Hanford), Advanced LIGO (Livingston), Advanced Virgo, and KAGRA. We found that the FoMs do not change greatly in sites within India, though the network can suffer a significant loss in reconstructing signal polarizations if the orientation angle of an L-shaped LIGO-India is not set to the optimal value of ∼58.2°( + k × 90°) (measured counterclockwise from East to the bisector of the arms). (paper)
Design and optimizing factors of PACS network architecture
International Nuclear Information System (INIS)
Tao Yonghao; Miao Jingtao
2001-01-01
Objective: Exploring the design and optimizing factors of picture archiving and communication system (PACS) network architecture. Methods: Based on the PACS of shanghai first hospital to performed the measurements and tests on the requirements of network bandwidth and transmitting rate for different PACS functions and procedures respectively in static and dynamic network traffic situation, utilizing the network monitoring tools which built-in workstations and provided by Windows NT. Results: No obvious difference between switch equipment and HUB when measurements and tests implemented in static situation except route which slow down the rate markedly. In dynamic environment Switch is able to provide higher bandwidth utilizing than HUB and local system scope communication achieved faster transmitting rate than global system. Conclusion: The primary optimizing factors of PACS network architecture design include concise network topology and disassemble tremendous global traffic to multiple distributed local scope network communication to reduce the traffic of network backbone. The most important issue is guarantee essential bandwidth for diagnosis procedure of medical imaging
Directory of Open Access Journals (Sweden)
Zhaoyu Chen
2018-01-01
Full Text Available The network planning is a key factor that directly affects the performance of the wireless networks. Distributed antenna system (DAS is an effective strategy for the network planning. This paper investigates the antenna deployment in a DAS for the high-speed railway communication networks and formulates an optimization problem which is NP-hard for achieving the optimal deployment of the antennas in the DAS. To solve this problem, a scheme based on an improved cuckoo search based on dimension cells (ICSDC algorithm is proposed. ICSDC introduces the dimension cell mechanism to avoid the internal dimension interferences in order to improve the performance of the algorithm. Simulation results show that the proposed ICSDC-based scheme obtains a lower network cost compared with the uniform network planning method. Moreover, ICSDC algorithm has better performance in terms of the convergence rate and accuracy compared with the conventional cuckoo search algorithm, the particle swarm optimization, and the firefly algorithm.
Max-Min Optimality of Service Rate Control in Closed Queueing Networks
Xia, Li
2013-04-01
In this technical note, we discuss the optimality properties of service rate control in closed Jackson networks. We prove that when the cost function is linear to a particular service rate, the system performance is monotonic w.r.t. (with respect to) that service rate and the optimal value of that service rate can be either maximum or minimum (we call it Max-Min optimality); When the second-order derivative of the cost function w.r.t. a particular service rate is always positive (negative), which makes the cost function strictly convex (concave), the optimal value of such service rate for the performance maximization (minimization) problem can be either maximum or minimum. To the best of our knowledge, this is the most general result for the optimality of service rates in closed Jackson networks and all the previous works only involve the first conclusion. Moreover, our result is also valid for both the state-dependent and load-dependent service rates, under both the time-average and customer-average performance criteria.
Multi-objective optimization in computer networks using metaheuristics
Donoso, Yezid
2007-01-01
Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...
Optimal Design of the Feeder-Bus Network Based on the Transfer System
Directory of Open Access Journals (Sweden)
Lianbo Deng
2013-01-01
Full Text Available This paper studied the classic feeder-bus network design problem (FBNDP, which can be described as follows: for the passenger travel demand between rail stations and bus stops on a given urban transit network, it designs the optimal feeder bus routes and frequencies so as to minimize the passengers’ travel expense and the operator’s cost. We extended the demand pattern of M-to-1 in most existing researches to M-to-M. We comprehensively considered the passenger travel cost, which includes the waiting and riding cost on the bus, riding cost on rail, and transfer cost between these two transportation modes, and presented a new genetic algorithm that determines the optimal feeder-bus operating frequencies under strict constraint conditions. The numerical examples under different demand patterns have been experienced and analysed, which showed the robustness and efficiency of the presented algorithm. We also found that the distribution pattern of the travel demand has a significant influence on the feeder-bus network construction.
Cross-layer optimization of wireless multi-hop networks
Soldati, Pablo
2007-01-01
The interest in wireless communications has grown constantly for the past decades, leading to an enormous number of applications and services embraced by billions of users. In order to meet the increasing demand for mobile Internet access, several high data-rate radio networking technologies have been proposed to offer wide area high-speed wireless communications, eventually replacing fixed (wired) networks for many applications. This thesis considers cross-layer optimization of multi-hop rad...
SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING
Directory of Open Access Journals (Sweden)
Viorel MINZU
2015-12-01
Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.
Influence maximization in complex networks through optimal percolation
Morone, Flaviano; Makse, Hernan; CUNY Collaboration; CUNY Collaboration
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. Reference: F. Morone, H. A. Makse, Nature 524,65-68 (2015)
Switching Fuzzy Guaranteed Cost Control for Nonlinear Networked Control Systems
Directory of Open Access Journals (Sweden)
Linqin Cai
2014-01-01
Full Text Available This paper deals with the problem of guaranteed cost control for a class of nonlinear networked control systems (NCSs with time-varying delay. A guaranteed cost controller design method is proposed to achieve the desired control performance based on the switched T-S fuzzy model. The switching mechanism is introduced to handle the uncertainties of NCSs. Based on Lyapunov functional approach, some sufficient conditions for the existence of state feedback robust guaranteed cost controller are presented. Simulation results show that the proposed method is effective to guarantee system’s global asymptotic stability and quality of service (QoS.
Guaranteed cost control of mobile sensor networks with Markov switching topologies.
Zhao, Yuan; Guo, Ge; Ding, Lei
2015-09-01
This paper investigates the consensus seeking problem of mobile sensor networks (MSNs) with random switching topologies. The network communication topologies are composed of a set of directed graphs (or digraph) with a spanning tree. The switching of topologies is governed by a Markov chain. The consensus seeking problem is addressed by introducing a global topology-aware linear quadratic (LQ) cost as the performance measure. By state transformation, the consensus problem is transformed to the stabilization of a Markovian jump system with guaranteed cost. A sufficient condition for global mean-square consensus is derived in the context of stochastic stability analysis of Markovian jump systems. A computational algorithm is given to synchronously calculate both the sub-optimal consensus controller gains and the sub-minimum upper bound of the cost. The effectiveness of the proposed design method is illustrated by three numerical examples. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Starling flock networks manage uncertainty in consensus at low cost.
Directory of Open Access Journals (Sweden)
George F Young
Full Text Available Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors. We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks.
Starling Flock Networks Manage Uncertainty in Consensus at Low Cost
Young, George F.; Scardovi, Luca; Cavagna, Andrea; Giardina, Irene; Leonard, Naomi E.
2013-01-01
Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors). We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks. PMID:23382667
A framework for reactive optimization in mobile ad hoc networks
DEFF Research Database (Denmark)
McClary, Dan; Syrotiuk, Violet; Kulahci, Murat
2008-01-01
We present a framework to optimize the performance of a mobile ad hoc network over a wide range of operating conditions. It includes screening experiments to quantify the parameters and interactions among parameters influential to throughput. Profile-driven regression is applied to obtain a model....... The predictive accuracy of the model is monitored and used to update the model dynamically. The results indicate the framework may be useful for the optimization of dynamic systems of high dimension....
Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization
Directory of Open Access Journals (Sweden)
Xing Xu
2014-04-01
Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.
A Global Network Alignment Method Using Discrete Particle Swarm Optimization.
Huang, Jiaxiang; Gong, Maoguo; Ma, Lijia
2016-10-19
Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations. The particle statuses and status updating rules are redefined in a discrete form by using permutation. A seed-and-extend strategy is employed to guide the searching for the superior alignment. The proposed initialization method "seeds" matches with high sequence similarity into the alignment, which guarantees the functional coherence of the mapping nodes. A greedy local search method is designed as the "extension" procedure to iteratively optimize the edge conservations. PSONA is compared with several state-of-art methods on ten network pairs combined by five species. The experimental results demonstrate that the proposed aligner can map the proteins with high functional coherence and can be used as a booster to effectively refine the well-studied aligners.
Network cost in transmission and distribution of electric power
International Nuclear Information System (INIS)
Lindahl, A.; Naeslund, B.; Oettinger-Biberg, C.; Olander, H.; Wuolikainen, T.; Fritz, P.
1994-01-01
This report is divided in two parts, where part 1 treats the charges on the regional nets with special emphasis on the net owners tariffs on a deregulated market. Part 2 describes the development of the network costs in electric power distribution for the period 1991-1993. 11 figs, 33 tabs
evaluation of total annual costs of heat exchanger networks using
African Journals Online (AJOL)
This study presents pinch analysis of some heat exchanger networks (HENs) problems using Hint integration (HINT) software. Three examples reported to have been solved using different approaches by various researchers to obtain the least possible total annual cost (TAC) were solved using the Hint software. In this work ...
Energy aware swarm optimization with intercluster search for wireless sensor network.
Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan
2015-01-01
Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.
Energy Aware Swarm Optimization with Intercluster Search for Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Shanmugasundaram Thilagavathi
2015-01-01
Full Text Available Wireless sensor networks (WSNs are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO algorithm with modified connected dominating set (CDS based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH. Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.
A Low-Carbon-Based Bilevel Optimization Model for Public Transit Network
Directory of Open Access Journals (Sweden)
Xu Sun
2013-01-01
Full Text Available To satisfy the demand of low-carbon transportation, this paper studies the optimization of public transit network based on the concept of low carbon. Taking travel time, operation cost, energy consumption, pollutant emission, and traffic efficiency as the optimization objectives, a bilevel model is proposed in order to maximize the benefits of both travelers and operators and minimize the environmental cost. Then the model is solved with the differential evolution (DE algorithm and applied to a real network of Baoji city. The results show that the model can not only ensure the benefits of travelers and operators, but can also reduce pollutant emission and energy consumption caused by the operations of buses, which reflects the concept of low carbon.
Design of shared unit-dose drug distribution network using multi-level particle swarm optimization.
Chen, Linjie; Monteiro, Thibaud; Wang, Tao; Marcon, Eric
2018-03-01
Unit-dose drug distribution systems provide optimal choices in terms of medication security and efficiency for organizing the drug-use process in large hospitals. As small hospitals have to share such automatic systems for economic reasons, the structure of their logistic organization becomes a very sensitive issue. In the research reported here, we develop a generalized multi-level optimization method - multi-level particle swarm optimization (MLPSO) - to design a shared unit-dose drug distribution network. Structurally, the problem studied can be considered as a type of capacitated location-routing problem (CLRP) with new constraints related to specific production planning. This kind of problem implies that a multi-level optimization should be performed in order to minimize logistic operating costs. Our results show that with the proposed algorithm, a more suitable modeling framework, as well as computational time savings and better optimization performance are obtained than that reported in the literature on this subject.
An efficient and cost effective nuclear medicine image network
International Nuclear Information System (INIS)
Sampathkumaran, K.S.; Miller, T.R.
1987-01-01
An image network that is in use in a large nuclear medicine department is described. This network was designed to efficiently handle a large volume of clinical data at reasonable cost. Small, limited function computers are attached to each scintillation camera for data acquisition. The images are transferred by cable network or floppy disc to a large, powerful central computer for processing and display. Cost is minimized by use of small acquisition computers not equipped with expensive video display systems or elaborate analysis software. Thus, financial expenditure can be concentrated in a powerful central computer providing a centralized data base, rapid processing, and an efficient environment for program development. Clinical work is greatly facilitated because the physicians can process and display all studies without leaving the main reading area. (orig.)
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Coevolution of Synchronization and Cooperation in Costly Networked Interactions
Antonioni, Alberto; Cardillo, Alessio
2017-06-01
Despite the large number of studies on synchronization, the hypothesis that interactions bear a cost for involved individuals has seldom been considered. The introduction of costly interactions leads, instead, to the formulation of a dichotomous scenario in which an individual may decide to cooperate and pay the cost in order to get synchronized with the rest of the population. Alternatively, the same individual can decide to free ride, without incurring any cost, waiting for others to get synchronized to his or her state. Thus, the emergence of synchronization may be seen as the byproduct of an evolutionary game in which individuals decide their behavior according to the benefit-to-cost ratio they accrued in the past. We study the onset of cooperation and synchronization in networked populations of Kuramoto oscillators and report how topology is essential in order for cooperation to thrive. We also display how different classes of topology foster synchronization differently both at microscopic and macroscopic levels.
Testing of Strategies for the Acceleration of the Cost Optimization
Energy Technology Data Exchange (ETDEWEB)
Ponciroli, Roberto [Argonne National Lab. (ANL), Argonne, IL (United States); Vilim, Richard B. [Argonne National Lab. (ANL), Argonne, IL (United States)
2017-08-31
The general problem addressed in the Nuclear-Renewable Hybrid Energy System (N-R HES) project is finding the optimum economical dispatch (ED) and capacity planning solutions for the hybrid energy systems. In the present test-problem configuration, the N-R HES unit is composed of three electrical power-generating components, i.e. the Balance of Plant (BOP), the Secondary Energy Source (SES), and the Energy Storage (ES). In addition, there is an Industrial Process (IP), which is devoted to hydrogen generation. At this preliminary stage, the goal is to find the power outputs of each one of the N-R HES unit components (BOP, SES, ES) and the IP hydrogen production level that maximizes the unit profit by simultaneously satisfying individual component operational constraints. The optimization problem is meant to be solved in the Risk Analysis Virtual Environment (RAVEN) framework. The dynamic response of the N-R HES unit components is simulated by using dedicated object-oriented models written in the Modelica modeling language. Though this code coupling provides for very accurate predictions, the ensuing optimization problem is characterized by a very large number of solution variables. To ease the computational burden and to improve the path to a converged solution, a method to better estimate the initial guess for the optimization problem solution was developed. The proposed approach led to the definition of a suitable Monte Carlo-based optimization algorithm (called the preconditioner), which provides an initial guess for the optimal N-R HES power dispatch and the optimal installed capacity for each one of the unit components. The preconditioner samples a set of stochastic power scenarios for each one of the N-R HES unit components, and then for each of them the corresponding value of a suitably defined cost function is evaluated. After having simulated a sufficient number of power histories, the configuration which ensures the highest profit is selected as the optimal
Designing the optimal bit: balancing energetic cost, speed and reliability.
