A real-time Java tool chain for resource constrained platforms

DEFF Research Database (Denmark)

Korsholm, Stephan Erbs; Søndergaard, Hans; Ravn, Anders P.

2013-01-01

The Java programming language was originally developed for embedded systems, but the resource requirements of previous and current Java implementations - especially memory consumption - tend to exclude them from being used on a significant class of resource constrained embedded platforms. The con......The Java programming language was originally developed for embedded systems, but the resource requirements of previous and current Java implementations - especially memory consumption - tend to exclude them from being used on a significant class of resource constrained embedded platforms...... by integrating: (1) a lean virtual machine (HVM) without any external dependencies on POSIX-like libraries or other OS functionalities, (2) a hardware abstraction layer, implemented almost entirely in Java through the use of hardware objects, first level interrupt handlers, and native variables, and (3....... An evaluation of the presented solution shows that the miniCDj benchmark gets reduced to a size where it can run on resource constrained platforms....

A real-time Java tool chain for resource constrained platforms

DEFF Research Database (Denmark)

Korsholm, Stephan E.; Søndergaard, Hans; Ravn, Anders Peter

2014-01-01

The Java programming language was originally developed for embedded systems, but the resource requirements of previous and current Java implementations – especially memory consumption – tend to exclude them from being used on a significant class of resource constrained embedded platforms. The con......The Java programming language was originally developed for embedded systems, but the resource requirements of previous and current Java implementations – especially memory consumption – tend to exclude them from being used on a significant class of resource constrained embedded platforms...... by integrating the following: (1) a lean virtual machine without any external dependencies on POSIX-like libraries or other OS functionalities; (2) a hardware abstraction layer, implemented almost entirely in Java through the use of hardware objects, first level interrupt handlers, and native variables; and (3....... An evaluation of the presented solution shows that the miniCDj benchmark gets reduced to a size where it can run on resource constrained platforms....

A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

Science.gov (United States)

Zhang, Chenglong; Guo, Ping

2017-10-01

The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.

Node Discovery and Interpretation in Unstructured Resource-Constrained Environments

DEFF Research Database (Denmark)

Gechev, Miroslav; Kasabova, Slavyana; Mihovska, Albena D.

2014-01-01

for the discovery, linking and interpretation of nodes in unstructured and resource-constrained network environments and their interrelated and collective use for the delivery of smart services. The model is based on a basic mathematical approach, which describes and predicts the success of human interactions...... in the context of long-term relationships and identifies several key variables in the context of communications in resource-constrained environments. The general theoretical model is described and several algorithms are proposed as part of the node discovery, identification, and linking processes in relation...

Modeling Oil Exploration and Production: Resource-Constrained and Agent-Based Approaches

International Nuclear Information System (INIS)

Jakobsson, Kristofer

2010-05-01

Energy is essential to the functioning of society, and oil is the single largest commercial energy source. Some analysts have concluded that the peak in oil production is soon about to happen on the global scale, while others disagree. Such incompatible views can persist because the issue of 'peak oil' cuts through the established scientific disciplines. The question is: what characterizes the modeling approaches that are available today, and how can they be further developed to improve a trans-disciplinary understanding of oil depletion? The objective of this thesis is to present long-term scenarios of oil production (Paper I) using a resource-constrained model; and an agent-based model of the oil exploration process (Paper II). It is also an objective to assess the strengths, limitations, and future development potentials of resource-constrained modeling, analytical economic modeling, and agent-based modeling. Resource-constrained models are only suitable when the time frame is measured in decades, but they can give a rough indication of which production scenarios are reasonable given the size of the resource. However, the models are comprehensible, transparent and the only feasible long-term forecasting tools at present. It is certainly possible to distinguish between reasonable scenarios, based on historically observed parameter values, and unreasonable scenarios with parameter values obtained through flawed analogy. The economic subfield of optimal depletion theory is founded on the notion of rational economic agents, and there is a causal relation between decisions made at the micro-level and the macro-result. In terms of future improvements, however, the analytical form considerably restricts the versatility of the approach. Agent-based modeling makes it feasible to combine economically motivated agents with a physical environment. An example relating to oil exploration is given in Paper II, where it is shown that the exploratory activities of individual

Butterfly Encryption Scheme for Resource-Constrained Wireless Networks

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Raghav V. Sampangi

2015-09-01

Full Text Available Resource-constrained wireless networks are emerging networks such as Radio Frequency Identification (RFID and Wireless Body Area Networks (WBAN that might have restrictions on the available resources and the computations that can be performed. These emerging technologies are increasing in popularity, particularly in defence, anti-counterfeiting, logistics and medical applications, and in consumer applications with growing popularity of the Internet of Things. With communication over wireless channels, it is essential to focus attention on securing data. In this paper, we present an encryption scheme called Butterfly encryption scheme. We first discuss a seed update mechanism for pseudorandom number generators (PRNG, and employ this technique to generate keys and authentication parameters for resource-constrained wireless networks. Our scheme is lightweight, as in it requires less resource when implemented and offers high security through increased unpredictability, owing to continuously changing parameters. Our work focuses on accomplishing high security through simplicity and reuse. We evaluate our encryption scheme using simulation, key similarity assessment, key sequence randomness assessment, protocol analysis and security analysis.

Butterfly Encryption Scheme for Resource-Constrained Wireless Networks.

Science.gov (United States)

Sampangi, Raghav V; Sampalli, Srinivas

2015-09-15

Resource-constrained wireless networks are emerging networks such as Radio Frequency Identification (RFID) and Wireless Body Area Networks (WBAN) that might have restrictions on the available resources and the computations that can be performed. These emerging technologies are increasing in popularity, particularly in defence, anti-counterfeiting, logistics and medical applications, and in consumer applications with growing popularity of the Internet of Things. With communication over wireless channels, it is essential to focus attention on securing data. In this paper, we present an encryption scheme called Butterfly encryption scheme. We first discuss a seed update mechanism for pseudorandom number generators (PRNG), and employ this technique to generate keys and authentication parameters for resource-constrained wireless networks. Our scheme is lightweight, as in it requires less resource when implemented and offers high security through increased unpredictability, owing to continuously changing parameters. Our work focuses on accomplishing high security through simplicity and reuse. We evaluate our encryption scheme using simulation, key similarity assessment, key sequence randomness assessment, protocol analysis and security analysis.

Resource-Constrained Low-Complexity Video Coding for Wireless Transmission

DEFF Research Database (Denmark)

Ukhanova, Ann

of video quality. We proposed a new metric for objective quality assessment that considers frame rate. As many applications deal with wireless video transmission, we performed an analysis of compression and transmission systems with a focus on power-distortion trade-off. We proposed an approach...... for ratedistortion-complexity optimization of upcoming video compression standard HEVC. We also provided a new method allowing decrease of power consumption on mobile devices in 3G networks. Finally, we proposed low-delay and low-power approaches for video transmission over wireless personal area networks, including......Constrained resources like memory, power, bandwidth and delay requirements in many mobile systems pose limitations for video applications. Standard approaches for video compression and transmission do not always satisfy system requirements. In this thesis we have shown that it is possible to modify...

Neuroevolutionary Constrained Optimization for Content Creation

DEFF Research Database (Denmark)

Liapis, Antonios; Yannakakis, Georgios N.; Togelius, Julian

2011-01-01

and thruster types and topologies) independently of game physics and steering strategies. According to the proposed framework, the designer picks a set of requirements for the spaceship that a constrained optimizer attempts to satisfy. The constraint satisfaction approach followed is based on neuroevolution...... and survival tasks and are also visually appealing....

Quasicanonical structure of optimal control in constrained discrete systems

Science.gov (United States)

Sieniutycz, S.

2003-06-01

This paper considers discrete processes governed by difference rather than differential equations for the state transformation. The basic question asked is if and when Hamiltonian canonical structures are possible in optimal discrete systems. Considering constrained discrete control, general optimization algorithms are derived that constitute suitable theoretical and computational tools when evaluating extremum properties of constrained physical models. The mathematical basis of the general theory is the Bellman method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage criterion which allows a variation of the terminal state that is otherwise fixed in the Bellman's method. Two relatively unknown, powerful optimization algorithms are obtained: an unconventional discrete formalism of optimization based on a Hamiltonian for multistage systems with unconstrained intervals of holdup time, and the time interval constrained extension of the formalism. These results are general; namely, one arrives at: the discrete canonical Hamilton equations, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory along with all basic results of variational calculus. Vast spectrum of applications of the theory is briefly discussed.