Deshpande, Abhishek; Gopalkrishnan, Manoj; Ouldridge, Thomas E; Jones, Nick S
2017-08-01
We consider the challenge of operating a reliable bit that can be rapidly erased. We find that both erasing and reliability times are non-monotonic in the underlying friction, leading to a trade-off between erasing speed and bit reliability. Fast erasure is possible at the expense of low reliability at moderate friction, and high reliability comes at the expense of slow erasure in the underdamped and overdamped limits. Within a given class of bit parameters and control strategies, we define 'optimal' designs of bits that meet the desired reliability and erasing time requirements with the lowest operational work cost. We find that optimal designs always saturate the bound on the erasing time requirement, but can exceed the required reliability time if critically damped. The non-trivial geometry of the reliability and erasing time scales allows us to exclude large regions of parameter space as suboptimal. We find that optimal designs are either critically damped or close to critical damping under the erasing procedure.
Service network design of bike sharing systems analysis and optimization
Vogel, Patrick
2016-01-01
This monograph presents a tactical planning approach for service network design in metropolitan areas. Designing the service network requires the suitable aggregation of demand data as well as the anticipation of operational relocation decisions. To this end, an integrated approach of data analysis and mathematical optimization is introduced. The book also includes a case study based on real-world data to demonstrate the benefit of the proposed service network design approach. The target audience comprises primarily research experts in the field of traffic engineering, but the book may also be beneficial for graduate students.
An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Plank, James [University of Tennessee (UT); Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2016-01-01
As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.
5G heterogeneous networks self-organizing and optimization
Rong, Bo; Kadoch, Michel; Sun, Songlin; Li, Wenjing
2016-01-01
This SpringerBrief provides state-of-the-art technical reviews on self-organizing and optimization in 5G systems. It covers the latest research results from physical-layer channel modeling to software defined network (SDN) architecture. This book focuses on the cutting-edge wireless technologies such as heterogeneous networks (HetNets), self-organizing network (SON), smart low power node (LPN), 3D-MIMO, and more. It will help researchers from both the academic and industrial worlds to better understand the technical momentum of 5G key technologies.
No Cost – Low Cost Compressed Air System Optimization in Industry
Dharma, A.; Budiarsa, N.; Watiniasih, N.; Antara, N. G.
2018-04-01
Energy conservation is a systematic, integrated of effort, in order to preserve energy sources and improve energy utilization efficiency. Utilization of energy in efficient manner without reducing the energy usage it must. Energy conservation efforts are applied at all stages of utilization, from utilization of energy resources to final, using efficient technology, and cultivating an energy-efficient lifestyle. The most common way is to promote energy efficiency in the industry on end use and overcome barriers to achieve such efficiency by using system energy optimization programs. The facts show that energy saving efforts in the process usually only focus on replacing tools and not an overall system improvement effort. In this research, a framework of sustainable energy reduction work in companies that have or have not implemented energy management system (EnMS) will be conducted a systematic technical approach in evaluating accurately a compressed-air system and potential optimization through observation, measurement and verification environmental conditions and processes, then processing the physical quantities of systems such as air flow, pressure and electrical power energy at any given time measured using comparative analysis methods in this industry, to provide the potential savings of energy saving is greater than the component approach, with no cost to the lowest cost (no cost - low cost). The process of evaluating energy utilization and energy saving opportunities will provide recommendations for increasing efficiency in the industry and reducing CO2 emissions and improving environmental quality.
Optimal sensor placement for leakage detection and isolation in water distribution networks
Rosich Oliva, Albert; Sarrate Estruch, Ramon; Nejjari Akhi-Elarab, Fatiha
2012-01-01
In this paper, the problem of leakage detection and isolation in water distribution networks is addressed applying an optimal sensor placement methodology. The chosen technique is based on structural models and thus it is suitable to handle non-linear and large scale systems. A drawback of this technique arises when costs are assigned uniformly. A main contribution of this paper is the proposal of an iterative methodology that focuses on identifying essential sensors which ultimately leads to...
International Nuclear Information System (INIS)
Meo, Santolo; Zohoori, Alireza; Vahedi, Abolfazl
2016-01-01
Highlights: • A new optimal design of flux switching permanent magnet generator is developed. • A prototype is employed to validate numerical data used for optimization. • A novel hybrid multi-objective particle swarm optimization approach is proposed. • Optimization targets are weight, cost, voltage and its total harmonic distortion. • The hybrid approach preference is proved compared with other optimization methods. - Abstract: In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent
Life Cycle Cost optimization of a BOLIG+ Zero Energy Building
Energy Technology Data Exchange (ETDEWEB)
Marszal, A.J.
2011-12-15
Buildings consume approximately 40% of the world's primary energy use. Considering the total energy consumption throughout the whole life cycle of a building, the energy performance and supply is an important issue in the context of climate change, scarcity of energy resources and reduction of global energy consumption. An energy consuming as well as producing building, labelled as the Zero Energy Building (ZEB) concept, is seen as one of the solutions that could change the picture of energy consumption in the building sector, and thus contribute to the reduction of the global energy use. However, before being fully implemented in the national building codes and international standards, the ZEB concept requires a clear understanding and a uniform definition. The ZEB concept is an energy-conservation solution, whose successful adaptation in real life depends significantly on private building owners' approach to it. For this particular target group, the cost is often an obstacle when investing money in environmental or climate friendly products. Therefore, this PhD project took the perspective of a future private ZEB owner to investigate the cost-optimal Net ZEB definition applicable in the Danish context. The review of the various ZEB approaches indicated a general concept of a Zero Energy Building as a building with significantly reduced energy demand that is balanced by an equivalent energy generation from renewable sources. And, with this as a general framework, each ZEB definition should further specify: (1) the connection or the lack of it to the energy infrastructure, (2) the unit of the balance, (3) the period of the balance, (4) the types of energy use included in the balance, (5) the minimum energy performance requirements (6) the renewable energy supply options, and if applicable (7) the requirements of the building-grid interaction. Moreover, the study revealed that the future ZEB definitions applied in Denmark should mostly be focused on grid
Directory of Open Access Journals (Sweden)
JongHyup Lee
2016-08-01
Full Text Available For practical deployment of wireless sensor networks (WSN, WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.
Lee, JongHyup; Pak, Dohyun
2016-01-01
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743
A Risk-Based Multi-Objective Optimization Concept for Early-Warning Monitoring Networks
Bode, F.; Loschko, M.; Nowak, W.
2014-12-01
Groundwater is a resource for drinking water and hence needs to be protected from contaminations. However, many well catchments include an inventory of known and unknown risk sources which cannot be eliminated, especially in urban regions. As matter of risk control, all these risk sources should be monitored. A one-to-one monitoring situation for each risk source would lead to a cost explosion and is even impossible for unknown risk sources. However, smart optimization concepts could help to find promising low-cost monitoring network designs.In this work we develop a concept to plan monitoring networks using multi-objective optimization. Our considered objectives are to maximize the probability of detecting all contaminations and the early warning time and to minimize the installation and operating costs of the monitoring network. A qualitative risk ranking is used to prioritize the known risk sources for monitoring. The unknown risk sources can neither be located nor ranked. Instead, we represent them by a virtual line of risk sources surrounding the production well.We classify risk sources into four different categories: severe, medium and tolerable for known risk sources and an extra category for the unknown ones. With that, early warning time and detection probability become individual objectives for each risk class. Thus, decision makers can identify monitoring networks which are valid for controlling the top risk sources, and evaluate the capabilities (or search for least-cost upgrade) to also cover moderate, tolerable and unknown risk sources. Monitoring networks which are valid for the remaining risk also cover all other risk sources but the early-warning time suffers.The data provided for the optimization algorithm are calculated in a preprocessing step by a flow and transport model. Uncertainties due to hydro(geo)logical phenomena are taken into account by Monte-Carlo simulations. To avoid numerical dispersion during the transport simulations we use the
Incremental Optimization of Hub and Spoke Network for the Spokes’ Numbers and Flow
Directory of Open Access Journals (Sweden)
Yanfeng Wang
2015-01-01
Full Text Available Hub and spoke network problem is solved as part of a strategic decision making process which may have a profound effect on the future of enterprises. In view of the existing network structure, as time goes on, the number of spokes and the flow change because of different sources of uncertainty. Hence, the incremental optimization of hub and spoke network problem is considered in this paper, and the policy makers should adopt a series of strategies to cope with the change, such as setting up new hubs, adjusting the capacity level of original hubs, or closing some original hubs. The objective is to minimize the total cost, which includes the setup costs for the new hubs, the closure costs, and the adjustment costs for the original hubs as well as the flow routing costs. Two mixed-integer linear programming formulations are proposed and analyzed for this problem. China Deppon Logistics as an example is performed to present computational analysis, and we analyze the changes in the solutions driven by the number of spokes and the flow. The tests also allow an analysis to consider the effect of variation in parameters on network.
Directory of Open Access Journals (Sweden)
Mohammad Mahdi Saffar
2015-01-01
Full Text Available Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ε-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE is applied.
Model checking optimal finite-horizon control for probabilistic gene regulatory networks.
Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan
2017-12-14
Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.
International Nuclear Information System (INIS)
Gurcan, Metin N.; Sahiner, Berkman; Chan Heangping; Hadjiiski, Lubomir; Petrick, Nicholas
2001-01-01
Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are 'optimized' by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area A z under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost
Distributed Optimization of Multi Beam Directional Communication Networks
2017-06-30
Distributed Optimization of Multi-Beam Directional Communication Networks Theodoros Tsiligkaridis MIT Lincoln Laboratory Lexington, MA 02141, USA...based routing. I. INTRODUCTION Missions where multiple communication goals are of in- terest are becoming more prevalent in military applications...Multilayer communications may occur within a coalition; for example, a team consisting of ground vehicles and an airborne set of assets may desire to
Optimization of composite panels using neural networks and genetic algorithms
Ruijter, W.; Spallino, R.; Warnet, Laurent; de Boer, Andries
2003-01-01
The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a
Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. (2009). Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring. Presentation at the IADIS international conference on Cognition and Exploratory in Digital Age (CELDA 2009). November, 20-22, 2009, Rome, Italy.
Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning
Optimal resource allocation in downlink CDMA wireless networks
Endrayanto, A.I.
2013-01-01
This thesis presents a full analytical characterization of the optimal joint downlink rate and power assignment for maximal total system throughput in a multi cell CDMA network. In Chapter 2, we analyze the feasibility of downlink power assignment in a linear model of two CDMA cell, under the
Optimization of Gas Flow Network using the Traveling Salesman ...
African Journals Online (AJOL)
The overall goal of this paper is to develop a general formulation for an optimal infrastructure layout design of gas pipeline distribution networks using algorithm developed from the application of two industrial engineering concepts: the traveling salesman problem (TSP) and the nearest neighbor (NN). The focus is on the ...
On Optimal Geographical Caching in Heterogeneous Cellular Networks
Serbetci, Berksan; Goseling, Jasper
2017-01-01
In this work we investigate optimal geographical caching in heterogeneous cellular networks where different types of base stations (BSs) have different cache capacities. Users request files from a content library according to a known probability distribution. The performance metric is the total hit
Towards Optimal Buffer Size in Wi-Fi Networks
Showail, Ahmad J.
2016-01-19
Buffer sizing is an important network configuration parameter that impacts the quality of data traffic. Falling memory cost and the fallacy that ‘more is better’ lead to over provisioning network devices with large buffers. Over-buffering or the so called ‘bufferbloat’ phenomenon creates excessive end-to-end delay in today’s networks. On the other hand, under-buffering results in frequent packet loss and subsequent under-utilization of network resources. The buffer sizing problem has been studied extensively for wired networks. However, there is little work addressing the unique challenges of wireless environment. In this dissertation, we discuss buffer sizing challenges in wireless networks, classify the state-of-the-art solutions, and propose two novel buffer sizing schemes. The first scheme targets buffer sizing in wireless multi-hop networks where the radio spectral resource is shared among a set of con- tending nodes. Hence, it sizes the buffer collectively and distributes it over a set of interfering devices. The second buffer sizing scheme is designed to cope up with recent Wi-Fi enhancements. It adapts the buffer size based on measured link characteristics and network load. Also, it enforces limits on the buffer size to maximize frame aggregation benefits. Both mechanisms are evaluated using simulation as well as testbed implementation over half-duplex and full-duplex wireless networks. Experimental evaluation shows that our proposal reduces latency by an order of magnitude.
Optimal scheduling for distribution network with redox flow battery storage
International Nuclear Information System (INIS)
Hosseina, Majid; Bathaee, Seyed Mohammad Taghi
2016-01-01
Highlights: • A novel method for optimal scheduling of storages in radial network is presented. • Peak shaving and load leveling are the main objectives. • Vanadium redox flow battery is considered as the energy storage unit. • Real data is used for simulation. - Abstract: There are many advantages to utilize storages in electric power system. Peak shaving, load leveling, load frequency control, integration of renewable, energy trading and spinning reserve are the most important of them. Batteries, especially redox flow batteries, are one of the appropriate storages for utilization in distribution network. This paper presents a novel, heuristic and practical method for optimal scheduling in distribution network with flow battery storage. This heuristic method is more suitable for scheduling and operation of distribution networks which require installation of storages. Peak shaving and load leveling is considered as the main objective in this paper. Several indices are presented in this paper for determine the place of storages and also scheduling for optimal use of energy in them. Simulations of this paper are based on real information of distribution network substation that located in Semnan, Iran.
Shi, Shengchao; Li, Guangxia; An, Kang; Gao, Bin; Zheng, Gan
2017-09-04
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach's method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.
Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)
2002-01-01
Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic
Neural network-based optimal adaptive output feedback control of a helicopter UAV.
Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani
2013-07-01
Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.
Optimal Intermittent Operation of Water Distribution Networks under Water Shortage
Directory of Open Access Journals (Sweden)
mohamad Solgi
2017-07-01
Full Text Available Under water shortage conditions, it is necessary to exercise water consumption management practices in water distribution networks (WDN. Intermittent supply of water is one such practice that makes it possible to supply consumption nodal demands with the required pressure via water cutoff to some consumers during certain hours of the day. One of the most important issues that must be observed in this management practice is the equitable and uniform water distribution among the consumers. In the present study, uniformity in water distribution and minimum supply of water to all consumers are defined as justice and equity, respectively. Also, an optimization model has been developed to find an optimal intermittent supply schedule that ensures maximum number of demand nodes are supplied with water while the constraints on the operation of water distribution networks are also observed. To show the efficiency of the proposed model, it has been used in the Two-Loop distribution network under several different scenarios of water shortage. The optimization model has been solved using the honey bee mating optimization algorithm (HBMO linked to the hydraulic simulator EPANET. The results obtained confirm the efficiency of the proposed model in achieving an optimal intermittent supply schedule. Moreover, the model is found capable of distributing the available water in an equitable and just manner among all the consumers even under severe water shoratges.
Derivative-free optimization under uncertainty applied to costly simulators
International Nuclear Information System (INIS)
Pauwels, Benoit
2016-01-01
The modeling of complex phenomena encountered in industrial issues can lead to the study of numerical simulation codes. These simulators may require extensive execution time (from hours to days), involve uncertain parameters and even be intrinsically stochastic. Importantly within the context of simulation-based optimization, the derivatives of the outputs with respect to the inputs may be inexistent, inaccessible or too costly to approximate reasonably. This thesis is organized in four chapters. The first chapter discusses the state of the art in derivative-free optimization and uncertainty modeling. The next three chapters introduce three independent - although connected - contributions to the field of derivative-free optimization in the presence of uncertainty. The second chapter addresses the emulation of costly stochastic simulation codes - stochastic in the sense simulations run with the same input parameters may lead to distinct outputs. Such was the matter of the CODESTOCH project carried out at the Summer mathematical research center on scientific computing and its applications (CEMRACS) during the summer of 2013, together with two Ph.D. students from Electricity of France (EDF) and the Atomic Energy and Alternative Energies Commission (CEA). We designed four methods to build emulators for functions whose values are probability density functions. These methods were tested on two toy functions and applied to industrial simulation codes concerned with three complex phenomena: the spatial distribution of molecules in a hydrocarbon system (IFPEN), the life cycle of large electric transformers (EDF) and the repercussions of a hypothetical accidental in a nuclear plant (CEA). Emulation was a preliminary process towards optimization in the first two cases. In the third chapter we consider the influence of inaccurate objective function evaluations on direct search - a classical derivative-free optimization method. In real settings inaccuracy may never vanish
Synchrony-optimized networks of non-identical Kuramoto oscillators
International Nuclear Information System (INIS)
Brede, Markus
2008-01-01
In this Letter we discuss a method for generating synchrony-optimized coupling architectures of Kuramoto oscillators with a heterogeneous distribution of native frequencies. The method allows us to relate the properties of the coupling network to its synchronizability. These relations were previously only established from a linear stability analysis of the identical oscillator case. We further demonstrate that the heterogeneity in the oscillator population produces heterogeneity in the optimal coupling network as well. Two rules for enhancing the synchronizability of a given network by a suitable placement of oscillators are given: (i) native frequencies of adjacent oscillators must be anti-correlated and (ii) frequency magnitudes should positively correlate with the degree of the node they are placed at
Optimal design of district heating and cooling pipe network of seawater-source heat pump
Energy Technology Data Exchange (ETDEWEB)
Li, Xiang-li; Duanmu, Lin; Shu, Hai-wen [School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian, Liaoning Province 116024 (China)
2010-01-15
The district heating and cooling (DHC) system of a seawater-source heat pump is large system engineering. The investments and the operational cost of DHC pipe network are higher than a tradition system. Traditional design methods only satisfy the needs of the technology but dissatisfy the needs of the economy, which not only waste a mass of money but also bring problems to the operation, the maintenance and the management. So we build a least-annualized-cost global optimal mathematic model that comprises all constrict conditions. Furthermore, this model considers the variety of heating load and cooling load, the operational adjustment in different periods of the year. Genetic algorithm (GA) is used to obtain the optimal combinations of discrete diameters. Some operators of GA are selected to reduce the calculation time and obtain good calculation accuracy. This optimal method is used to the design of the DHC network of Xinghai Bay commercial district which is a real engineering. The design optimization can avoid the matter of the hydraulic unbalance of the system, enhance the running efficiency and greatly reduce the annualized-cost comparing with the traditional design method. (author)
Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Yao Wang
2015-08-01
Full Text Available Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.
Designing Green Networks and Network Operations Saving Run-the-Engine Costs
Minoli, Daniel
2011-01-01
In recent years the confluence of socio-political trends toward environmental responsibility and the pressing need to reduce Run-the-Engine (RTE) costs has given birth to a nascent discipline of Green IT. A clear and concise introduction to green networks and green network operations, this book examines analytical measures and discusses virtualization, network computing, and web services as approaches for green data centers and networks. It identifies some strategies for green appliance and end devices and examines the methodical steps that can be taken over time to achieve a seamless migratio
Optimal design of degradation tests in presence of cost constraint
International Nuclear Information System (INIS)
Wu, S.-J.; Chang, C.-T.
2002-01-01
Degradation test is a useful technique to provide information about the lifetime of highly reliable products. We obtain the degradation measurements over time in such a test. In general, the degradation data are modeled by a nonlinear regression model with random coefficients. If we can obtain the estimates of parameters under the model, then the failure time distribution can be estimated. However, in order to obtain a precise estimate of the percentile of failure time distribution, one needs to design an optimal degradation test. Therefore, this study proposes an approach to determine the number of units to test, inspection frequency, and termination time of a degradation test under a determined cost of experiment such that the variance of estimator of percentile of failure time distribution is minimum. The method will be applied to a numerical example and the sensitivity analysis will be discussed
Optimal operation of water distribution networks by predictive control ...
African Journals Online (AJOL)
This paper presents an approach for the operational optimisation of potable water distribution networks. The maximisation of the use of low-cost power (e.g. overnight pumping) and the maintenance of a target chlorine concentration at final delivery points were defined as important optimisation objectives. The first objective ...
Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium
Directory of Open Access Journals (Sweden)
Xiao Han
2017-12-01
Full Text Available This paper focuses on the optimal intraday scheduling of a distribution system that includes renewable energy (RE generation, energy storage systems (ESSs, and thermostatically controlled loads (TCLs. This system also provides time-of-use pricing to customers. Unlike previous studies, this study attempts to examine how to optimize the allocation of electric energy and to improve the equilibrium of the load curve. Accordingly, we propose a concept of load equilibrium entropy to quantify the overall equilibrium of the load curve and reflect the allocation optimization of electric energy. Based on this entropy, we built a novel multi-objective optimal dispatching model to minimize the operational cost and maximize the load curve equilibrium. To aggregate TCLs into the optimization objective, we introduced the concept of a virtual power plant (VPP and proposed a calculation method for VPP operating characteristics based on the equivalent thermal parameter model and the state-queue control method. The Particle Swarm Optimization algorithm was employed to solve the optimization problems. The simulation results illustrated that the proposed dispatching model can achieve cost reductions of system operations, peak load curtailment, and efficiency improvements, and also verified that the load equilibrium entropy can be used as a novel index of load characteristics.
An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking
Directory of Open Access Journals (Sweden)
Hyunhun Cho
2015-05-01
Full Text Available Legacy networks do not open the precise information of the network domain because of scalability, management and commercial reasons, and it is very hard to compute an optimal path to the destination. According to today’s ICT environment change, in order to meet the new network requirements, the concept of software-defined networking (SDN has been developed as a technological alternative to overcome the limitations of the legacy network structure and to introduce innovative concepts. The purpose of this paper is to propose the application that calculates the optimal paths for general data transmission and real-time audio/video transmission, which consist of the major services of the National Research & Education Network (NREN in the SDN environment. The proposed SDN routing computation (SRC application is designed and applied in a multi-domain network for the efficient use of resources, selection of the optimal path between the multi-domains and optimal establishment of end-to-end connections.
Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.
Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen
2017-02-01
Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.
Aircraft path planning for optimal imaging using dynamic cost functions
Christie, Gordon; Chaudhry, Haseeb; Kochersberger, Kevin
2015-05-01
Unmanned aircraft development has accelerated with recent technological improvements in sensing and communications, which has resulted in an "applications lag" for how these aircraft can best be utilized. The aircraft are becoming smaller, more maneuverable and have longer endurance to perform sensing and sampling missions, but operating them aggressively to exploit these capabilities has not been a primary focus in unmanned systems development. This paper addresses a means of aerial vehicle path planning to provide a realistic optimal path in acquiring imagery for structure from motion (SfM) reconstructions and performing radiation surveys. This method will allow SfM reconstructions to occur accurately and with minimal flight time so that the reconstructions can be executed efficiently. An assumption is made that we have 3D point cloud data available prior to the flight. A discrete set of scan lines are proposed for the given area that are scored based on visibility of the scene. Our approach finds a time-efficient path and calculates trajectories between scan lines and over obstacles encountered along those scan lines. Aircraft dynamics are incorporated into the path planning algorithm as dynamic cost functions to create optimal imaging paths in minimum time. Simulations of the path planning algorithm are shown for an urban environment. We also present our approach for image-based terrain mapping, which is able to efficiently perform a 3D reconstruction of a large area without the use of GPS data.
Energy Technology Data Exchange (ETDEWEB)
L. DOWELL
1999-08-01
The optimization of the configuration of communications and control networks is important for assuring the reliability and performance of the networks. This paper presents techniques for determining the optimal configuration for such a network in the presence of communication and connectivity constraints. reconfiguration to restore connectivity to a data-fusion network following the failure of a network component.
Cost Optimal Elastic Auto-Scaling in Cloud Infrastructure
Mukhopadhyay, S.; Sidhanta, S.; Ganguly, S.; Nemani, R. R.
2014-12-01
Today, elastic scaling is critical part of leveraging cloud. Elastic scaling refers to adding resources only when it is needed and deleting resources when not in use. Elastic scaling ensures compute/server resources are not over provisioned. Today, Amazon and Windows Azure are the only two platform provider that allow auto-scaling of cloud resources where servers are automatically added and deleted. However, these solution falls short of following key features: A) Requires explicit policy definition such server load and therefore lacks any predictive intelligence to make optimal decision; B) Does not decide on the right size of resource and thereby does not result in cost optimal resource pool. In a typical cloud deployment model, we consider two types of application scenario: A. Batch processing jobs → Hadoop/Big Data case B. Transactional applications → Any application that process continuous transactions (Requests/response) In reference of classical queuing model, we are trying to model a scenario where servers have a price and capacity (size) and system can add delete servers to maintain a certain queue length. Classical queueing models applies to scenario where number of servers are constant. So we cannot apply stationary system analysis in this case. We investigate the following questions 1. Can we define Job queue and use the metric to define such a queue to predict the resource requirement in a quasi-stationary way? Can we map that into an optimal sizing problem? 2. Do we need to get into a level of load (CPU/Data) on server level to characterize the size requirement? How do we learn that based on Job type?
Kim, U.; Parker, J.; Borden, R. C.
2014-12-01
In-situ chemical oxidation (ISCO) has been applied at many dense non-aqueous phase liquid (DNAPL) contaminated sites. A stirred reactor-type model was developed that considers DNAPL dissolution using a field-scale mass transfer function, instantaneous reaction of oxidant with aqueous and adsorbed contaminant and with readily oxidizable natural oxygen demand ("fast NOD"), and second-order kinetic reactions with "slow NOD." DNAPL dissolution enhancement as a function of oxidant concentration and inhibition due to manganese dioxide precipitation during permanganate injection are included in the model. The DNAPL source area is divided into multiple treatment zones with different areas, depths, and contaminant masses based on site characterization data. The performance model is coupled with a cost module that involves a set of unit costs representing specific fixed and operating costs. Monitoring of groundwater and/or soil concentrations in each treatment zone is employed to assess ISCO performance and make real-time decisions on oxidant reinjection or ISCO termination. Key ISCO design variables include the oxidant concentration to be injected, time to begin performance monitoring, groundwater and/or soil contaminant concentrations to trigger reinjection or terminate ISCO, number of monitoring wells or geoprobe locations per treatment zone, number of samples per sampling event and location, and monitoring frequency. Design variables for each treatment zone may be optimized to minimize expected cost over a set of Monte Carlo simulations that consider uncertainty in site parameters. The model is incorporated in the Stochastic Cost Optimization Toolkit (SCOToolkit) program, which couples the ISCO model with a dissolved plume transport model and with modules for other remediation strategies. An example problem is presented that illustrates design tradeoffs required to deal with characterization and monitoring uncertainty. Monitoring soil concentration changes during ISCO
Jakovetic, Dusan; Xavier, João; Moura, José M. F.
2011-08-01
We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.