Selection of magnetorheological brake types via optimal design considering maximum torque and constrained volume

International Nuclear Information System (INIS)

Nguyen, Q H; Choi, S B

2012-01-01

This research focuses on optimal design of different types of magnetorheological brakes (MRBs), from which an optimal selection of MRB types is identified. In the optimization, common types of MRB such as disc-type, drum-type, hybrid-types, and T-shaped type are considered. The optimization problem is to find the optimal value of significant geometric dimensions of the MRB that can produce a maximum braking torque. The MRB is constrained in a cylindrical volume of a specific radius and length. After a brief description of the configuration of MRB types, the braking torques of the MRBs are derived based on the Herschel–Bulkley model of the MR fluid. The optimal design of MRBs constrained in a specific cylindrical volume is then analysed. The objective of the optimization is to maximize the braking torque while the torque ratio (the ratio of maximum braking torque and the zero-field friction torque) is constrained to be greater than a certain value. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions of the MRBs. Optimal solutions of MRBs constrained in different volumes are obtained based on the proposed optimization procedure. From the results, discussions on the optimal selection of MRB types depending on constrained volumes are given. (paper)

Thermally-Constrained Fuel-Optimal ISS Maneuvers

Science.gov (United States)

Bhatt, Sagar; Svecz, Andrew; Alaniz, Abran; Jang, Jiann-Woei; Nguyen, Louis; Spanos, Pol

2015-01-01

Optimal Propellant Maneuvers (OPMs) are now being used to rotate the International Space Station (ISS) and have saved hundreds of kilograms of propellant over the last two years. The savings are achieved by commanding the ISS to follow a pre-planned attitude trajectory optimized to take advantage of environmental torques. The trajectory is obtained by solving an optimal control problem. Prior to use on orbit, OPM trajectories are screened to ensure a static sun vector (SSV) does not occur during the maneuver. The SSV is an indicator that the ISS hardware temperatures may exceed thermal limits, causing damage to the components. In this paper, thermally-constrained fuel-optimal trajectories are presented that avoid an SSV and can be used throughout the year while still reducing propellant consumption significantly.

Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

Directory of Open Access Journals (Sweden)

Minggang Dong

2014-01-01

Full Text Available Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE for constrained optimization problems (COPs. More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay.

Science.gov (United States)

Lu, Weidang; Lin, Yuanrong; Peng, Hong; Nan, Tian; Liu, Xin

2017-09-13

Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN's achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

Optimization of an implicit constrained multi-physics system for motor wheels of electric vehicle

International Nuclear Information System (INIS)

Lei, Fei; Du, Bin; Liu, Xin; Xie, Xiaoping; Chai, Tian

2016-01-01

In this paper, implicit constrained multi-physics model of a motor wheel for an electric vehicle is built and then optimized. A novel optimization approach is proposed to solve the compliance problem between implicit constraints and stochastic global optimization. Firstly, multi-physics model of motor wheel is built from the theories of structural mechanics, electromagnetism and thermal physics. Then, implicit constraints are applied from the vehicle performances and magnetic characteristics. Implicit constrained optimization is carried out by a series of unconstrained optimization and verifications. In practice, sequentially updated subspaces are designed to completely substitute the original design space in local areas. In each subspace, a solution is obtained and is then verified by the implicit constraints. Optimal solutions which satisfy the implicit constraints are accepted as final candidates. The final global optimal solution is optimized from those candidates. Discussions are carried out to discover the differences between optimal solutions with unconstrained problem and different implicit constrained problems. Results show that the implicit constraints have significant influences on the optimal solution and the proposed approach is effective in finding the optimals. - Highlights: • An implicit constrained multi-physics model is built for sizing a motor wheel. • Vehicle dynamic performances are applied as implicit constraints for nonlinear system. • An efficient novel optimization is proposed to explore the constrained design space. • The motor wheel is optimized to achieve maximum efficiency on vehicle dynamics. • Influences of implicit constraints on vehicle performances are compared and analyzed.

Chance-constrained optimization of demand response to price signals

DEFF Research Database (Denmark)

Dorini, Gianluca Fabio; Pinson, Pierre; Madsen, Henrik

2013-01-01

within a recursive least squares (RLS) framework using data measurable at the grid level, in an adaptive fashion. Optimal price signals are generated by embedding the FIR models within a chance-constrained optimization framework. The objective is to keep the price signal as unchanged as possible from...

A penalty method for PDE-constrained optimization in inverse problems

International Nuclear Information System (INIS)

Leeuwen, T van; Herrmann, F J

2016-01-01

Many inverse and parameter estimation problems can be written as PDE-constrained optimization problems. The goal is to infer the parameters, typically coefficients of the PDE, from partial measurements of the solutions of the PDE for several right-hand sides. Such PDE-constrained problems can be solved by finding a stationary point of the Lagrangian, which entails simultaneously updating the parameters and the (adjoint) state variables. For large-scale problems, such an all-at-once approach is not feasible as it requires storing all the state variables. In this case one usually resorts to a reduced approach where the constraints are explicitly eliminated (at each iteration) by solving the PDEs. These two approaches, and variations thereof, are the main workhorses for solving PDE-constrained optimization problems arising from inverse problems. In this paper, we present an alternative method that aims to combine the advantages of both approaches. Our method is based on a quadratic penalty formulation of the constrained optimization problem. By eliminating the state variable, we develop an efficient algorithm that has roughly the same computational complexity as the conventional reduced approach while exploiting a larger search space. Numerical results show that this method indeed reduces some of the nonlinearity of the problem and is less sensitive to the initial iterate. (paper)

Scheduling of resource-constrained projects

CERN Document Server

Klein, Robert

2000-01-01

Project management has become a widespread instrument enabling organizations to efficiently master the challenges of steadily shortening product life cycles, global markets and decreasing profit margins. With projects increasing in size and complexity, their planning and control represents one of the most crucial management tasks. This is especially true for scheduling, which is concerned with establishing execution dates for the sub-activities to be performed in order to complete the project. The ability to manage projects where resources must be allocated between concurrent projects or even sub-activities of a single project requires the use of commercial project management software packages. However, the results yielded by the solution procedures included are often rather unsatisfactory. Scheduling of Resource-Constrained Projects develops more efficient procedures, which can easily be integrated into software packages by incorporated programming languages, and thus should be of great interest for practiti...

Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization

Directory of Open Access Journals (Sweden)

Weishang Gao

2013-01-01

Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.

A Novel Optimal Joint Resource Allocation Method in Cooperative Multicarrier Networks: Theory and Practice

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Yuan Gao

2016-04-01

Full Text Available With the increasing demands for better transmission speed and robust quality of service (QoS, the capacity constrained backhaul gradually becomes a bottleneck in cooperative wireless networks, e.g., in the Internet of Things (IoT scenario in joint processing mode of LTE-Advanced Pro. This paper focuses on resource allocation within capacity constrained backhaul in uplink cooperative wireless networks, where two base stations (BSs equipped with single antennae serve multiple single-antennae users via multi-carrier transmission mode. In this work, we propose a novel cooperative transmission scheme based on compress-and-forward with user pairing to solve the joint mixed integer programming problem. To maximize the system capacity under the limited backhaul, we formulate the joint optimization problem of user sorting, subcarrier mapping and backhaul resource sharing among different pairs (subcarriers for users. A novel robust and efficient centralized algorithm based on alternating optimization strategy and perfect mapping is proposed. Simulations show that our novel method can improve the system capacity significantly under the constraint of the backhaul resource compared with the blind alternatives.

Optimal Power Constrained Distributed Detection over a Noisy Multiaccess Channel

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Zhiwen Hu

2015-01-01

Full Text Available The problem of optimal power constrained distributed detection over a noisy multiaccess channel (MAC is addressed. Under local power constraints, we define the transformation function for sensor to realize the mapping from local decision to transmitted waveform. The deflection coefficient maximization (DCM is used to optimize the performance of power constrained fusion system. Using optimality conditions, we derive the closed-form solution to the considered problem. Monte Carlo simulations are carried out to evaluate the performance of the proposed new method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system with low signal-to-noise ratio (SNR. We also show that the proposed new method has a robust detection performance for broad SNR region.

A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

Science.gov (United States)

Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

2018-03-01

In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

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R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Volume-constrained optimization of magnetorheological and electrorheological valves and dampers

Science.gov (United States)

Rosenfeld, Nicholas C.; Wereley, Norman M.

2004-12-01

This paper presents a case study of magnetorheological (MR) and electrorheological (ER) valve design within a constrained cylindrical volume. The primary purpose of this study is to establish general design guidelines for volume-constrained MR valves. Additionally, this study compares the performance of volume-constrained MR valves against similarly constrained ER valves. Starting from basic design guidelines for an MR valve, a method for constructing candidate volume-constrained valve geometries is presented. A magnetic FEM program is then used to evaluate the magnetic properties of the candidate valves. An optimized MR valve is chosen by evaluating non-dimensional parameters describing the candidate valves' damping performance. A derivation of the non-dimensional damping coefficient for valves with both active and passive volumes is presented to allow comparison of valves with differing proportions of active and passive volumes. The performance of the optimized MR valve is then compared to that of a geometrically similar ER valve using both analytical and numerical techniques. An analytical equation relating the damping performances of geometrically similar MR and ER valves in as a function of fluid yield stresses and relative active fluid volume, and numerical calculations are provided to calculate each valve's damping performance and to validate the analytical calculations.

Mature Basin Development Portfolio Management in a Resource Constrained Environment

International Nuclear Information System (INIS)

Mandhane, J. M.; Udo, S. D.

2002-01-01

Nigerian Petroleum industry is constantly faced with management of resource constraints stemming from capital and operating budget, availability of skilled manpower, capacity of an existing surface facility, size of well assets, amount of soft and hard information, etceteras. Constrained capital forces the industry to rank subsurface resource and potential before proceeding with preparation of development scenarios. Availability of skilled manpower limits scope of integrated reservoir studies. Level of information forces technical and management to find low-risk development alternative in a limited time. Volume of either oil or natural gas or water or combination of them may be constrained due to design limits of the existing facility, or an external OPEC quota, requires high portfolio management skills.The first part of the paper statistically analyses development portfolio of a mature basin for (a) subsurface resources volume, (b) developed and undeveloped and undeveloped volumes, (c) sweating of wells, and (d) facility assets. The analysis presented conclusively demonstrates that the 80/20 is active in the statistical sample. The 80/20 refers to 80% of the effect coming from the 20% of the cause. The second part of the paper deals with how 80/20 could be applied to manage portfolio for a given set of constraints. Three application examples are discussed. Feedback on implementation of them resulting in focussed resource management with handsome rewards is documented.The statistical analysis and application examples from a mature basin form a way forward for a development portfolio management in an resource constrained environment

Solution for state constrained optimal control problems applied to power split control for hybrid vehicles

NARCIS (Netherlands)

Keulen, van T.A.C.; Gillot, J.; Jager, de A.G.; Steinbuch, M.

2014-01-01

This paper presents a numerical solution for scalar state constrained optimal control problems. The algorithm rewrites the constrained optimal control problem as a sequence of unconstrained optimal control problems which can be solved recursively as a two point boundary value problem. The solution

A one-layer recurrent neural network for constrained nonsmooth invex optimization.

Science.gov (United States)

Li, Guocheng; Yan, Zheng; Wang, Jun

2014-02-01

Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

Antifungal susceptibility testing method for resource constrained laboratories

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Khan S

2006-01-01

Full Text Available Purpose: In resource-constrained laboratories of developing countries determination of antifungal susceptibility testing by NCCLS/CLSI method is not always feasible. We describe herein a simple yet comparable method for antifungal susceptibility testing. Methods: Reference MICs of 72 fungal isolates including two quality control strains were determined by NCCLS/CLSI methods against fluconazole, itraconazole, voriconazole, amphotericin B and cancidas. Dermatophytes were also tested against terbinafine. Subsequently, on selection of optimum conditions, MIC was determined for all the fungal isolates by semisolid antifungal agar susceptibility method in Brain heart infusion broth supplemented with 0.5% agar (BHIA without oil overlay and results were compared with those obtained by reference NCCLS/CLSI methods. Results: Comparable results were obtained by NCCLS/CLSI and semisolid agar susceptibility (SAAS methods against quality control strains. MICs for 72 isolates did not differ by more than one dilution for all drugs by SAAS. Conclusions: SAAS using BHIA without oil overlay provides a simple and reproducible method for obtaining MICs against yeast, filamentous fungi and dermatophytes in resource-constrained laboratories.

New Exact Penalty Functions for Nonlinear Constrained Optimization Problems

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Bingzhuang Liu

2014-01-01

Full Text Available For two kinds of nonlinear constrained optimization problems, we propose two simple penalty functions, respectively, by augmenting the dimension of the primal problem with a variable that controls the weight of the penalty terms. Both of the penalty functions enjoy improved smoothness. Under mild conditions, it can be proved that our penalty functions are both exact in the sense that local minimizers of the associated penalty problem are precisely the local minimizers of the original constrained problem.

Constrained multi-objective optimization of storage ring lattices

Science.gov (United States)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

Communication Schemes with Constrained Reordering of Resources

DEFF Research Database (Denmark)

Popovski, Petar; Utkovski, Zoran; Trillingsgaard, Kasper Fløe

2013-01-01

This paper introduces a communication model inspired by two practical scenarios. The first scenario is related to the concept of protocol coding, where information is encoded in the actions taken by an existing communication protocol. We investigate strategies for protocol coding via combinatorial...... reordering of the labelled user resources (packets, channels) in an existing, primary system. However, the degrees of freedom of the reordering are constrained by the operation of the primary system. The second scenario is related to communication systems with energy harvesting, where the transmitted signals...... are constrained by the energy that is available through the harvesting process. We have introduced a communication model that covers both scenarios and elicits their key feature, namely the constraints of the primary system or the harvesting process. We have shown how to compute the capacity of the channels...

HotpathVM: An Effective JIT for Resource-constrained Devices

DEFF Research Database (Denmark)

Gal, Andreas; Franz, Michael; Probst, Christian

2006-01-01

We present a just-in-time compiler for a Java VM that is small enough to fit on resource-constrained devices, yet surprisingly effective. Our system dynamically identifies traces of frequently executed bytecode instructions (which may span several basic blocks across several methods) and compiles...

Stochastic Resource Allocation for Energy-Constrained Systems

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Sachs DanielGrobe

2009-01-01

Full Text Available Battery-powered wireless systems running media applications have tight constraints on energy, CPU, and network capacity, and therefore require the careful allocation of these limited resources to maximize the system's performance while avoiding resource overruns. Usually, resource-allocation problems are solved using standard knapsack-solving techniques. However, when allocating conservable resources like energy (which unlike CPU and network remain available for later use if they are not used immediately knapsack solutions suffer from excessive computational complexity, leading to the use of suboptimal heuristics. We show that use of Lagrangian optimization provides a fast, elegant, and, for convex problems, optimal solution to the allocation of energy across applications as they enter and leave the system, even if the exact sequence and timing of their entrances and exits is not known. This permits significant increases in achieved utility compared to heuristics in common use. As our framework requires only a stochastic description of future workloads, and not a full schedule, we also significantly expand the scope of systems that can be optimized.

Adaptive Multi-Agent Systems for Constrained Optimization

Science.gov (United States)

Macready, William; Bieniawski, Stefan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

Stress-constrained topology optimization for compliant mechanism design

DEFF Research Database (Denmark)

de Leon, Daniel M.; Alexandersen, Joe; Jun, Jun S.

2015-01-01

This article presents an application of stress-constrained topology optimization to compliant mechanism design. An output displacement maximization formulation is used, together with the SIMP approach and a projection method to ensure convergence to nearly discrete designs. The maximum stress...... is approximated using a normalized version of the commonly-used p-norm of the effective von Mises stresses. The usual problems associated with topology optimization for compliant mechanism design: one-node and/or intermediate density hinges are alleviated by the stress constraint. However, it is also shown...

Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization

International Nuclear Information System (INIS)

Zhang Xiaomeng; Wang Jing; Xing Lei

2011-01-01

Purpose: The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization. Methods: After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of ''metal shadows'' in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective. Results: The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images. Conclusions: Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general ''missing data'' image reconstruction problems.

Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems

National Research Council Canada - National Science Library

Abramson, Mark A; Audet, Charles; Dennis, Jr, J. E

2004-01-01

.... This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints...

A constrained optimization algorithm for total energy minimization in electronic structure calculations

International Nuclear Information System (INIS)

Yang Chao; Meza, Juan C.; Wang Linwang

2006-01-01

A new direct constrained optimization algorithm for minimizing the Kohn-Sham (KS) total energy functional is presented in this paper. The key ingredients of this algorithm involve projecting the total energy functional into a sequence of subspaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer of a subspace energy functional not only provides a search direction along which the KS total energy functional decreases but also gives an optimal 'step-length' to move along this search direction. Numerical examples are provided to demonstrate that this new direct constrained optimization algorithm can be more efficient than the self-consistent field (SCF) iteration

OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

Directory of Open Access Journals (Sweden)

Dhananjay Kumar

2016-01-01

Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity.

Science.gov (United States)

Quan, Quan; Cai, Kai-Yuan

2016-02-01

In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-time, equality-constrained optimization is developed. First, the equality constraint is transformed into a continuous-time dynamical system with solutions that always satisfy the equality constraint. Second, a new projection matrix without singularity is proposed to realize the transformation. An update (or say a controller) is subsequently designed to decrease the objective function along the solutions of the transformed continuous-time dynamical system. The invariance principle is then applied to analyze the behavior of the solution. Furthermore, the proposed method is modified to address cases in which solutions do not satisfy the equality constraint. Finally, the proposed optimization approach is applied to three examples to demonstrate its effectiveness.