Optimal network structure to induce the maximal small-world effect
International Nuclear Information System (INIS)
Zhang Zheng-Zhen; Xu Wen-Jun; Lin Jia-Ru; Zeng Shang-You
2014-01-01
In this paper, the general efficiency, which is the average of the global efficiency and the local efficiency, is defined to measure the communication efficiency of a network. The increasing ratio of the general efficiency of a small-world network relative to that of the corresponding regular network is used to measure the small-world effect quantitatively. The more considerable the small-world effect, the higher the general efficiency of a network with a certain cost is. It is shown that the small-world effect increases monotonically with the increase of the vertex number. The optimal rewiring probability to induce the best small-world effect is approximately 0.02 and the optimal average connection probability decreases monotonically with the increase of the vertex number. Therefore, the optimal network structure to induce the maximal small-world effect is the structure with the large vertex number (> 500), the small rewiring probability (≍ 0.02) and the small average connection probability (< 0.1). Many previous research results support our results. (interdisciplinary physics and related areas of science and technology)
Xu, Pengcheng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Liu, Jiufu; Zou, Ying; He, Ruimin
2017-12-01
Hydrometeorological data are needed for obtaining point and areal mean, quantifying the spatial variability of hydrometeorological variables, and calibration and verification of hydrometeorological models. Hydrometeorological networks are utilized to collect such data. Since data collection is expensive, it is essential to design an optimal network based on the minimal number of hydrometeorological stations in order to reduce costs. This study proposes a two-phase copula entropy- based multiobjective optimization approach that includes: (1) copula entropy-based directional information transfer (CDIT) for clustering the potential hydrometeorological gauges into several groups, and (2) multiobjective method for selecting the optimal combination of gauges for regionalized groups. Although entropy theory has been employed for network design before, the joint histogram method used for mutual information estimation has several limitations. The copula entropy-based mutual information (MI) estimation method is shown to be more effective for quantifying the uncertainty of redundant information than the joint histogram (JH) method. The effectiveness of this approach is verified by applying to one type of hydrometeorological gauge network, with the use of three model evaluation measures, including Nash-Sutcliffe Coefficient (NSC), arithmetic mean of the negative copula entropy (MNCE), and MNCE/NSC. Results indicate that the two-phase copula entropy-based multiobjective technique is capable of evaluating the performance of regional hydrometeorological networks and can enable decision makers to develop strategies for water resources management.
Optimal Investment Under Transaction Costs: A Threshold Rebalanced Portfolio Approach
Tunc, Sait; Donmez, Mehmet Ali; Kozat, Suleyman Serdar
2013-06-01
We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. We achieve this using "threshold rebalanced portfolios", where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. Our derivations can be readily extended to markets having more than two stocks, where these extensions are pointed out in the paper. As predicted from our derivations, we significantly improve the achieved wealth over portfolio selection algorithms from the literature on historical data sets.
Particle swarm optimization algorithm based low cost magnetometer calibration
Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.
2011-12-01
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments
Competitive game theoretic optimal routing in optical networks
Yassine, Abdulsalam; Kabranov, Ognian; Makrakis, Dimitrios
2002-09-01
Optical transport service providers need control and optimization strategies for wavelength management, network provisioning, restoration and protection, allowing them to define and deploy new services and maintain competitiveness. In this paper, we investigate a game theory based model for wavelength and flow assignment in multi wavelength optical networks, consisting of several backbone long-haul optical network transport service providers (TSPs) who are offering their services -in terms of bandwidth- to Internet service providers (ISPs). The ISPs act as brokers or agents between the TSP and end user. The agent (ISP) buys services (bandwidth) from the TSP. The TSPs compete among themselves to sell their services and maintain profitability. We present a case study, demonstrating the impact of different bandwidth broker demands on the supplier's profit and the price paid by the network broker.
Optimizing bulk data transfers using network measurements: A practical case
International Nuclear Information System (INIS)
Ciuffoletti, A; Merola, L; Palmieri, F; Russo, G; Pardi, S
2010-01-01
In modern Data Grid infrastructures, we increasingly face the problem of providing the running applications with fast and reliable access to large data volumes, often geographically distributed across the network. As a direct consequence, the concept of replication has been adopted by the grid community to increase data availability and maximize job throughput. To be really effective, such process has to be driven by specific optimization strategies that define when and where replicas should be created or deleted on a per-site basis, and which replicas a job should use. These strategies have to take into account the available network bandwidth as a primary resource, prior to any consideration about storage or processing power. We present a novel replica management service, integrated within the Gluedomains active network monitoring architecture, designed and implemented within the centralized collective middleware framework of the SCoPE project to provide network-aware transfer services for data intensive Grid applications.
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
Directory of Open Access Journals (Sweden)
M. Maharjan
2009-04-01
Full Text Available Urban flooding causes large economic losses, property damage and loss of lives. The impact of environmental changes, mainly urbanization and climatic change, leads to increased runoff and peak flows which the drainage system must be able to cope with to reduce potential damage and inconvenience. Allowing for detention storage to compliment the conveyance capacity of the drainage system network is one of the approaches to reduce urban floods. Contemporary practice is to design systems against stationary environmental forcings – including design rainfall, landuse, etc. Due to the rapid change in the climate- and the urban environment, this approach is no longer appropriate, and explicit consideration of gradual changes during the life-time of the drainage system is warranted. In this paper, a staged cost optimization tool based on the hydraulic performance of the drainage system is presented. A one dimensional hydraulic model is used for hydraulic evaluation of the network together with a genetic algorithm based optimization tool to determine optimal intervention timings and responses over the analysis period. The model was applied in a case study area in the city of Porto Alegre, Brazil. It was concluded that considerable financial savings and/or additional level of flood-safety can be achieved by approaching the design problem as a staged plan rather than one-off scheme.
Optimal multicasting in a multi-line-rate ethernet-over-WDM network
Harve, Shruthi; Batayneh, Marwan; Mukherjee, Biswanath
2009-11-01
Ethernet is the dominant transport technology for Local Area Networks. Efforts are now under way to use carrier-grade Ethernet in backbone networks of different service providers. With the advent of applications such as IPTV and Videoon- Demand, there is need for techniques to route multicast traffic over the Ethernet backbone networks. Here, we address the problem of Routing and Wavelength Assignment (RWA) of a set of multicast requests in a Multi-Line-Rate Ethernet backbone network with the objective of minimizing the cost of setting up the network, in terms of the Service Provider's Capital Expenditure (CAPEX). We present an Auxiliary Graph based heuristic algorithm that routes each multicast request on a light-tree structure, and assigns minimum cost wavelengths along the route. We compare the properties of the algorithm to the optimal solution given by a mathematical model formulated as an Integer Linear Program (ILP), and show that they compare very well. We also find that the algorithm is most cost-effective when the incoming requests are processed in descending order of their bandwidth requirements.
Video-on-demand network design and maintenance using fuzzy optimization.
Abadpour, Arash; Alfa, Attahiru Sule; Diamond, Jeff
2008-04-01
Video-on-demand (VoD) is the entertainment source that, in the future, will likely overtake regular television in many aspects. Although many companies have deployed working VoD services, some aspects of the VoD should still undergo further improvement in order for it to reach to the foreseen potentials. An important aspect of a VoD system is the underlying network in which it operates. According to the huge number of customers in this network, it should be carefully designed to fulfill certain performance criteria. This process should be capable of finding optimal locations for the nodes of the network as well as determining the content that should be cached in each one. While this problem is categorized in the general group of network optimization problems, its specific characteristics demand a new solution to be sought for it. In this paper, which is inspired by the successful use of fuzzy optimization in similar problems in other fields, a fuzzy objective function that is heuristically shown to minimize the communication cost in a VoD network is derived while also controlling the storage cost. Then, an iterative algorithm is proposed to find a locally optimal solution to the proposed objective function. Capitalizing on the unrepeatable tendency of the proposed algorithm, a heuristic method for picking a good solution from a bundle of solutions produced by the proposed algorithm is also suggested. This paper includes a formal statement of the problem and its mathematical analysis. In addition, different scenarios in which the proposed algorithm can be utilized are discussed.
Design and management of networks with fixed transportation costs
Lange, Jean-Charles
2010-01-01
Reusable packages are logistic items used for the shipments of goods from a producer to its customers and which, once the goods have been consumed by the customers, are to be returned to the producer. This doctoral thesis addresses the strategic design and the operational management of large independent reverse networks for their return flows when fixed transportation costs apply. At the strategic level, we identify the economic logic by which the allocation of the customers should be made...
Directory of Open Access Journals (Sweden)
Gustavo Callou
2014-01-01
Full Text Available Data centers have evolved dramatically in recent years, due to the advent of social networking services, e-commerce and cloud computing. The conflicting requirements are the high availability levels demanded against the low sustainability impact and cost values. The approaches that evaluate and optimize these requirements are essential to support designers of data center architectures. Our work aims to propose an integrated approach to estimate and optimize these issues with the support of the developed environment, Mercury. Mercury is a tool for dependability, performance and energy flow evaluation. The tool supports reliability block diagrams (RBD, stochastic Petri nets (SPNs, continuous-time Markov chains (CTMC and energy flow (EFM models. The EFM verifies the energy flow on data center architectures, taking into account the energy efficiency and power capacity that each device can provide (assuming power systems or extract (considering cooling components. The EFM also estimates the sustainability impact and cost issues of data center architectures. Additionally, a methodology is also considered to support the modeling, evaluation and optimization processes. Two case studies are presented to illustrate the adopted methodology on data center power systems.
Energy network dispatch optimization under emergency of local energy shortage
International Nuclear Information System (INIS)
Cai, Tianxing; Zhao, Chuanyu; Xu, Qiang
2012-01-01
The consequence of short-time energy shortage under extreme conditions, such as earthquake, tsunami, and hurricane, may cause local areas to suffer from delayed rescues, widespread power outages, tremendous economic losses, and even public safety threats. In such urgent events of local energy shortage, agile energy dispatching through an effective energy transportation network, targeting the minimum energy recovery time, should be a top priority. In this paper, a novel methodology is developed for energy network dispatch optimization under emergency of local energy shortage, which includes four stages of work. First, emergency-area-centered energy network needs to be characterized, where the capacity, quantity, and availability of various energy sources are determined. Second, the energy initial situation under emergency conditions needs to be identified. Then, the energy dispatch optimization is conducted based on a developed MILP (mixed-integer linear programming) model in the third stage. Finally, the sensitivity of the minimum dispatch time with respect to uncertainty parameters is characterized by partitioning the entire space of uncertainty parameters into multiple subspaces. The efficacy of the developed methodology is demonstrated via a case study with in-depth discussions. -- Highlights: ► Address the energy network dispatch problem under emergency of local energy shortage. ► Minimize the energy restoration time for the entire energy network under emergency events. ► Develop a new MILP model and a sensitivity analysis method with respect to uncertainties.
Bridge Management Strategy Based on Extreme User Costs for Bridge Network Condition
Directory of Open Access Journals (Sweden)
Ladislaus Lwambuka
2014-01-01
Full Text Available This paper presents a practical approach for prioritization of bridge maintenance within a given bridge network. The maintenance prioritization is formulated as a multiobjective optimization problem where the simultaneous satisfaction of several conflicting objectives includes minimization of maintenance costs, maximization of bridge deck condition, and minimization of traffic disruption and associated user costs. The prevalence of user cost during maintenance period is twofold; the first case refers to the period of dry season where normally the traffic flow is diverted to alternative routes usually resurfaced to regain traffic access. The second prevalence refers to the absence of alternative routes which is often the case in the least developed countries; in this case the user cost referred to results from the waiting time while the traffic flow is put on hold awaiting accomplishment of the maintenance activity. This paper deals with the second scenario of traffic closure in the absence of alternative diversion routes which in essence results in extreme user cost. The paper shows that the multiobjective optimization approach remains valid for extreme cases of user costs in the absence of detour roads as often is the scenario in countries with extreme poor road infrastructure.
Gao, Ya; Cheng, Wenchi; Zhang, Hailin
2017-08-23
Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks.
[Construction and optimization of ecological network for nature reserves in Fujian Province, China].
Gu, Fan; Huang, Yi Xiong; Chen, Chuan Ming; Cheng, Dong Liang; Guo, Jia Lei
2017-03-18
The nature reserve is very important to biodiversity maintenance. However, due to the urbanization, the nature reserve has been fragmented with reduction in area, leading to the loss of species diversity. Establishing ecological network can effectively connect the fragmented habitats and plays an important role in species conversation. In this paper, based on deciding habitat patches and the landscape cost surface in ArcGIS, a minimum cumulative resistance model was used to simulate the potential ecological network of Fujian provincial nature reserves. The connectivity and importance of network were analyzed and evaluated based on comparison of connectivity indices (including the integral index of connectivity and probability of connectivity) and gravity model both before and after the potential ecological network construction. The optimum ecological network optimization measures were proposed. The result demonstrated that woodlands, grasslands and wetlands together made up the important part of the nature reserve ecological network. The habitats with large area had a higher degree of importance in the network. After constructing the network, the connectivity level was significantly improved. Although interaction strength between different patches va-ried greatly, the corridors between patches with large interaction were very important. The research could provide scientific reference and basis for nature protection and planning in Fujian Province.
Optimizing Markovian modeling of chaotic systems with recurrent neural networks
International Nuclear Information System (INIS)
Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de
2008-01-01
In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included
Implementing size-optimal discrete neural networks require analog circuitry
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.
1998-12-01
This paper starts by overviewing results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions the authors show that implementing Boolean functions can be done using neurons having an identity transfer function. Because in this case the size of the network is minimized, it follows that size-optimal solutions for implementing Boolean functions can be obtained using analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Optimization of TTEthernet Networks to Support Best-Effort Traffic
DEFF Research Database (Denmark)
Tamas-Selicean, Domitian; Pop, Paul
2014-01-01
This paper focuses on the optimization of the TTEthernet communication protocol, which offers three traffic classes: time-triggered (TT), sent according to static schedules, rate-constrained (RC) that has bounded end-to-end latency, and best-effort (BE), the classic Ethernet traffic, with no timing...... guarantees. In our earlier work we have proposed an optimization approach named DOTTS that performs the routing, scheduling and packing / fragmenting of TT and RC messages, such that the TT and RC traffic is schedulable. Although backwards compatibility with classic Ethernet networks is one of TTEthernet...