Amodified probabilistic genetic algorithm for the solution of complex constrained optimization problems

OpenAIRE

Vorozheikin, A.; Gonchar, T.; Panfilov, I.; Sopov, E.; Sopov, S.

2009-01-01

A new algorithm for the solution of complex constrained optimization problems based on the probabilistic genetic algorithm with optimal solution prediction is proposed. The efficiency investigation results in comparison with standard genetic algorithm are presented.

Adaptive multi-objective Optimization scheme for cognitive radio resource management

KAUST Repository

Alqerm, Ismail

2014-12-01

Cognitive Radio is an intelligent Software Defined Radio that is capable to alter its transmission parameters according to predefined objectives and wireless environment conditions. Cognitive engine is the actuator that performs radio parameters configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance. The optimization relies on adapting radio transmission parameters to environment conditions using constrained optimization modeling called fitness functions in an iterative manner. These functions include minimizing power consumption, Bit Error Rate, delay and interference. On the other hand, maximizing throughput and spectral efficiency. Cross-layer optimization is exploited to access environmental parameters from all TCP/IP stack layers. AMOS uses adaptive Genetic Algorithm in terms of its parameters and objective weights as the vehicle of optimization. The proposed scheme has demonstrated quick response and efficiency in three different scenarios compared to other schemes. In addition, it shows its capability to optimize the performance of TCP/IP layers as whole not only the physical layer.

On meeting capital requirements with a chance-constrained optimization model.

Science.gov (United States)

Atta Mills, Ebenezer Fiifi Emire; Yu, Bo; Gu, Lanlan

2016-01-01

This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets.

Topology Optimization of Constrained Layer Damping on Plates Using Method of Moving Asymptote (MMA Approach

Directory of Open Access Journals (Sweden)

Zheng Ling

2011-01-01

Full Text Available Damping treatments have been extensively used as a powerful means to damp out structural resonant vibrations. Usually, damping materials are fully covered on the surface of plates. The drawbacks of this conventional treatment are also obvious due to an added mass and excess material consumption. Therefore, it is not always economical and effective from an optimization design view. In this paper, a topology optimization approach is presented to maximize the modal damping ratio of the plate with constrained layer damping treatment. The governing equation of motion of the plate is derived on the basis of energy approach. A finite element model to describe dynamic performances of the plate is developed and used along with an optimization algorithm in order to determine the optimal topologies of constrained layer damping layout on the plate. The damping of visco-elastic layer is modeled by the complex modulus formula. Considering the vibration and energy dissipation mode of the plate with constrained layer damping treatment, damping material density and volume factor are considered as design variable and constraint respectively. Meantime, the modal damping ratio of the plate is assigned as the objective function in the topology optimization approach. The sensitivity of modal damping ratio to design variable is further derived and Method of Moving Asymptote (MMA is adopted to search the optimized topologies of constrained layer damping layout on the plate. Numerical examples are used to demonstrate the effectiveness of the proposed topology optimization approach. The results show that vibration energy dissipation of the plates can be enhanced by the optimal constrained layer damping layout. This optimal technology can be further extended to vibration attenuation of sandwich cylindrical shells which constitute the major building block of many critical structures such as cabins of aircrafts, hulls of submarines and bodies of rockets and missiles as an

A Simply Constrained Optimization Reformulation of KKT Systems Arising from Variational Inequalities

International Nuclear Information System (INIS)

Facchinei, F.; Fischer, A.; Kanzow, C.; Peng, J.-M.

1999-01-01

The Karush-Kuhn-Tucker (KKT) conditions can be regarded as optimality conditions for both variational inequalities and constrained optimization problems. In order to overcome some drawbacks of recently proposed reformulations of KKT systems, we propose casting KKT systems as a minimization problem with nonnegativity constraints on some of the variables. We prove that, under fairly mild assumptions, every stationary point of this constrained minimization problem is a solution of the KKT conditions. Based on this reformulation, a new algorithm for the solution of the KKT conditions is suggested and shown to have some strong global and local convergence properties

Fuzzy chance constrained linear programming model for scrap charge optimization in steel production

DEFF Research Database (Denmark)

Rong, Aiying; Lahdelma, Risto

2008-01-01

the uncertainty based on fuzzy set theory and constrain the failure risk based on a possibility measure. Consequently, the scrap charge optimization problem is modeled as a fuzzy chance constrained linear programming problem. Since the constraints of the model mainly address the specification of the product...

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

Directory of Open Access Journals (Sweden)

Hailong Wang

2018-01-01

Full Text Available The backtracking search optimization algorithm (BSA is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

Directory of Open Access Journals (Sweden)

Ziyang He

2018-04-01

Full Text Available By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

Science.gov (United States)

He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-04-17

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

Managing pressures ulcers in a resource constrained situation: A holistic approach

Directory of Open Access Journals (Sweden)

Abhijit Dam

2011-01-01

Full Text Available Managing pressure ulcers remain a challenge and call for a multidisciplinary team approach to care. Even more daunting is the management of such patients in remote locations and in resource constrained situations. The management of pressure sores in a patient with progressive muscular atrophy has been discussed using resources that were locally available, accessible, and affordable. Community participation was encouraged. A holistic approach to care was adopted.

Resource Constrained Project Scheduling Subject to Due Dates: Preemption Permitted with Penalty

Directory of Open Access Journals (Sweden)

Behrouz Afshar-Nadjafi

2014-01-01

Full Text Available Extensive research works have been carried out in resource constrained project scheduling problem. However, scarce researches have studied the problems in which a setup cost must be incurred if activities are preempted. In this research, we investigate the resource constrained project scheduling problem to minimize the total project cost, considering earliness-tardiness and preemption penalties. A mixed integer programming formulation is proposed for the problem. The resulting problem is NP-hard. So, we try to obtain a satisfying solution using simulated annealing (SA algorithm. The efficiency of the proposed algorithm is tested based on 150 randomly produced examples. Statistical comparison in terms of the computational times and objective function indicates that the proposed algorithm is efficient and effective.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

Science.gov (United States)

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

Nonlinear Chance Constrained Problems: Optimality Conditions, Regularization and Solvers

Czech Academy of Sciences Publication Activity Database

Adam, Lukáš; Branda, Martin

2016-01-01

Roč. 170, č. 2 (2016), s. 419-436 ISSN 0022-3239 R&D Projects: GA ČR GA15-00735S Institutional support: RVO:67985556 Keywords : Chance constrained programming * Optimality conditions * Regularization * Algorithms * Free MATLAB codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.289, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0460909.pdf

Superalloy design - A Monte Carlo constrained optimization method

CSIR Research Space (South Africa)

Stander, CM

1996-01-01

Full Text Available optimization method C. M. Stander Division of Materials Science and Technology, CSIR, PO Box 395, Pretoria, Republic of South Africa Received 74 March 1996; accepted 24 June 1996 A method, based on Monte Carlo constrained... successful hit, i.e. when Liow < LMP,,, < Lhiph, and for all the properties, Pj?, < P, < Pi@?. If successful this hit falls within the ROA. Repeat steps 4 and 5 to find at least ten (or more) successful hits with values...

CLFs-based optimization control for a class of constrained visual servoing systems.

Science.gov (United States)

Song, Xiulan; Miaomiao, Fu

2017-03-01

In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

KAUST Repository

Reyes, Juan Carlos De los

2013-11-01

We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

KAUST Repository

Reyes, Juan Carlos De los; Schö nlieb, Carola-Bibiane

2013-01-01

We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

A hybrid electromagnetism-like algorithm for a multi-mode resource-constrained project scheduling problem

Directory of Open Access Journals (Sweden)

Mohammad Hossein Sadeghi

2013-08-01

Full Text Available In this paper, two different sub-problems are considered to solve a resource constrained project scheduling problem (RCPSP, namely i assignment of modes to tasks and ii scheduling of these tasks in order to minimize the makespan of the project. The modified electromagnetism-like algorithm deals with the first problem to create an assignment of modes to activities. This list is used to generate a project schedule. When a new assignment is made, it is necessary to fix all mode dependent requirements of the project activities and to generate a random schedule with the serial SGS method. A local search will optimize the sequence of the activities. Also in this paper, a new penalty function has been proposed for solutions which are infeasible with respect to non-renewable resources. Performance of the proposed algorithm has been compared with the best algorithms published so far on the basis of CPU time and number of generated schedules stopping criteria. Reported results indicate excellent performance of the algorithm.

Subspace Barzilai-Borwein Gradient Method for Large-Scale Bound Constrained Optimization

International Nuclear Information System (INIS)

Xiao Yunhai; Hu Qingjie

2008-01-01

An active set subspace Barzilai-Borwein gradient algorithm for large-scale bound constrained optimization is proposed. The active sets are estimated by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the Barzilai-Borwein gradient method. In this work, a nonmonotone line search strategy that guarantees global convergence is used. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known method SPG on a subset of bound constrained problems from CUTEr collection

Analysis of multi cloud storage applications for resource constrained mobile devices

Directory of Open Access Journals (Sweden)

Rajeev Kumar Bedi

2016-09-01

Full Text Available Cloud storage, which can be a surrogate for all physical hardware storage devices, is a term which gives a reflection of an enormous advancement in engineering (Hung et al., 2012. However, there are many issues that need to be handled when accessing cloud storage on resource constrained mobile devices due to inherent limitations of mobile devices as limited storage capacity, processing power and battery backup (Yeo et al., 2014. There are many multi cloud storage applications available, which handle issues faced by single cloud storage applications. In this paper, we are providing analysis of different multi cloud storage applications developed for resource constrained mobile devices to check their performance on the basis of parameters as battery consumption, CPU usage, data usage and time consumed by using mobile phone device Sony Xperia ZL (smart phone on WiFi network. Lastly, conclusion and open research challenges in these multi cloud storage apps are discussed.

PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

Energy Technology Data Exchange (ETDEWEB)

Sen, Satyabrata [ORNL

2014-01-01

We propose a peak-to-average power ratio (PAPR) constrained Pareto-optimal waveform design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system, but also enables us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signaldependent clutter and colored noise. Then, we observe that the resulting STAP-performance can be improved by maximizing the output signal-to-interference-plus-noise ratio (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target scattering responses. Therefore, we formulate a PAPR-constrained multi-objective optimization (MOO) problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramer-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of CRB matrix on the target scattering coefficients estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.

High-level Programming and Symbolic Reasoning on IoT Resource Constrained Devices

Directory of Open Access Journals (Sweden)

Sal vatore Gaglio

2015-05-01

Full Text Available While the vision of Internet of Things (IoT is rather inspiring, its practical implementation remains challenging. Conventional programming approaches prove unsuitable to provide IoT resource constrained devices with the distributed processing capabilities required to implement intelligent, autonomic, and self-organizing behaviors. In our previous work, we had already proposed an alternative programming methodology for such systems that is characterized by high-level programming and symbolic expressions evaluation, and developed a lightweight middleware to support it. Our approach allows for interactive programming of deployed nodes, and it is based on the simple but e ective paradigm of executable code exchange among nodes. In this paper, we show how our methodology can be used to provide IoT resource constrained devices with reasoning abilities by implementing a Fuzzy Logic symbolic extension on deployed nodes at runtime.

Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal

Science.gov (United States)

Steinley, Douglas; Hubert, Lawrence

2008-01-01

This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…

Support for Resource Constrained Microcontroller Programming by a Broad Developer Community

Science.gov (United States)

Amar, Amichi

2010-01-01

Resource constrained microcontrollers with as little as several hundred bytes of RAM and a few dozen megahertz of processing power are the most prevalent computing devices on earth. Microcontrollers and the many application components that interface to them, such as sensors, actuators, transceivers and displays are now cheap and readily available.…

The Resource constrained shortest path problem implemented in a lazy functional language

NARCIS (Netherlands)

Hartel, Pieter H.; Glaser, Hugh

The resource constrained shortest path problem is an NP-hard problem for which many ingenious algorithms have been developed. These algorithms are usually implemented in Fortran or another imperative programming language. We have implemented some of the simpler algorithms in a lazy functional

Cross-training workers in dual resource constrained systems with heterogeneous processing times

NARCIS (Netherlands)

Bokhorst, J. A. C.; Gaalman, G. J. C.

2009-01-01

In this paper, we explore the effect of cross-training workers in Dual Resource Constrained (DRC) systems with machines having different mean processing times. By means of queuing and simulation analysis, we show that the detrimental effects of pooling (cross-training) previously found in single

Optimal utilization of energy resources

Energy Technology Data Exchange (ETDEWEB)

Hudson, E. A.

1977-10-15

General principles that should guide the extraction of New Zealand's energy resources are presented. These principles are based on the objective of promoting the general economic and social benefit obtained from the use of the extracted fuel. For a single resource, the central question to be answered is, simply, what quantity of energy should be extracted in each year of the resource's lifetime. For the energy system as a whole the additional question must be answered of what mix of fuels should be used in any year. The analysis of optimal management of a single energy resource is specifically discussed. The general principles for optimal resource extraction are derived, and then applied to the examination of the characteristics of the optimal time paths of energy quantity and price; to the appraisal of the efficiency, in resource management, of various market structures; to the evaluation of various energy pricing policies; and to the examination of circumstances in which market organization is inefficient and the guidelines for corrective government policy in such cases.

Optimal utilization of energy resources

Energy Technology Data Exchange (ETDEWEB)

Hudson, E.A.

1977-10-15

General principles that should guide the extraction of New Zealand's energy resources are presented. These principles are based on the objective of promoting the general economic and social benefit obtained from the use of the extracted fuel. For a single resource, the central question to be answered is, simply, what quantity of energy should be extracted in each year of the resource's lifetime. For the energy system as a whole the additional question must be answered of what mix of fuels should be used in any year. The analysis of optimal management of a single energy resource is specifically discussed. The general principles for optimal resource extraction are derived, and then applied to the examination of the characteristics of the optimal time paths of energy quantity and price; to the appraisal of the efficiency, in resource management, of various market structures; to the evaluation of various energy pricing policies; and to the examination of circumstances in which market organization is inefficient and the guidelines for corrective government policy in such cases.

Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project

Science.gov (United States)

Xu, Jiuping

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method. PMID:24550708

Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project.

Science.gov (United States)

Xu, Jiuping; Feng, Cuiying

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.

Resource-constrained project scheduling: computing lower bounds by solving minimum cut problems

NARCIS (Netherlands)

Möhring, R.H.; Nesetril, J.; Schulz, A.S.; Stork, F.; Uetz, Marc Jochen

1999-01-01

We present a novel approach to compute Lagrangian lower bounds on the objective function value of a wide class of resource-constrained project scheduling problems. The basis is a polynomial-time algorithm to solve the following scheduling problem: Given a set of activities with start-time dependent

An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

Directory of Open Access Journals (Sweden)

Vivek Patel

2012-08-01

Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

Directory of Open Access Journals (Sweden)

R. Manam

2017-12-01

Full Text Available In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

A first-order multigrid method for bound-constrained convex optimization

Czech Academy of Sciences Publication Activity Database

Kočvara, Michal; Mohammed, S.

2016-01-01

Roč. 31, č. 3 (2016), s. 622-644 ISSN 1055-6788 R&D Projects: GA ČR(CZ) GAP201/12/0671 Grant - others:European Commission - EC(XE) 313781 Institutional support: RVO:67985556 Keywords : bound-constrained optimization * multigrid methods * linear complementarity problems Subject RIV: BA - General Mathematics Impact factor: 1.023, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/kocvara-0460326.pdf

A New Interpolation Approach for Linearly Constrained Convex Optimization

KAUST Repository

Espinoza, Francisco

2012-08-01

In this thesis we propose a new class of Linearly Constrained Convex Optimization methods based on the use of a generalization of Shepard\\'s interpolation formula. We prove the properties of the surface such as the interpolation property at the boundary of the feasible region and the convergence of the gradient to the null space of the constraints at the boundary. We explore several descent techniques such as steepest descent, two quasi-Newton methods and the Newton\\'s method. Moreover, we implement in the Matlab language several versions of the method, particularly for the case of Quadratic Programming with bounded variables. Finally, we carry out performance tests against Matab Optimization Toolbox methods for convex optimization and implementations of the standard log-barrier and active-set methods. We conclude that the steepest descent technique seems to be the best choice so far for our method and that it is competitive with other standard methods both in performance and empirical growth order.

A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Biwei Tang

2016-01-01

Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

Science.gov (United States)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

Stroke in a resource-constrained hospital in Madagascar.