Optimal synthesis of a heat-exchanger network
Energy Technology Data Exchange (ETDEWEB)
Hamed, O A; Aly, S [University of United Arab Emirates, Al-Ain (United Arab Emirates). Faculty of Engineering
1991-01-01
Thermodynamic, heat transfer and economic concepts influencing the synthesis of a heat-exchanger network (HEN) coupled to a crude fractionation unit are examined. The impact of the variation of the minimum temperature approach on energy and capital targets is studied using recent developments in pinch technology. The optimal pinch approach temperature has been determined using the 'supertargeting' concept where proper trade-off between energy and capital targets is observed prior to design. A heuristic evolutionary approach has then been used for the generation of the optimal HEN. (author).
Directory of Open Access Journals (Sweden)
Jing Li
2017-01-01
Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.
Heat exchanger network retrofit optimization involving heat transfer enhancement
International Nuclear Information System (INIS)
Wang Yufei; Smith, Robin; Kim, Jin-Kuk
2012-01-01
Heat exchanger network retrofit plays an important role in energy saving in process industry. Many design methods for the retrofit of heat exchanger networks have been proposed during the last three decades. Conventional retrofit methods rely heavily on topology modifications which often result in a long retrofit duration and high initial costs. Moreover, the addition of extra surface area to the heat exchanger can prove difficult due to topology, safety and downtime constraints. Both of these problems can be avoided through the use of heat transfer enhancement in heat exchanger network retrofit. This paper presents a novel design approach to solve heat exchanger network retrofit problems based on heat transfer enhancement. An optimisation method based on simulated annealing has been developed to find the appropriate heat exchangers to be enhanced and to calculate the level of enhancement required. The physical insight of enhanced exchangers is also analysed. The new methodology allows several possible retrofit strategies using different retrofit methods be determined. Comparison of these retrofit strategies demonstrates that retrofit modification duration and payback time are reduced when heat transfer enhancement is utilised. Heat transfer enhancement can be also used as a substitute for increased heat exchanger network surface area to reduce retrofit investment costs.
Designing optimal bioethanol networks with purification for integrated biorefineries
International Nuclear Information System (INIS)
Shenoy, Akshay U.; Shenoy, Uday V.
2014-01-01
Highlights: • An analytical method is devised for bioethanol network integration with purification. • Minimum fresh bioethanol flow and pinch are found by the Unified Targeting Algorithm. • Optimal bioethanol networks are then synthesized by the Nearest Neighbors Algorithm. • Continuous targets and networks are developed over the purifier inlet flowrate range. • Case study of a biorefinery producing bioethanol from wheat shows large savings. - Abstract: Bioethanol networks with purification for processing pathways in integrated biorefineries are targeted and designed in this work by an analytical approach not requiring graphical constructions. The approach is based on six fundamental equations involving eight variables: two balance equations for the stream flowrate and the bioethanol load over the total network system; one equation for the above-pinch bioethanol load being picked up by the minimum fresh resource and the purified stream; and three equations for the purification unit. A solution strategy is devised by specifying the two variables associated with the purifier inlet stream. Importantly, continuous targeting is then possible over the entire purifier inlet flowrate range on deriving elegant formulae for the remaining six variables. The Unified Targeting Algorithm (UTA) is utilized to establish the minimum fresh bioethanol resource flowrate and identify the pinch purity. The fresh bioethanol resource flowrate target is shown to decrease linearly with purifier inlet flowrate provided the pinch is held by the same point. The Nearest Neighbors Algorithm (NNA) is used to methodically synthesize optimal networks matching bioethanol demands and sources. A case study of a biorefinery producing bioethanol from wheat with arabinoxylan (AX) coproduction is presented. It illustrates the versatility of the approach in generating superior practical designs with up to nearly 94% savings for integrated bioethanol networks, both with and without process
Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Ferrandez, S. M.; Harbison, T.; Weber, T.; Sturges, R.; Rich, R.
2016-07-01
The purpose of this paper is to investigate the effectiveness of implementing unmanned aerial delivery vehicles in delivery networks. We investigate the notion of the reduced overall delivery time, energy, and costs for a truck-drone network by comparing the in-tandem system with a stand-alone delivery effort. The objectives are (1) to investigate the time, energy, and costs associated to a truck-drone delivery network compared to standalone truck or drone, (2) to propose an optimization algorithm that determines the optimal number of launch sites and locations given delivery requirements, and drones per truck, (3) to develop mathematical formulations for closed form estimations for the optimal number of launch locations, optimal total time, as well as the associated cost for the system. The design of the algorithm herein computes the minimal time of delivery utilizing K-means clustering to find launch locations, as well as a genetic algorithm to solve the truck route as a traveling salesmen problem (TSP). The optimal solution is determined by finding the minimum cost associated to the parabolic convex cost function. The optimal min-cost is determined by finding the most efficient launch locations using K-means algorithms to determine launch locations and a genetic algorithm to determine truck route between those launch locations. Results show improvements with in-tandem delivery efforts as opposed to standalone systems. Further, multiple drones per truck are more optimal and contribute to savings in both energy and time. For this, we sampled various initialization variables to derive closed form mathematical solutions for the problem. Ultimately, this provides the necessary analysis of an integrated truck-drone delivery system which could be implemented by a company in order to maximize deliveries while minimizing time and energy. Closed-form mathematical solutions can be used as close estimators for final costs and time. (Author)
Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm
Directory of Open Access Journals (Sweden)
Sergio Mourelo Ferrandez
2016-04-01
Full Text Available Purpose: The purpose of this paper is to investigate the effectiveness of implementing unmanned aerial delivery vehicles in delivery networks. We investigate the notion of the reduced overall delivery time, energy, and costs for a truck-drone network by comparing the in-tandem system with a stand-alone delivery effort. The objectives are (1 to investigate the time, energy, and costs associated to a truck-drone delivery network compared to standalone truck or drone, (2 to propose an optimization algorithm that determines the optimal number of launch sites and locations given delivery requirements, and drones per truck, (3 to develop mathematical formulations for closed form estimations for the optimal number of launch locations, optimal total time, as well as the associated cost for the system. Design/methodology/approach: The design of the algorithm herein computes the minimal time of delivery utilizing K-means clustering to find launch locations, as well as a genetic algorithm to solve the truck route as a traveling salesmen problem (TSP. The optimal solution is determined by finding the minimum cost associated to the parabolic convex cost function. The optimal min-cost is determined by finding the most efficient launch locations using K-means algorithms to determine launch locations and a genetic algorithm to determine truck route between those launch locations. Findings: Results show improvements with in-tandem delivery efforts as opposed to standalone systems. Further, multiple drones per truck are more optimal and contribute to savings in both energy and time. For this, we sampled various initialization variables to derive closed form mathematical solutions for the problem. Originality/value: Ultimately, this provides the necessary analysis of an integrated truck-drone delivery system which could be implemented by a company in order to maximize deliveries while minimizing time and energy. Closed-form mathematical solutions can be used as
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
2013-01-01
Finding a location for a new facility such that the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions...... that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. The query returns the road segment(s) with the highest score. To achieve low latency, it is essential to prune the very large search space. We propose two algorithms...
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
Finding a location for a new facility such that the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions...... that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. The query returns the road segment(s) with the highest score. To achieve low latency, it is essential to prune the very large search space. We propose two algorithms...
2013-11-04
... (NOA) for Strategic Network Optimization (SNO) Environmental Assessment Finding of No Significant... Network Optimization (SNO) Environmental Assessment (EA) Finding of No Significant Impact (FONSI). SUMMARY... interpreted comprehensively to include the natural and physical environment and the relationship of people...
2014-12-01
The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications : bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impen...
PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks
Directory of Open Access Journals (Sweden)
Mansour Sheikhan
2012-06-01
Full Text Available Mobile ad-hoc network (MANET is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN which its parameters are optimized by particle swarm optimization (PSO algorithm is proposed as multipath routing algorithm. Link expiration time (LET between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in singlephase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA in terms of the path set reliability and number of paths in the set.
A measure theoretic approach to traffic flow optimization on networks
Cacace, Simone; Camilli, Fabio; De Maio, Raul; Tosin, Andrea
2018-01-01
We consider a class of optimal control problems for measure-valued nonlinear transport equations describing traffic flow problems on networks. The objective isto minimise/maximise macroscopic quantities, such as traffic volume or average speed,controlling few agents, for example smart traffic lights and automated cars. The measuretheoretic approach allows to study in a same setting local and nonlocal drivers interactionsand to consider the control variables as additional measures interacting ...
PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN
Directory of Open Access Journals (Sweden)
Arie Rachmad Syulistyo
2016-02-01
Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN. The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.
Extending Resolution of Fault Slip With Geodetic Networks Through Optimal Network Design
Sathiakumar, Sharadha; Barbot, Sylvain Denis; Agram, Piyush
2017-12-01
Geodetic networks consisting of high precision and high rate Global Navigation Satellite Systems (GNSS) stations continuously monitor seismically active regions of the world. These networks measure surface displacements and the amount of geodetic strain accumulated in the region and give insight into the seismic potential. SuGar (Sumatra GPS Array) in Sumatra, GEONET (GNSS Earth Observation Network System) in Japan, and PBO (Plate Boundary Observatory) in California are some examples of established networks around the world that are constantly expanding with the addition of new stations to improve the quality of measurements. However, installing new stations to existing networks is tedious and expensive. Therefore, it is important to choose suitable locations for new stations to increase the precision obtained in measuring the geophysical parameters of interest. Here we describe a methodology to design optimal geodetic networks that augment the existing system and use it to investigate seismo-tectonics at convergent and transform boundaries considering land-based and seafloor geodesy. The proposed network design optimization would be pivotal to better understand seismic and tsunami hazards around the world. Land-based and seafloor networks can monitor fault slip around subduction zones with significant resolution, but transform faults are more challenging to monitor due to their near-vertical geometry.
Directory of Open Access Journals (Sweden)
Bruno M. Brentan
2017-01-01
Full Text Available Management of large water distribution systems can be improved by dividing their networks into so-called district metered areas (DMAs. However, such divisions must be based on appropriated technical criteria. Considering the importance of deeply understanding the relationship between DMA creation and these criteria, this work proposes a performance analysis of DMA generation that takes into account such indicators as resilience index, demand similarity, pressure uniformity, water age (and thus water quality, solution implantation costs, and electrical consumption. To cope with the complexity of the problem, suitable mathematical techniques are proposed in this paper. We use a social community detection technique to define the sectors, and then a multilevel particle swarm optimization approach is applied to find the optimal placement and operating point of the necessary devices. The results obtained by implementing the methodology in a real water supply network show its validity and the meaningful influence on the final result of, especially, elevation and pipe length.
Energy Technology Data Exchange (ETDEWEB)
Ilo, Albana [Siemens AG, Wien (Austria); Schaffer, Walter; Rieder, Thomas [Salzburg Netz GmbH, Salzburg (Austria); Dzafic, Izudin [Siemens AG, Nuernberg (Germany)
2012-07-01
A holistic approach of power system control that includes all voltage levels from highest to low voltage is provided. The power grid is conceived as a supply chain. The medium voltage grid represents the central link. The implemented automatic voltage control and the dynamic operation optimization are based on Distribution System State Estimator (DSSE) and Volt/Var Control (VVC) applications. The last one realizes the dynamic optimization of distribution network combining the reactive power of the decentralized generation, capacitors and voltage set points of on-line tap changers. Application of this method has shown, that by using the dynamic voltage control the grid can be stable operated near the low voltage limit. The conservation voltage reduction can be applied in real time. Furthermore the integration of the decentralized generation is facilitated with minimal costs. Until now in this regard required network expansion can be prevented or delayed. (orig.)
Hamdi, Hadiwardoyo, Sigit P.; Correia, A. Gomes; Pereira, Paulo
2017-06-01
A road network requires timely maintenance to keep the road surface in good condition onward better services to improve accessibility and mobility. Strategies and maintenance techniques must be chosen in order to maximize road service level through cost-effective interventions. This approach requires an updated database, which the road network in Indonesia is supported by a manual and visual survey, also using NAASRA profiler. Furthermore, in this paper, the deterministic model of deterioration was used. This optimization model uses life cycle cost analysis (LCCA), applied in an integrated manner, using IRI indicator, and allows determining the priority of treatment, type of treatment and its relation to the cost. The purpose of this paper was focussed on the aspects of road maintenance management, i.e., maintenance optimization models for different levels of traffic and various initial of road distress conditions on the national road network in Indonesia. The implementation of Integrated Road Management System (IRMS) can provide a solution to the problem of cost constraints in the maintenance of the national road network. The results from this study found that as the lowest as agency cost, it will affect the increasing of user cost. With the achievement of the target plan scenario Pl000 with initial value IRI 2, it was found that the routine management throughout the year and in early reconstruction and periodic maintenance with a 30 mm thick overlay, will simultaneously provide a higher net benefit value and has the lowest total cost of transportation.
Self-Configuration and Self-Optimization Process in Heterogeneous Wireless Networks
Directory of Open Access Journals (Sweden)
Eduardo Camponogara
2010-12-01
Full Text Available Self-organization in Wireless Mesh Networks (WMN is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR and the ad hoc on demand distance vector (AODV routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network’s scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed.
Self-Configuration and Self-Optimization Process in Heterogeneous Wireless Networks
Guardalben, Lucas; Villalba, Luis Javier García; Buiati, Fábio; Sobral, João Bosco Mangueira; Camponogara, Eduardo
2011-01-01
Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network’s scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed. PMID:22346584
Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles
Directory of Open Access Journals (Sweden)
Nan Jiang
2017-01-01
Full Text Available A signal design problem is studied for efficiently managing autonomous vehicles (AVs and regular vehicles (RVs simultaneously in transportation networks. AVs and RVs move on separate lanes and two types of vehicles share the green times at the same intersections. The signal design problem is formulated as a bilevel program. The lower-level model describes a mixed equilibrium where autonomous vehicles follow the Cournot-Nash (CN principle and RVs follow the user equilibrium (UE principle. In the upper-level model, signal timings are optimized at signalized intersections to allocate appropriate green times to both autonomous and RVs to minimize system travel cost. The sensitivity analysis based method is used to solve the bilevel optimization model. Various signal control strategies are evaluated through numerical examples and some insightful findings are obtained. It was found that the number of phases at intersections should be reduced for the optimal control of the AVs and RVs in the mixed networks. More importantly, incorporating AVs into the transportation network would improve the system performance due to the value of AV technologies in reducing random delays at intersections. Meanwhile, travelers prefer to choose AVs when the networks turn to be congested.