Science.gov (United States)

Stenumgård, Pål Sigurd; Rakotondranaivo, Miadana Joshua; Sletvold, Olav; Follestad, Turid; Ellekjær, Hanne

2017-07-24

Stroke is reported as the most frequent cause of in-hospital death in Madagascar. However, no descriptive data on hospitalized stroke patients in the country have been published. In the present study, we sought to investigate the feasibility of collecting data on stroke patients in a resource-constrained hospital in Madagascar. We also aimed to characterize patients hospitalized with stroke. We registered socio-demographics, clinical characteristics, and early outcomes of patients admitted for stroke between 23 September 2014 and 3 December 2014. We used several validated scales for the evaluation. Stroke severity was measured by the National Institutes of Health Stroke Scale (NIHSS), disability by the modified Rankin Scale (mRS), and function by the Barthel Index (BI). We studied 30 patients. Sixteen were males. The median age was 62.5 years (IQR 58-67). The NIHSS and mRS were completed for all of the patients, and BI was used for the survivors. Three patients received a computed tomography (CT) brain scan. The access to laboratory investigations was limited. Electrocardiographs (ECGs) were not performed. The median NIHSS score was 16.5 (IQR 10-35). The in-hospital stroke mortality was 30%. At discharge, the median mRS score was 5 (IQR 4-6), and the median BI score was 45 (IQR 0-72.5). Although the access to brain imaging and supporting investigations was deficient, this small-scale study suggests that it is feasible to collect essential data on stroke patients in a resource-constrained hospital in Madagascar. Such data should be useful for improving stroke services and planning further research. The hospitalized stroke patients had severe symptoms. The in-hospital stroke mortality was high. At discharge, the disability category was high, and functional status low.

Mixed Integer PDE Constrained Optimization for the Control of a Wildfire Hazard

Science.gov (United States)

2017-01-01

Constrained Optimization for the Control of a Wildfire Hazard Herausgegeben von der Professor fur Angewandte Mathematik Professor Dr. rer. nat. Armin...and H.H. Tan . Finite difference methods for solving the two-dimensional advection-diffusion equation. Int. J. Numer. Meth. Fluids, 9:75-98, 1989. 6

Optimal resource allocation for distributed video communication

CERN Document Server

He, Yifeng

2013-01-01

While most books on the subject focus on resource allocation in just one type of network, this book is the first to examine the common characteristics of multiple distributed video communication systems. Comprehensive and systematic, Optimal Resource Allocation for Distributed Video Communication presents a unified optimization framework for resource allocation across these systems. The book examines the techniques required for optimal resource allocation over Internet, wireless cellular networks, wireless ad hoc networks, and wireless sensor networks. It provides you with the required foundat

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

Science.gov (United States)

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.

Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid

Directory of Open Access Journals (Sweden)

Xiaogang Guo

2018-01-01

Full Text Available In this paper, an adaptively constrained stochastic model predictive control (MPC is proposed to achieve less-conservative coordination between energy storage units and uncertain renewable energy sources (RESs in a microgrid (MG. Besides the economic objective of MG operation, the limits of state-of-charge (SOC and discharging/charging power of the energy storage unit are formulated as chance constraints when accommodating uncertainties of RESs, considering mild violations of these constraints are allowed during long-term operation, and a closed-loop online update strategy is performed to adaptively tighten or relax constraints according to the actual deviation probability of violation level from the desired one as well as the current change rate of deviation probability. Numerical studies show that the proposed adaptively constrained stochastic MPC for MG optimal operation is much less conservative compared with the scenario optimization based robust MPC, and also presents a better convergence performance to the desired constraint violation level than other online update strategies.

A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization

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Zhijun Luo

2014-01-01

Full Text Available A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

Block-triangular preconditioners for PDE-constrained optimization

KAUST Repository

Rees, Tyrone

2010-11-26

In this paper we investigate the possibility of using a block-triangular preconditioner for saddle point problems arising in PDE-constrained optimization. In particular, we focus on a conjugate gradient-type method introduced by Bramble and Pasciak that uses self-adjointness of the preconditioned system in a non-standard inner product. We show when the Chebyshev semi-iteration is used as a preconditioner for the relevant matrix blocks involving the finite element mass matrix that the main drawback of the Bramble-Pasciak method-the appropriate scaling of the preconditioners-is easily overcome. We present an eigenvalue analysis for the block-triangular preconditioners that gives convergence bounds in the non-standard inner product and illustrates their competitiveness on a number of computed examples. Copyright © 2010 John Wiley & Sons, Ltd.

Block-triangular preconditioners for PDE-constrained optimization

KAUST Repository

Rees, Tyrone; Stoll, Martin

2010-01-01

In this paper we investigate the possibility of using a block-triangular preconditioner for saddle point problems arising in PDE-constrained optimization. In particular, we focus on a conjugate gradient-type method introduced by Bramble and Pasciak that uses self-adjointness of the preconditioned system in a non-standard inner product. We show when the Chebyshev semi-iteration is used as a preconditioner for the relevant matrix blocks involving the finite element mass matrix that the main drawback of the Bramble-Pasciak method-the appropriate scaling of the preconditioners-is easily overcome. We present an eigenvalue analysis for the block-triangular preconditioners that gives convergence bounds in the non-standard inner product and illustrates their competitiveness on a number of computed examples. Copyright © 2010 John Wiley & Sons, Ltd.

An L∞/L1-Constrained Quadratic Optimization Problem with Applications to Neural Networks

International Nuclear Information System (INIS)

Leizarowitz, Arie; Rubinstein, Jacob

2003-01-01

Pattern formation in associative neural networks is related to a quadratic optimization problem. Biological considerations imply that the functional is constrained in the L ∞ norm and in the L 1 norm. We consider such optimization problems. We derive the Euler-Lagrange equations, and construct basic properties of the maximizers. We study in some detail the case where the kernel of the quadratic functional is finite-dimensional. In this case the optimization problem can be fully characterized by the geometry of a certain convex and compact finite-dimensional set

Resource-constrained project scheduling problem: review of past and recent developments

Directory of Open Access Journals (Sweden)

Farhad Habibi

2018-01-01

Full Text Available The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the in-teresting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Re-source-Constrained Project Scheduling Problem (RCPSP are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four as-pects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previ-ous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem

Directory of Open Access Journals (Sweden)

Yongyi Shou

2014-01-01

Full Text Available A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem.

Workshop on Computational Optimization

CERN Document Server

2015-01-01

Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many real world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2013. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, resource constrained project scheduling, problems arising in transport services, error correcting codes, optimal system performance and energy consumption and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others.

The Ha Noi Expert Statement: recognition of maternal mental health in resource-constrained settings is essential for achieving the Millennium Development Goals

Directory of Open Access Journals (Sweden)

Izutsu Takashi

2011-01-01

Full Text Available Abstract Mental health problems in women during pregnancy and after childbirth and their adverse consequences for child health and development have received sustained detailed attention in high-income countries. In contrast, evidence has only been generated more recently in resource-constrained settings. In June 2007 the United Nations Population Fund, the World Health Organization, the Key Centre for Women's Health in Society, a WHO Collaborating Centre for Women's Health and the Research and Training Centre for Community Development in Vietnam convened the first international expert meeting on maternal mental health and child health and development in resource-constrained settings. It aimed to appraise the evidence about the nature, prevalence and risks for common perinatal mental disorders in women; the consequences of these for child health and development and ameliorative strategies in these contexts. The substantial disparity in rates of perinatal mental disorders between women living in high- and low-income settings, suggests social rather than biological determinants. Risks in resource-constrained contexts include: poverty; crowded living situations; limited reproductive autonomy; unintended pregnancy; lack of empathy from the intimate partner; rigid gender stereotypes about responsibility for household work and infant care; family violence; poor physical health and discrimination. Development is adversely affected if infants lack day-to-day interactions with a caregiver who can interpret their cues, and respond effectively. Women with compromised mental health are less able to provide sensitive, responsive infant care. In resource-constrained settings infants whose mothers are depressed are less likely to thrive and to receive optimal care than those whose mothers are well. The meeting outcome is the Hanoi Expert Statement (Additional file 1. It argues that the Millennium Development Goals to improve maternal health, reduce child

Factors that influence m-health implementations in resource constrained areas in the developing world

CSIR Research Space (South Africa)

Ouma, S

2011-11-01

Full Text Available the primary healthcare levels in order to improve the delivery of services within various communities. They further provide the issues that the mhealth service providers should take into account when providing m-health solutions to the resource constrained...

Preventive Security-Constrained Optimal Power Flow Considering UPFC Control Modes

Directory of Open Access Journals (Sweden)

Xi Wu

2017-08-01

Full Text Available The successful application of the unified power flow controller (UPFC provides a new control method for the secure and economic operation of power system. In order to make the full use of UPFC and improve the economic efficiency and static security of a power system, a preventive security-constrained power flow optimization method considering UPFC control modes is proposed in this paper. Firstly, an iterative method considering UPFC control modes is deduced for power flow calculation. Taking into account the influence of different UPFC control modes on the distribution of power flow after N-1 contingency, the optimization model is then constructed by setting a minimal system operation cost and a maximum static security margin as the objective. Based on this model, the particle swarm optimization (PSO algorithm is utilized to optimize power system operating parameters and UPFC control modes simultaneously. Finally, a standard IEEE 30-bus system is utilized to demonstrate that the proposed method fully exploits the potential of static control of UPFC and significantly increases the economic efficiency and static security of the power system.

Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems

Czech Academy of Sciences Publication Activity Database

Axelsson, Owe; Farouq, S.; Neytcheva, M.

2017-01-01

Roč. 74, č. 1 (2017), s. 19-37 ISSN 1017-1398 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution methods * preconditioning Subject RIV: BA - General Mathematics OBOR OECD: Applied mathematics Impact factor: 1.241, year: 2016 https://link.springer.com/article/10.1007%2Fs11075-016-0136-5

The Ha Noi Expert Statement: recognition of maternal mental health in resource-constrained settings is essential for achieving the Millennium Development Goals.

Science.gov (United States)

Fisher, Jane Rw; de Mello, Meena Cabral; Izutsu, Takashi; Tran, Tuan

2011-01-07

Mental health problems in women during pregnancy and after childbirth and their adverse consequences for child health and development have received sustained detailed attention in high-income countries. In contrast, evidence has only been generated more recently in resource-constrained settings.In June 2007 the United Nations Population Fund, the World Health Organization, the Key Centre for Women's Health in Society, a WHO Collaborating Centre for Women's Health and the Research and Training Centre for Community Development in Vietnam convened the first international expert meeting on maternal mental health and child health and development in resource-constrained settings. It aimed to appraise the evidence about the nature, prevalence and risks for common perinatal mental disorders in women; the consequences of these for child health and development and ameliorative strategies in these contexts.The substantial disparity in rates of perinatal mental disorders between women living in high- and low-income settings, suggests social rather than biological determinants. Risks in resource-constrained contexts include: poverty; crowded living situations; limited reproductive autonomy; unintended pregnancy; lack of empathy from the intimate partner; rigid gender stereotypes about responsibility for household work and infant care; family violence; poor physical health and discrimination. Development is adversely affected if infants lack day-to-day interactions with a caregiver who can interpret their cues, and respond effectively. Women with compromised mental health are less able to provide sensitive, responsive infant care. In resource-constrained settings infants whose mothers are depressed are less likely to thrive and to receive optimal care than those whose mothers are well.The meeting outcome is the Hanoi Expert Statement (Additional file 1). It argues that the Millennium Development Goals to improve maternal health, reduce child mortality, promote gender equality

Characteristics and critical success factors for implementing problem-based learning in a human resource-constrained country.

Science.gov (United States)

Giva, Karen R N; Duma, Sinegugu E

2015-08-31

Problem-based learning (PBL) was introduced in Malawi in 2002 in order to improve the nursing education system and respond to the acute nursing human resources shortage. However, its implementation has been very slow throughout the country. The objectives of the study were to explore and describe the goals that were identified by the college to facilitate the implementation of PBL, the resources of the organisation that facilitated the implementation of PBL, the factors related to sources of students that facilitated the implementation of PBL, and the influence of the external system of the organisation on facilitating the implementation of PBL, and to identify critical success factors that could guide the implementation of PBL in nursing education in Malawi. This is an ethnographic, exploratory and descriptive qualitative case study. Purposive sampling was employed to select the nursing college, participants and documents for review.Three data collection methods, including semi-structured interviews, participant observation and document reviews, were used to collect data. The four steps of thematic analysis were used to analyse data from all three sources. Four themes and related subthemes emerged from the triangulated data sources. The first three themes and their subthemes are related to the characteristics related to successful implementation of PBL in a human resource-constrained nursing college, whilst the last theme is related to critical success factors that contribute to successful implementation of PBL in a human resource-constrained country like Malawi. This article shows that implementation of PBL is possible in a human resource-constrained country if there is political commitment and support.

Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

DEFF Research Database (Denmark)

Wang, Yong; Cai, Zixing; Zhou, Yuren

2009-01-01

A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems

Czech Academy of Sciences Publication Activity Database

Axelsson, Owe; Farouq, S.; Neytcheva, M.

2017-01-01

Roč. 74, č. 1 (2017), s. 19-37 ISSN 1017-1398 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution method s * preconditioning Subject RIV: BA - General Mathematics OBOR OECD: Applied mathematics Impact factor: 1.241, year: 2016 https://link.springer.com/article/10.1007%2Fs11075-016-0136-5

Workshop on Computational Optimization

CERN Document Server

2016-01-01

This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2014, held at Warsaw, Poland, September 7-10, 2014. The book presents recent advances in computational optimization. The volume includes important real problems like parameter settings for controlling processes in bioreactor and other processes, resource constrained project scheduling, infection distribution, molecule distance geometry, quantum computing, real-time management and optimal control, bin packing, medical image processing, localization the abrupt atmospheric contamination source and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks.

Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization

Science.gov (United States)

Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich

2018-05-01

Application of the multi-arm space robot will be more effective than single arm especially when the target is tumbling. This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode. In order to overcome the dynamics singularities issue, the direct kinematics equations in conjunction with constrained PSO are employed for coordinated trajectory planning of dual-arm space robot. The joint trajectories are parametrized with Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for coordinated trajectory planning of two kinematically redundant manipulators mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

Science.gov (United States)

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

Science.gov (United States)

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

SmartFix: Indoor Locating Optimization Algorithm for Energy-Constrained Wearable Devices

Directory of Open Access Journals (Sweden)

Xiaoliang Wang

2017-01-01

Full Text Available Indoor localization technology based on Wi-Fi has long been a hot research topic in the past decade. Despite numerous solutions, new challenges have arisen along with the trend of smart home and wearable computing. For example, power efficiency needs to be significantly improved for resource-constrained wearable devices, such as smart watch and wristband. For a Wi-Fi-based locating system, most of the energy consumption can be attributed to real-time radio scan; however, simply reducing radio data collection will cause a serious loss of locating accuracy because of unstable Wi-Fi signals. In this paper, we present SmartFix, an optimization algorithm for indoor locating based on Wi-Fi RSS. SmartFix utilizes user motion features, extracts characteristic value from history trajectory, and corrects deviation caused by unstable Wi-Fi signals. We implemented a prototype of SmartFix both on Moto 360 2nd-generation Smartwatch and on HTC One Smartphone. We conducted experiments both in a large open area and in an office hall. Experiment results demonstrate that average locating error is less than 2 meters for more than 80% cases, and energy consumption is only 30% of Wi-Fi fingerprinting method under the same experiment circumstances.

Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics

Directory of Open Access Journals (Sweden)

Xi Wang

2016-01-01

Full Text Available We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs. Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.

Optimal natural resources management under uncertainty with catastrophic risk

Energy Technology Data Exchange (ETDEWEB)

Motoh, Tsujimura [Graduate School of Economics, Kyoto University, Yoshida-honmochi, Sakyo-ku, Kyoto 606-8501 (Japan)

2004-05-01

We examine an optimal natural resources management problem under uncertainty with catastrophic risk and investigate the optimal rate of use of a natural resource. For this purpose, we use stochastic control theory. We assume that, until a catastrophic event occurs, the stock of the natural resource is governed by a stochastic differential equation. We describe the catastrophic phenomenon as a Poisson process. From this analysis, we show the optimal rate of use of the natural resource in explicit form. Furthermore, we present comparative static results for the optimal rate of use of the natural resource.

Optimal natural resources management under uncertainty with catastrophic risk

International Nuclear Information System (INIS)

Motoh, Tsujimura

2004-01-01

We examine an optimal natural resources management problem under uncertainty with catastrophic risk and investigate the optimal rate of use of a natural resource. For this purpose, we use stochastic control theory. We assume that, until a catastrophic event occurs, the stock of the natural resource is governed by a stochastic differential equation. We describe the catastrophic phenomenon as a Poisson process. From this analysis, we show the optimal rate of use of the natural resource in explicit form. Furthermore, we present comparative static results for the optimal rate of use of the natural resource

Order-constrained linear optimization.

Science.gov (United States)

Tidwell, Joe W; Dougherty, Michael R; Chrabaszcz, Jeffrey S; Thomas, Rick P

2017-11-01

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data. © 2017 The British Psychological Society.

Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation.

Science.gov (United States)

Torres-González, Arturo; Martínez-de Dios, Jose Ramiro; Ollero, Anibal

2017-04-20

This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the technological constraints of the robot and beacons, many applications impose further resource consumption limitations. This paper presents a scalable distributed RO-SLAM scheme for resource-constrained operation. It is capable of exploiting robot-beacon cooperation in order to improve SLAM accuracy while meeting a given resource consumption bound expressed as the maximum number of measurements that are integrated in SLAM per iteration. The proposed scheme combines a Sparse Extended Information Filter (SEIF) SLAM method, in which each beacon gathers and integrates robot-beacon and inter-beacon measurements, and a distributed information-driven measurement allocation tool that dynamically selects the measurements that are integrated in SLAM, balancing uncertainty improvement and resource consumption. The scheme adopts a robot-beacon distributed approach in which each beacon participates in the selection, gathering and integration in SLAM of robot-beacon and inter-beacon measurements, resulting in significant estimation accuracies, resource-consumption efficiency and scalability. It has been integrated in an octorotor Unmanned Aerial System (UAS) and evaluated in 3D SLAM outdoor experiments. The experimental results obtained show its performance and robustness and evidence its advantages over existing methods.