Energy Technology Data Exchange (ETDEWEB)
Faria, Debora C.; Souza, Selene M.A. Guelli Ulson de; Souza, Antonio A. Ulson de [Universidade Federal de Santa Catarina (UFSC), Florianopolis, SC (Brazil)
2004-07-01
The petroleum refineries have shown high water's consuming that creates high costs and damages the hydric resources. However, the industrial sector, especially the petroleum industry, has been looking for alternatives that minimizing the impact caused by to use these natural resources. Currently, methodologies of controlling the pollution on the source have been appearing how a strong tendency and the reuse and/or recycle of wastewater can be emphasized. The optimization by mathematical programming, together with engineering know-how, is one of the great tendency in process integration technology developed. The present work presents one optimization mathematical model that objectifies to reduce the water's consuming and/or operational costs. The model is based in chemical species and mass conservation equation. This work presents the solution of one case found in literature that broach one petroleum refinery's network of water. This network is composed of six operations and three regenerative processes, and there are four keys contaminants. The water's consuming was minimized first and next the minimum cost in the minimum consume was broached. The results found were enough satisfactory and presented reductions up to 76% in the water consume and approximately 65% in the operational costs. (author)
Optimal residential smart appliances scheduling considering distribution network constraints
Directory of Open Access Journals (Sweden)
Yu-Ree Kim
2016-01-01
Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.
Bi-objective Optimization of the Water Distribution Networks (Case Study: Sahand City
Directory of Open Access Journals (Sweden)
Ali Nikjoofar
2012-12-01
Full Text Available To design an urban water network in addition to minimizing the cost, improving the water pressure is very important. Then in this paper a bi-objective optimization model for the new city of Sahand in Northwestern Iran is developed. Due to its non-linearity and the huge number of variables, the genetic algorithm has been utilized to solve it. Several Pareto solutions have been obtained and then based on the game theory approach (the area monotonic solution, the most efficient point was provided. The solution is simulated by the WaterGems software and the elements of the network are designed. This optimum solution shows a decrease of 13% in total cost in addition to the improved water pressure.
Directory of Open Access Journals (Sweden)
Daniel Jorge Caetano
2017-12-01
Full Text Available The problem of airline flight network optimization can be split into subproblems such as Schedule Generation (SG and Fleet Assignment (FA, solved in consecutive steps or in an integrated way, usually based on monetary costs and revenue forecasts. A linear programming model to solve SG and FA in an integrated way is presented, but with an alternative approach based on transport momentum and aircraft load factor. This alternative approach relies on demand forecast and allows obtaining solutions considering minimum average load factors. Results of the proposed model applications to instances of a regional Brazilian airline are presented. The comparison of the schedules generated by the proposed approach against those obtained by applying a model based on monetary costs and revenue forecasts demonstrates the validity of this alternative approach for airlines network planning.
TRU Waste Management Program. Cost/schedule optimization analysis
International Nuclear Information System (INIS)
Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.; Hastings, G.A.
1985-10-01
This Current Year Work Plan presents in detail a description of the activities to be performed by the Joint Integration Office Rockwell International (JIO/RI) during FY86. It breaks down the activities into two major work areas: Program Management and Program Analysis. Program Management is performed by the JIO/RI by providing technical planning and guidance for the development of advanced TRU waste management capabilities. This includes equipment/facility design, engineering, construction, and operations. These functions are integrated to allow transition from interim storage to final disposition. JIO/RI tasks include program requirements identification, long-range technical planning, budget development, program planning document preparation, task guidance development, task monitoring, task progress information gathering and reporting to DOE, interfacing with other agencies and DOE lead programs, integrating public involvement with program efforts, and preparation of reports for DOE detailing program status. Program Analysis is performed by the JIO/RI to support identification and assessment of alternatives, and development of long-term TRU waste program capabilities. These analyses include short-term analyses in response to DOE information requests, along with performing an RH Cost/Schedule Optimization report. Systems models will be developed, updated, and upgraded as needed to enhance JIO/RI's capability to evaluate the adequacy of program efforts in various fields. A TRU program data base will be maintained and updated to provide DOE with timely responses to inventory related questions
Optimized Energy Procurement for Cellular Networks with Uncertain Renewable Energy Generation
Rached, Nadhir B.
2017-02-07
Renewable energy (RE) is an emerging solution for reducing carbon dioxide (CO2) emissions from cellular networks. One of the challenges of using RE sources is to handle its inherent uncertainty. In this paper, a RE powered cellular network is investigated. For a one-day operation cycle, the cellular network aims to reduce energy procurement costs from the smart grid by optimizing the amounts of energy procured from their locally deployed RE sources as well as from the smart grid. In addition to that, it aims to determine the extra amount of energy to be sold to the electrical grid at each time period. Chance constrained optimization is first proposed to deal with the randomness in the RE generation. Then, to make the optimization problem tractable, two well- know convex approximation methods, namely; Chernoff and Chebyshev based-approaches, are analyzed in details. Numerical results investigate the optimized energy procurement for various daily scenarios and compare between the performances of the employed convex approximation approaches.
Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions
Mahayni, Malek A.
2011-01-01
developed to solve the optimal paths problem with different kinds of graphs. An algorithm that solves the problem of paths’ optimization in directed graphs relative to different cost functions is described in [1]. It follows an approach extended from
Optimization of operation cycles in BWRs using neural networks
International Nuclear Information System (INIS)
Ortiz S, J. J.; Castillo, A.; Alejandro P, D.
2011-11-01
The first results of a system for the optimization of operation cycles in boiling water reactors by means of a multi state recurrent neural network are present in this work. The neural network finds the best combination of fuel cells; fuel reloads and control bars patterns previously designed, according to an energy function that qualifies the performance of the three partial solutions for the solution of the whole problem. The partial solutions are designed by means of optimization systems non couple among them and that can use any optimization technique. The phase of the fuel axial design is not made and the size of the axial areas is fixed during the optimization process. The methodology was applied to design a balance cycle of 18 months for the reactors of the nuclear power station of Laguna Verde. The results show that is possible to find combinations of partial solutions that in set represent good solutions to the complete design problem of an operation cycle of a nuclear reactor. The results are compared with others obtained previously by other techniques. This system was developed in platform Li nux and programmed in Fortran 95 taking advantage of the 8 nuclei of a work station Dell Precision T7400. (Author)
Robustness and Optimization of Complex Networks : Reconstructability, Algorithms and Modeling
Liu, D.
2013-01-01
The infrastructure networks, including the Internet, telecommunication networks, electrical power grids, transportation networks (road, railway, waterway, and airway networks), gas networks and water networks, are becoming more and more complex. The complex infrastructure networks are crucial to our
Using neural networks to speed up optimization algorithms
Bazan, M
2000-01-01
The paper presents the application of radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables. (13 refs).
Nonlinear Non-convex Optimization of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Kallesøe, Carsten; Leth, John-Josef
2013-01-01
Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption in p....... They can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users. The results in this paper show that the power consumption of the pumps is reduced.......Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption...
Pricing Resources in LTE Networks through Multiobjective Optimization
Lai, Yung-Liang; Jiang, Jehn-Ruey
2014-01-01
The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution. PMID:24526889
Pricing resources in LTE networks through multiobjective optimization.
Lai, Yung-Liang; Jiang, Jehn-Ruey
2014-01-01
The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.
Pricing Resources in LTE Networks through Multiobjective Optimization
Directory of Open Access Journals (Sweden)
Yung-Liang Lai
2014-01-01
Full Text Available The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1 maximizing operator profit and (2 maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.
Optimal design of mixed-media packet-switching networks - Routing and capacity assignment
Huynh, D.; Kuo, F. F.; Kobayashi, H.
1977-01-01
This paper considers a mixed-media packet-switched computer communication network which consists of a low-delay terrestrial store-and-forward subnet combined with a low-cost high-bandwidth satellite subnet. We show how to route traffic via ground and/or satellite links by means of static, deterministic procedures and assign capacities to channels subject to a given linear cost such that the network average delay is minimized. Two operational schemes for this network model are investigated: one is a scheme in which the satellite channel is used as a slotted ALOHA channel; the other is a new multiaccess scheme we propose in which whenever a channel collision occurs, retransmission of the involved packets will route through ground links to their destinations. The performance of both schemes is evaluated and compared in terms of cost and average packet delay tradeoffs for some examples. The results offer guidelines for the design and optimal utilization of mixed-media networks.
Converged Wireless Networking and Optimization for Next Generation Services
Directory of Open Access Journals (Sweden)
J. Rodriguez
2010-01-01
Full Text Available The Next Generation Network (NGN vision is tending towards the convergence of internet and mobile services providing the impetus for new market opportunities in combining the appealing services of internet with the roaming capability of mobile networks. However, this convergence does not go far enough, and with the emergence of new coexistence scenarios, there is a clear need to evolve the current architecture to provide cost-effective end-to-end communication. The LOOP project, a EUREKA-CELTIC driven initiative, is one piece in the jigsaw by helping European industry to sustain a leading role in telecommunications and manufacturing of high-value products and machinery by delivering pioneering converged wireless networking solutions that can be successfully demonstrated. This paper provides an overview of the LOOP project and the key achievements that have been tunneled into first prototypes for showcasing next generation services for operators and process manufacturers.
de Jong, S.A.|info:eu-repo/dai/nl/41200836X; Hoefnagels, E.T.A.|info:eu-repo/dai/nl/313935998; Wetterlund, Elisabeth; Pettersson, Karin; Faaij, André; Junginger, H.M.|info:eu-repo/dai/nl/202130703
2017-01-01
This study uses a geographically-explicit cost optimization model to analyze the impact of and interrelation between four cost reduction strategies for biofuel production: economies of scale, intermodal transport, integration with existing industries, and distributed supply chain configurations
Fu, Chun; Shuai, Zhenzhen
2015-01-01
Purpose: By studying the case of a Changsha engineering machinery manufacturing firm, this paper aims to find out the optimization tactics to reduce enterprise’s logistics operational cost. Design/methodology/approach: This paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of social firm network and simulates the model based on system dynamics. Findings: It concludes that applying system dynamics in the research o...
Liu, Qingshan; Guo, Zhishan; Wang, Jun
2012-02-01
In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Haibo Zhang
2016-08-01
Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.
Robust Optimization for Time-Cost Tradeoff Problem in Construction Projects
Li, Ming; Wu, Guangdong
2014-01-01
Construction projects are generally subject to uncertainty, which influences the realization of time-cost tradeoff in project management. This paper addresses a time-cost tradeoff problem under uncertainty, in which activities in projects can be executed in different construction modes corresponding to specified time and cost with interval uncertainty. Based on multiobjective robust optimization method, a robust optimization model for time-cost tradeoff problem is developed. In order to illus...
A low complexity method for the optimization of network path length in spatially embedded networks
International Nuclear Information System (INIS)
Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li; Ming, Yong; Chen, Sheng-Yong; Wang, Wan-Liang
2014-01-01
The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. (paper)
International Nuclear Information System (INIS)
Maroufmashat, Azadeh; Elkamel, Ali; Fowler, Michael; Sattari, Sourena; Roshandel, Ramin; Hajimiragha, Amir; Walker, Sean; Entchev, Evgueniy
2015-01-01
Energy hubs that incorporate a variety of energy generation and energy transformation technologies can be used to provide the energy storage needed to enable the efficient operation of a ‘smart energy network’. When these hubs are combined as a network and allowed to exchange energy, they create efficiency advantages in both financial and environmental performance. Further, the interconnectedness of the energy network design provides an added layer of reliability. In this paper, a complex network of energy hubs is modeled and optimized under different scenarios to examine both the financial viability and potential reduction of greenhouse gas emissions. Two case studies consisting of two and three energy hubs within a network are considered. The modeling Scenarios vary according to the consideration of distributed energy systems and energy interaction between energy hubs. In the case of a network of two energy hubs, there is no significant economic or emissions benefit with only a 0.5% reduction in total cost and 3% reduction in CO_2 emission. In the case of a network of three energy hubs, there is a significant economic benefit ranging from 11% to 29%, and 11% emission reduction benefit, as well as a 13% reduction in natural gas consumption. - Highlights: • The generic form of the modified energy hub concept with network model is presented. • Two case studies are presented to demonstrate the benefits of energy hub network. • Distributed energy is shown to provide economic and environmental advantages. • Multi criteria optimization of the economic and environmental performance is done.
Remote Autonomous Sensor Networks: A Study in Redundancy and Life Cycle Costs
Ahlrichs, M.; Dotson, A.; Cenek, M.
2017-12-01
The remote nature of the United States and Canada border and their extreme seasonal shifts has made monitoring much of the area impossible using conventional monitoring techniques. Currently, the United States has large gaps in its ability to detect movement on an as-needed-basis in remote areas. The proposed autonomous sensor network aims to meet that need by developing a product that is low cost, robust, and can be deployed on an as-needed-basis for short term monitoring events. This is accomplished by identifying radio frequency disturbance and acoustic disturbance. This project aims to validate the proposed design and offer optimization strategies by conducting a redundancy model as well as performing a Life Cycle Assessment (LCA). The model will incorporate topological, meteorological, and land cover datasets to estimate sensor loss over a three-month period, ensuring that the remaining network does not have significant gaps in coverage which preclude being able to receive and transmit data. The LCA will investigate the materials used to create the sensor to generate an estimate of the total environmental energy that is utilized to create the network and offer alternative materials and distribution methods that can lower this cost. This platform can function as a stand-alone monitoring network or provide additional spatial and temporal resolution to existing monitoring networks. This study aims to create the framework to determine if a sensor's design and distribution is appropriate for the target environment. The incorporation of a LCA will seek to answer if the data a proposed sensor network will collect outweighs the environmental damage that will result from its deployment. Furthermore, as the arctic continues to thaw and economic development grows, the methodology described in paper will function as a guidance document to ensure that future sensor networks have a minimal impact on these pristine areas.
Social Optimization and Pricing Policy in Cognitive Radio Networks with an Energy Saving Strategy
Directory of Open Access Journals (Sweden)
Shunfu Jin
2016-01-01
Full Text Available The rapid growth of wireless application results in an increase in demand for spectrum resource and communication energy. In this paper, we firstly introduce a novel energy saving strategy in cognitive radio networks (CRNs and then propose an appropriate pricing policy for secondary user (SU packets. We analyze the behavior of data packets in a discrete-time single-server priority queue under multiple-vacation discipline. With the help of a Quasi-Birth-Death (QBD process model, we obtain the joint distribution for the number of SU packets and the state of base station (BS via the Matrix-Geometric Solution method. We assess the average latency of SU packets and the energy saving ratio of system. According to a natural reward-cost structure, we study the individually optimal behavior and the socially optimal behavior of the energy saving strategy and use an optimization algorithm based on standard particle swarm optimization (SPSO method to search the socially optimal arrival rate of SU packets. By comparing the individually optimal behavior and the socially optimal behavior, we impose an appropriate admission fee to SU packets. Finally, we present numerical results to show the impacts of system parameters on the system performance and the pricing policy.
Directory of Open Access Journals (Sweden)
Yong Wang
2018-04-01
Full Text Available The formation of a cooperative alliance is an effective means of approaching the vehicle routing optimization in two-echelon reverse logistics networks. Cooperative mechanisms can contribute to avoiding the inefficient assignment of resources for the recycling logistics operations and reducing long distance transportation. With regard to the relatively low performance of waste collection, this paper proposes a three-phase methodology to properly address the corresponding vehicle routing problem on two echelons. First, a bi-objective programming model is established to minimize the total cost and the number of vehicles considering semitrailers and vehicles sharing. Furthermore, the Clarke–Wright (CW savings method and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II are combined to design a hybrid routing optimization heuristic, which is denoted CW_NSGA-II. Routes on the first and second echelons are obtained on the basis of sub-optimal solutions provided by CW algorithm. Compared to other intelligent algorithms, CW_NSGA-II reduces the complexity of the multi-objective solutions search and mostly converges to optimality. The profit generated by cooperation among retail stores and the recycling hub in the reverse logistics network is fairly and reasonably distributed to the participants by applying the Minimum Costs-Remaining Savings (MCRS method. Finally, an empirical study in Chengdu City, China, reveals the superiority of CW_NSGA over the multi-objective particle swarm optimization and the multi objective genetic algorithms in terms of solutions quality and convergence. Meanwhile, the comparison of MCRS method with the Shapley value model, equal profit method and cost gap allocation proves that MCRS method is more conducive to the stability of the cooperative alliance. In general, the implementation of cooperation in the optimization of the reverse logistics network effectively leads to the sustainable development of urban and sub
Directory of Open Access Journals (Sweden)
Nadjla Hariri
2013-03-01
Full Text Available This study aimed to determine the status of Persian professional web social networks' features and provide a suitable solution for optimization of these networks in Iran. The research methods were library research and evaluative method, and study population consisted of 10 Persian professional web social networks. In this study, for data collection, a check list of social networks important tools and features was used. According to the results, “Cloob”, “IR Experts” and “Doreh” were the most compatible networks with the criteria of social networks. Finally, some solutions were presented for optimization of capabilities of Persian professional web social networks.
How should fixed costs in the network be covered?
International Nuclear Information System (INIS)
1999-01-01
The report examines how tariffs that are only meant to cover costs in the transmission network should be formulated. Should the tariffs be based on power figures, such as for instance installed production capacity, or on an energy figure such as total annual energy production? Tariffs based on the producers' installed production capacity will in the long run cause the prises to rise under peak loads while tariffs based on generated energy elevate the consumer price by a small amount for all load segments. Thus, tariffs based on installed power capacity cause greater price distortion and greater socio-economic losses than energy tariffs. There are good arguments that fixed costs should mainly be paid by consumers with inelastic demand
Computation of optimal transport and related hedging problems via penalization and neural networks
Eckstein, Stephan; Kupper, Michael
2018-01-01
This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks. The core idea is to penalize the optimization problem in its dual formulation and reduce it to a finite dimensional one which corresponds to optimizing a neural network with smooth objective function. We present numerical examples from optimal transport, martingale optimal transport, portfolio optimization under uncertainty and generative adversa...
Optimal interval for major maintenance actions in electricity distribution networks
Energy Technology Data Exchange (ETDEWEB)
Louit, Darko; Pascual, Rodrigo [Centro de Mineria, Pontificia Universidad Catolica de Chile, Av. Vicuna MacKenna, 4860 Santiago (Chile); Banjevic, Dragan [Centre for Maintenance Optimization and Reliability Engineering, University of Toronto, 5 King' s College Rd., Toronto, Ontario (Canada)
2009-09-15
Many systems require the periodic undertaking of major (preventive) maintenance actions (MMAs) such as overhauls in mechanical equipment, reconditioning of train lines, resurfacing of roads, etc. In the long term, these actions contribute to achieving a lower rate of occurrence of failures, though in many cases they increase the intensity of the failure process shortly after performed, resulting in a non-monotonic trend for failure intensity. Also, in the special case of distributed assets such as communications and energy networks, pipelines, etc., it is likely that the maintenance action takes place sequentially over an extended period of time, implying that different sections of the network underwent the MMAs at different periods. This forces the development of a model based on a relative time scale (i.e. time since last major maintenance event) and the combination of data from different sections of a grid, under a normalization scheme. Additionally, extended maintenance times and sequential execution of the MMAs make it difficult to identify failures occurring before and after the preventive maintenance action. This results in the loss of important information for the characterization of the failure process. A simple model is introduced to determine the optimal MMA interval considering such restrictions. Furthermore, a case study illustrates the optimal tree trimming interval around an electricity distribution network. (author)
Optimal allocation of resources for suppressing epidemic spreading on networks
Chen, Hanshuang; Li, Guofeng; Zhang, Haifeng; Hou, Zhonghuai
2017-07-01
Efficient allocation of limited medical resources is crucial for controlling epidemic spreading on networks. Based on the susceptible-infected-susceptible model, we solve the optimization problem of how best to allocate the limited resources so as to minimize prevalence, providing that the curing rate of each node is positively correlated to its medical resource. By quenched mean-field theory and heterogeneous mean-field (HMF) theory, we prove that an epidemic outbreak will be suppressed to the greatest extent if the curing rate of each node is directly proportional to its degree, under which the effective infection rate λ has a maximal threshold λcopt=1 / , where is the average degree of the underlying network. For a weak infection region (λ ≳λcopt ), we combine perturbation theory with the Lagrange multiplier method (LMM) to derive the analytical expression of optimal allocation of the curing rates and the corresponding minimized prevalence. For a general infection region (λ >λcopt ), the high-dimensional optimization problem is converted into numerically solving low-dimensional nonlinear equations by the HMF theory and LMM. Counterintuitively, in the strong infection region the low-degree nodes should be allocated more medical resources than the high-degree nodes to minimize prevalence. Finally, we use simulated annealing to validate the theoretical results.
Zheng, Yan
2015-03-01
Internet of things (IoT), focusing on providing users with information exchange and intelligent control, attracts a lot of attention of researchers from all over the world since the beginning of this century. IoT is consisted of large scale of sensor nodes and data processing units, and the most important features of IoT can be illustrated as energy confinement, efficient communication and high redundancy. With the sensor nodes increment, the communication efficiency and the available communication band width become bottle necks. Many research work is based on the instance which the number of joins is less. However, it is not proper to the increasing multi-join query in whole internet of things. To improve the communication efficiency between parallel units in the distributed sensor network, this paper proposed parallel query optimization algorithm based on distribution attributes cost graph. The storage information relations and the network communication cost are considered in this algorithm, and an optimized information changing rule is established. The experimental result shows that the algorithm has good performance, and it would effectively use the resource of each node in the distributed sensor network. Therefore, executive efficiency of multi-join query between different nodes could be improved.
Directory of Open Access Journals (Sweden)
Mu Qiao
2017-01-01
Full Text Available We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.
Optimalization of logistic costs by the controlling approach
Directory of Open Access Journals (Sweden)
Katarína Teplická
2007-10-01
Full Text Available Article deals with logistic cost problems, their following, reporting and evaluation in the firm. It gives basic information about possible access of the effective decreasing of the logistic cost by the way of controlling access, or evaluation of Balance Score Card. It indicates short algorithm of logistic costs reporting by the way of controlling.
Optimalization of logistic costs by the controlling approach
Katarína Teplická
2007-01-01
Article deals with logistic cost problems, their following, reporting and evaluation in the firm. It gives basic information about possible access of the effective decreasing of the logistic cost by the way of controlling access, or evaluation of Balance Score Card. It indicates short algorithm of logistic costs reporting by the way of controlling.
Shorikov, A. F.; Butsenko, E. V.
2017-10-01
This paper discusses the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. On the basis of network modeling proposed a new economic and mathematical model and a method for solving the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. Network economic and mathematical modeling allows you to determine the optimal time and calendar schedule for the implementation of the investment project and serves as an instrument to increase the economic potential and competitiveness of the enterprise. On a meaningful practical example, the processes of forming network models are shown, including the definition of the sequence of actions of a particular investment projecting process, the network-based work schedules are constructed. The calculation of the parameters of network models is carried out. Optimal (critical) paths have been formed and the optimal time for implementing the chosen technologies of the investment project has been calculated. It also shows the selection of the optimal technology from a set of possible technologies for project implementation, taking into account the time and cost of the work. The proposed model and method for solving the problem of managing investment projects can serve as a basis for the development, creation and application of appropriate computer information systems to support the adoption of managerial decisions by business people.
Shakeri, Heman; Sahneh, Faryad Darabi; Scoglio, Caterina; Poggi-Corradini, Pietro; Preciado, Victor M
2015-06-01
Launching a prevention campaign to contain the spread of infection requires substantial financial investments; therefore, a trade-off exists between suppressing the epidemic and containing costs. Information exchange among individuals can occur as physical contacts (e.g., word of mouth, gatherings), which provide inherent possibilities of disease transmission, and non-physical contacts (e.g., email, social networks), through which information can be transmitted but the infection cannot be transmitted. Contact network (CN) incorporates physical contacts, and the information dissemination network (IDN) represents non-physical contacts, thereby generating a multilayer network structure. Inherent differences between these two layers cause alerting through CN to be more effective but more expensive than IDN. The constraint for an epidemic to die out derived from a nonlinear Perron-Frobenius problem that was transformed into a semi-definite matrix inequality and served as a constraint for a convex optimization problem. This method guarantees a dying-out epidemic by choosing the best nodes for adopting preventive behaviors with minimum monetary resources. Various numerical simulations with network models and a real-world social network validate our method.
Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization
Directory of Open Access Journals (Sweden)
Yong Wang
2018-01-01
Full Text Available In modern supply chain, logistics companies usually operate individually and optimization researches often concentrate on solving problems related to separate networks. Consequences like the complexity of urban transportation networks and long distance deliveries or pickups and pollution are leading problems to more expenses and more complaints from environment protection organizations. A solution approach to these issues is proposed in this article and consists in the adoption of two-echelon heterogeneous cooperative logistics networks (THCLN. The optimization methodology includes the formation of cooperative coalitions, the reallocation of customers to appropriate logistics facilities, and the determination of the best profit allocation scheme. First, a mixed integer linear programing model is introduced to minimize the total operating cost of nonempty coalitions. Thus, the Genetic Algorithm (GA and the Particle Swarm Optimization (PSO algorithm are hybridized to propose GA-PSO heuristics. GA-PSO is employed to provide good solutions to customer clustering units’ reallocation problem. In addition, a negotiation process is established based on logistics centers as coordinators. The case study of Chongqing city is conducted to verify the feasibility of THCLN in practice. The grand coalition and two heterogeneous subcoalitions are designed, and the collective profit is distributed based on cooperative game theory. The Minimum Cost Remaining Savings (MCRS model is used to determine good allocation schemes and strictly monotonic path principles are considered to evaluate and decide the most appropriate coalition sequence. Comparisons proved the combination of GA-PSO and MCRS better as results are found closest to the core center. Therefore, the proposed approach can be implemented in real world environment, increase the reliability of urban logistics network, and allow decision makers to improve service efficiency.
Optimal information transfer in enzymatic networks: A field theoretic formulation
Samanta, Himadri S.; Hinczewski, Michael; Thirumalai, D.
2017-07-01
Signaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a cascade involving kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus [Hinczewski and Thirumalai, Phys. Rev. X 4, 041017 (2014), 10.1103/PhysRevX.4.041017]. We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective three-species network with a pseudointermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudointermediate. Surprisingly, in these examples the minimum error computed using simulations that take nonlinearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, there are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second-order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that a field theoretic formulation of stochastic biological signaling offers a systematic way to understand error propagation in
Optimizing the African VLBI Network for Astronomy and Geodesy
de Witt, A.; Mayer, D.; MacLeod, G.; Combrinck, L.; Petrov, L.; Nickola, M.
2016-12-01
The African VLBI Network will be a pan-African network of radio telescopes comprised of converted redundant satellite Earth-station antennas and new purpose-built radio telescopes. The first of these antennas, in Ghana, is currently being converted to a radio telescope and current funding is estimated to permit the conversion of two more antennas in Africa. These antennas will initially be equipped with a 5-GHz and 6.7-GHz receiver and the next receiver likely to be fitted is a 1.4-1.7-GHz receiver. While it would be advantageous for the AVN antennas to be able to participate also in geodetic and astrometric VLBI observations, there is no funding currently for this. In this paper we re-visit the scientific justifications for the AVN in an attempt to optimize the AVN for each science case, both astronomical and geodetic.
Loss optimization in distribution networks with distributed generation
DEFF Research Database (Denmark)
Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte
2017-01-01
This paper presents a novel power loss minimization approach in distribution grids considering network reconfiguration, distributed generation and storage installation. Identification of optimum configuration in such scenario is one of the main challenges faced by distribution system operators...... in highly active distribution grids. This issue is tackled by formulating a hybrid loss optimization problem and solved using the Interior Point Method. Sensitivity analysis is used to identify the optimum location of storage units. Different scenarios of reconfiguration, storage and distributed generation...... penetration are created to test the proposed algorithm. It is tested in a benchmark medium voltage network to show the effectiveness and performance of the algorithm. Results obtained are found to be encouraging for radial distribution system. It shows that we can reduce the power loss by more than 30% using...
Optimal Node Placement in Underwater Wireless Sensor Networks
Felamban, M.
2013-03-25
Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance limitations that are very different from those used for terresstrial networks. In this paper, we investigate node placement for building an initial underwater WSN infrastructure. We formulate this problem as a nonlinear mathematical program with the objective of minimizing the total transmission loss under a given number of sensor nodes and targeted coverage volume. The obtained solution is the location of each node represented via a truncated octahedron to fill out the 3D space. Experiments are conducted to verify the proposed formulation, which is solved using Matlab optimization tool. Simulation is also conducted using an ns-3 simulator, and the simulation results are consistent with the obtained results from mathematical model with less than 10% error.
Big Data Reduction and Optimization in Sensor Monitoring Network
Directory of Open Access Journals (Sweden)
Bin He
2014-01-01
Full Text Available Wireless sensor networks (WSNs are increasingly being utilized to monitor the structural health of the underground subway tunnels, showing many promising advantages over traditional monitoring schemes. Meanwhile, with the increase of the network size, the system is incapable of dealing with big data to ensure efficient data communication, transmission, and storage. Being considered as a feasible solution to these issues, data compression can reduce the volume of data travelling between sensor nodes. In this paper, an optimization algorithm based on the spatial and temporal data compression is proposed to cope with these issues appearing in WSNs in the underground tunnel environment. The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel.
Distributed optimization of a multisubchannel Ad Hoc cognitive radio network
Leith, Alex
2012-05-01
In this paper, we study the distributed-duality-based optimization of a multisubchannel ad hoc cognitive radio network (CRN) that coexists with a multicell primary radio network (PRN). For radio resource allocation in multiuser orthogonal frequency-division multiplexing (MU-OFDM) systems, the orthogonal-access-based exclusive subchannel assignment (ESA) technique has been a popular method, but it is suboptimal in ad hoc networks, because nonorthogonal access between multiple secondary-user links by using shared subchannel assignment (SSA) can bring a higher weighted sum rate. We utilize the Lagrangian dual composition tool and design low-complexity near-optimal SSA resource allocation methods, assuming practical discrete-rate modulation and that the CRN-to-PRN interference constraint has to strictly be satisfied. However, available SSA methods for CRNs are either suboptimal or involve high complexity and suffer from slow convergence. To address this problem, we design fast-convergence SSA duality schemes and introduce several novel methods to increase the speed of convergence and to satisfy various system constraints with low complexity. For practical implementation in ad hoc CRNs, we design distributed-duality schemes that involve only a small number of CRN local information exchanges for dual update. The effects of many system parameters are presented through simulation results, which show that the near-optimal SSA duality scheme can perform significantly better than the suboptimal ESA duality and SSA-iterative waterfilling schemes and that the performance loss of the distributed schemes is small, compared with their centralized counterparts. © 2012 IEEE.
Directory of Open Access Journals (Sweden)
Andrzej Wędzik
2015-09-01
Full Text Available An internal network of a wind farm is similar to a wide network structure. Wind turbines are deployed over a vast area, and cable lines used to interconnect them may have lengths reaching tens of kilometres. The cost of constructing such a network is a major component of the entire investment. Therefore, it is advisable to develop a configuration of such a farm’s internal connections which will minimise the cost, while complying with technical requirements even at the design stage. So far this has usually been done within two independent processes. At first the network structure ensuring the shortest possible connections between the turbines is determined. Then appropriate cables compliant with technical regulations are selected for the specified structure. But does this design approach ensure the optimal (lowest investment cost? This paper gives an answer to this question. A method for accomplishing the task given in the title is presented. Examples of calculations are presented and results are compared for the two methods of optimal wind farm internal connection structure design and cable cross-section dimensioning: two-stage and integrated. The usefulness of employing the Mixed Integer Nonlinear Programming (MNLP method in the process of determining the optimal structure of a wind farm’s cable network is demonstrated.
Iterative free-energy optimization for recurrent neural networks (INFERNO)
2017-01-01
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439
Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties
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Vahid Amir
2017-11-01
Full Text Available A microgrid (MG is a small-scale version of the power system which makes possible the integration of renewable resources as well as achieving maximum demand side management (DSM utilization. The future power system will be faced with severe uncertainties owing to penetration of renewable resources. Consequently, the uncertainty assessment of system performance is essential. The conventional energy scheduling in an MG may not be suitable for active distribution networks. Hence, this study focuses on the probabilistic analysis of optimal power dispatch considering economic aspects in a multi-carrier networked microgrid. The aim is to study the impact of uncertain behavior of loads, renewable resources, and electricity market on the optimal management of a multi-carrier networked microgrid. Furthermore, a novel time-based demand side management is proposed in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. The optimization model is formulated as a mixed integer nonlinear program (MINLP and is solved using MATLAB and GAMS software. Results show that the energy sharing capability between MCMGs and MCMGs and the main grids as well as utilization of demand side management can decrease operating costs for smart distribution grids.
Prediction of energy demands using neural network with model identification by global optimization
Energy Technology Data Exchange (ETDEWEB)
Yokoyama, Ryohei; Wakui, Tetsuya; Satake, Ryoichi [Department of Mechanical Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531 (Japan)
2009-02-15
To operate energy supply plants properly from the viewpoints of stable energy supply, and energy and cost savings, it is important to predict energy demands accurately as basic conditions. Several methods of predicting energy demands have been proposed, and one of them is to use neural networks. Although local optimization methods such as gradient ones have conventionally been adopted in the back propagation procedure to identify the values of model parameters, they have the significant drawback that they can derive only local optimal solutions. In this paper, a global optimization method called ''Modal Trimming Method'' proposed for non-linear programming problems is adopted to identify the values of model parameters. In addition, the trend and periodic change are first removed from time series data on energy demand, and the converted data is used as the main input to a neural network. Furthermore, predicted values of air temperature and relative humidity are considered as additional inputs to the neural network, and their effect on the prediction of energy demand is investigated. This approach is applied to the prediction of the cooling demand in a building used for a bench mark test of a variety of prediction methods, and its validity and effectiveness are clarified. (author)
Directory of Open Access Journals (Sweden)
Shi Chen-guang
2014-08-01
Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.
Optimal pinnate leaf-like network/matrix structure for enhanced conductive cooling
International Nuclear Information System (INIS)
Hu, Liguo; Zhou, Han; Zhu, Hanxing; Fan, Tongxiang; Zhang, Di
2015-01-01
Highlights: • We present a pinnate leaf-like network/matrix structure for conductive cooling. • We study the effect of matrix thickness on network conductive cooling performance. • Matrix thickness determines optimal distance between collection channels in network. • We determine the optimal network architecture from a global perspective. • Optimal network greatly reduces the maximum temperature difference in the network. - Abstract: Heat generated in electronic devices has to be effectively removed because excessive temperature strongly impairs their performance and reliability. Embedding a high thermal conductivity network into an electronic device is an effective method to conduct the generated heat to the outside. In this study, inspired by the pinnate leaf, we present a pinnate leaf-like network embedded in the matrix (i.e., electronic device) to cool the matrix by conduction and develop a method to construct the optimal network. In this method, we first investigate the effect of the matrix thickness on the conductive cooling performance of the network, and then optimize the network architecture from a global perspective so that to minimize the maximum temperature difference between the heat sink and the matrix. The results indicate that the matrix thickness determines the optimal distance of the neighboring collection channels in the network, which minimizes the maximum temperature difference between the matrix and the network, and that the optimal network greatly reduces the maximum temperature difference in the network. The results can serve as a design guide for efficient conductive cooling of electronic devices
A cost-efficient method to optimize package size in emerging markets
Gamez-Alban, H.M.; Soto-Cardona, O.C.; Mejia Argueta, C.; Sarmiento, A.T.
2015-01-01
Packaging links the entire supply chain and coordinates all participants in the process to give a flexible and effective response to customer needs in order to maximize satisfaction at optimal cost. This research proposes an optimization model to define the minimum total cost combination of outer
Cellular Neural Networks for NP-Hard Optimization
Directory of Open Access Journals (Sweden)
Mária Ercsey-Ravasz
2009-02-01
Full Text Available A cellular neural/nonlinear network (CNN is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson spin-glass system. Using the properties of CNN, we show that one single operation (template always yields a local minimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10×10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.
Optimizing a Drone Network to Deliver Automated External Defibrillators.
Boutilier, Justin J; Brooks, Steven C; Janmohamed, Alyf; Byers, Adam; Buick, Jason E; Zhan, Cathy; Schoellig, Angela P; Cheskes, Sheldon; Morrison, Laurie J; Chan, Timothy C Y
2017-06-20
Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest, but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. We applied our model to 53 702 out-of-hospital cardiac arrests that occurred in the 8 regions of the Toronto Regional RescuNET between January 1, 2006, and December 31, 2014. Our primary analysis quantified the drone network size required to deliver an AED 1, 2, or 3 minutes faster than historical median 911 response times for each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as a large coordinated region. The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by 3 minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times. An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an out-of-hospital cardiac arrest event. © 2017 American Heart Association, Inc.
Optimal resource allocation solutions for heterogeneous cognitive radio networks
Directory of Open Access Journals (Sweden)
Babatunde Awoyemi
2017-05-01
Full Text Available Cognitive radio networks (CRN are currently gaining immense recognition as the most-likely next-generation wireless communication paradigm, because of their enticing promise of mitigating the spectrum scarcity and/or underutilisation challenge. Indisputably, for this promise to ever materialise, CRN must of necessity devise appropriate mechanisms to judiciously allocate their rather scarce or limited resources (spectrum and others among their numerous users. ‘Resource allocation (RA in CRN', which essentially describes mechanisms that can effectively and optimally carry out such allocation, so as to achieve the utmost for the network, has therefore recently become an important research focus. However, in most research works on RA in CRN, a highly significant factor that describes a more realistic and practical consideration of CRN has been ignored (or only partially explored, i.e., the aspect of the heterogeneity of CRN. To address this important aspect, in this paper, RA models that incorporate the most essential concepts of heterogeneity, as applicable to CRN, are developed and the imports of such inclusion in the overall networking are investigated. Furthermore, to fully explore the relevance and implications of the various heterogeneous classifications to the RA formulations, weights are attached to the different classes and their effects on the network performance are studied. In solving the developed complex RA problems for heterogeneous CRN, a solution approach that examines and exploits the structure of the problem in achieving a less-complex reformulation, is extensively employed. This approach, as the results presented show, makes it possible to obtain optimal solutions to the rather difficult RA problems of heterogeneous CRN.
Optimal Switch Configuration in Software-Defined Networks
Directory of Open Access Journals (Sweden)
Béla GENGE
2016-06-01
Full Text Available The emerging Software-Defined Networks (SDN paradigm facilitates innovative applications and enables the seamless provisioning of resilient communications. Nevertheless, the installation of communication flows in SDN requires careful planning in order to avoid configuration errors and to fulfill communication requirements. In this paper we propose an approach that installs automatically and optimally static flows in SDN switches. The approach aims to select high capacity links and shortest path routing, and enforces communication link and switch capacity limitations. Experimental results demonstrate the effectiveness and scalability of the developed methodology.
Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization
Directory of Open Access Journals (Sweden)
Lianbo Ma
2014-01-01
Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
Optimization of Multiple Related Negotiation through Multi-Negotiation Network
Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi
In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.
Hierarchical artificial bee colony algorithm for RFID network planning optimization.
Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong
2014-01-01
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks
Rai, Man Mohan
2006-01-01
flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.
Process Cost Modeling for Multi-Disciplinary Design Optimization
Bao, Han P.; Freeman, William (Technical Monitor)
2002-01-01
For early design concepts, the conventional approach to cost is normally some kind of parametric weight-based cost model. There is now ample evidence that this approach can be misleading and inaccurate. By the nature of its development, a parametric cost model requires historical data and is valid only if the new design is analogous to those for which the model was derived. Advanced aerospace vehicles have no historical production data and are nowhere near the vehicles of the past. Using an existing weight-based cost model would only lead to errors and distortions of the true production cost. This report outlines the development of a process-based cost model in which the physical elements of the vehicle are costed according to a first-order dynamics model. This theoretical cost model, first advocated by early work at MIT, has been expanded to cover the basic structures of an advanced aerospace vehicle. Elemental costs based on the geometry of the design can be summed up to provide an overall estimation of the total production cost for a design configuration. This capability to directly link any design configuration to realistic cost estimation is a key requirement for high payoff MDO problems. Another important consideration in this report is the handling of part or product complexity. Here the concept of cost modulus is introduced to take into account variability due to different materials, sizes, shapes, precision of fabrication, and equipment requirements. The most important implication of the development of the proposed process-based cost model is that different design configurations can now be quickly related to their cost estimates in a seamless calculation process easily implemented on any spreadsheet tool. In successive sections, the report addresses the issues of cost modeling as follows. First, an introduction is presented to provide the background for the research work. Next, a quick review of cost estimation techniques is made with the intention to
Cost Information – an Objective Necessity in Optimizing Decision Making
Petre Mihaela – Cosmina; Petroianu Grazia - Oana
2012-01-01
An overall growth can be registered at macro and micro level without achieving a development and this only under conditions of continuous improvement methods and techniques of organization and management within the unit. Cost and cost information play an important role being considered and recognized as useful and effective tools to reach any leader. They have features such as multiple facets to facilitate continuous improvement towards business unit. Cost awareness represents a decisive fact...