Optimal resource states for local state discrimination

Science.gov (United States)

Bandyopadhyay, Somshubhro; Halder, Saronath; Nathanson, Michael

2018-02-01

We study the problem of locally distinguishing pure quantum states using shared entanglement as a resource. For a given set of locally indistinguishable states, we define a resource state to be useful if it can enhance local distinguishability and optimal if it can distinguish the states as well as global measurements and is also minimal with respect to a partial ordering defined by entanglement and dimension. We present examples of useful resources and show that an entangled state need not be useful for distinguishing a given set of states. We obtain optimal resources with explicit local protocols to distinguish multipartite Greenberger-Horne-Zeilinger and graph states and also show that a maximally entangled state is an optimal resource under one-way local operations and classical communication to distinguish any bipartite orthonormal basis which contains at least one entangled state of full Schmidt rank.

Incorporating a Constrained Optimization Algorithm into Remote- Sensing/Precision Agriculture Methodology

Science.gov (United States)

Morgenthaler, George; Khatib, Nader; Kim, Byoungsoo

with information to improve their crop's vigor has been a major topic of interest. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, the efficiency of farming must increase to meet future food requirements and to make farming a sustainable occupation for the farmer. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The goal is to increase farm revenue by increasing crop yield and decreasing applications of costly chemical and water treatments. In addition, this methodology will decrease the environmental costs of farming, i.e., reduce air, soil, and water pollution. Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now available. Commercial satellite systems can image (multi-spectral) the Earth with a resolution of approximately 2.5 m. Variable precision dispensing systems using GPS are available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been formulated. Personal computers and internet access are in place in most farm homes and can provide a mechanism to periodically disseminate, e.g. bi-weekly, advice on what quantities of water and chemicals are needed in individual regions of the field. What is missing is a model that fuses the disparate sources of information on the current states of the crop and soil, and the remaining resource levels available with the decisions farmers are required to make. This must be a product that is easy for the farmer to understand and to implement. A "Constrained Optimization Feed-back Control Model" to fill this void will be presented. The objective function of the model will be used to maximize the farmer's profit by increasing yields while decreasing environmental costs and decreasing

Incorporating a constrained optimization algorithm into remote sensing/precision agriculture methodology

Science.gov (United States)

Moreenthaler, George W.; Khatib, Nader; Kim, Byoungsoo

2003-08-01

For two decades now, the use of Remote Sensing/Precision Agriculture to improve farm yields while reducing the use of polluting chemicals and the limited water supply has been a major goal. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, farm efficiency must increase to meet future food requirements and to make farming a sustainable, profitable occupation. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The real goal is to increase farm profitability by identifying the additional treatments of chemicals and water that increase revenues more than they increase costs and do no exceed pollution standards (constrained optimization). Even though the economic and environmental benefits appear to be great, Remote Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now in place, but other needed factors have been missing. Commercial satellite systems can now image the Earth (multi-spectrally) with a resolution as fine as 2.5 m. Precision variable dispensing systems using GPS are now available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been developed. Personal computers and internet access are now in place in most farm homes and can provide a mechanism for periodically disseminating advice on what quantities of water and chemicals are needed in specific regions of each field. Several processes have been selected that fuse the disparate sources of information on the current and historic states of the crop and soil, and the remaining resource levels available, with the critical decisions that farmers are required to make. These are done in a way that is easy for the farmer to understand and profitable to implement. A "Constrained

A First-order Prediction-Correction Algorithm for Time-varying (Constrained) Optimization: Preprint

Energy Technology Data Exchange (ETDEWEB)

Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Simonetto, Andrea [Universite catholique de Louvain

2017-07-25

This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are established to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.

Heuristic algorithm for single resource constrained project scheduling problem based on the dynamic programming

Directory of Open Access Journals (Sweden)

Stanimirović Ivan

2009-01-01

Full Text Available We introduce a heuristic method for the single resource constrained project scheduling problem, based on the dynamic programming solution of the knapsack problem. This method schedules projects with one type of resources, in the non-preemptive case: once started an activity is not interrupted and runs to completion. We compare the implementation of this method with well-known heuristic scheduling method, called Minimum Slack First (known also as Gray-Kidd algorithm, as well as with Microsoft Project.

A HYBRID HEURISTIC ALGORITHM FOR SOLVING THE RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM (RCPSP

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Juan Carlos Rivera

Full Text Available The Resource Constrained Project Scheduling Problem (RCPSP is a problem of great interest for the scientific community because it belongs to the class of NP-Hard problems and no methods are known that can solve it accurately in polynomial processing times. For this reason heuristic methods are used to solve it in an efficient way though there is no guarantee that an optimal solution can be obtained. This research presents a hybrid heuristic search algorithm to solve the RCPSP efficiently, combining elements of the heuristic Greedy Randomized Adaptive Search Procedure (GRASP, Scatter Search and Justification. The efficiency obtained is measured taking into account the presence of the new elements added to the GRASP algorithm taken as base: Justification and Scatter Search. The algorithms are evaluated using three data bases of instances of the problem: 480 instances of 30 activities, 480 of 60, and 600 of 120 activities respectively, taken from the library PSPLIB available online. The solutions obtained by the developed algorithm for the instances of 30, 60 and 120 are compared with results obtained by other researchers at international level, where a prominent place is obtained, according to Chen (2011.

Genetic algorithm parameters tuning for resource-constrained project scheduling problem

Science.gov (United States)

Tian, Xingke; Yuan, Shengrui

2018-04-01

Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

Optimal defense resource allocation in scale-free networks

Science.gov (United States)

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.

Conducting research in a resource-constrained environment: avoiding the pitfalls

Directory of Open Access Journals (Sweden)

Janine I. Munsamy

2014-03-01

Full Text Available Practical challenges affected the conducting of a retrospective drug use evaluation (DUE on the rational use of tenofovir in a resourceconstrained South African Antiretroviral Treatment Programme. The primary outcome measure was the percentage of patient records compliant with DUE criteria using initiation prescriptions from March 2009 to February 2010. Health system challenges encountered included stringent institutional administrative procedures, lack of efficient communication channels, reliance on overburdened personnel and fear of audit. Forty percent (222 of 556 of patient records identified for inclusion in the study had to be excluded, mainly due to poor record keeping. Research budgetary constraints also limited data collection. This experience highlighted real, unforeseen challenges when conducting a retrospective study in a resource-constrained environment. A sound understanding of the environment and adequate preparation is recommended. The lessons learnt may prove valuable to both firsttime and experienced researchers in a resource-limited setting using a similar methodology.

A simple two stage optimization algorithm for constrained power economic dispatch

International Nuclear Information System (INIS)

Huang, G.; Song, K.

1994-01-01

A simple two stage optimization algorithm is proposed and investigated for fast computation of constrained power economic dispatch control problems. The method is a simple demonstration of the hierarchical aggregation-disaggregation (HAD) concept. The algorithm first solves an aggregated problem to obtain an initial solution. This aggregated problem turns out to be classical economic dispatch formulation, and it can be solved in 1% of overall computation time. In the second stage, linear programming method finds optimal solution which satisfies power balance constraints, generation and transmission inequality constraints and security constraints. Implementation of the algorithm for IEEE systems and EPRI Scenario systems shows that the two stage method obtains average speedup ratio 10.64 as compared to classical LP-based method

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

Science.gov (United States)

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Breaking down the barriers of using strong authentication and encryption in resource constrained embedded systems

Science.gov (United States)

Knobler, Ron; Scheffel, Peter; Jackson, Scott; Gaj, Kris; Kaps, Jens Peter

2013-05-01

Various embedded systems, such as unattended ground sensors (UGS), are deployed in dangerous areas, where they are subject to compromise. Since numerous systems contain a network of devices that communicate with each other (often times with commercial off the shelf [COTS] radios), an adversary is able to intercept messages between system devices, which jeopardizes sensitive information transmitted by the system (e.g. location of system devices). Secret key algorithms such as AES are a very common means to encrypt all system messages to a sufficient security level, for which lightweight implementations exist for even very resource constrained devices. However, all system devices must use the appropriate key to encrypt and decrypt messages from each other. While traditional public key algorithms (PKAs), such as RSA and Elliptic Curve Cryptography (ECC), provide a sufficiently secure means to provide authentication and a means to exchange keys, these traditional PKAs are not suitable for very resource constrained embedded systems or systems which contain low reliability communication links (e.g. mesh networks), especially as the size of the network increases. Therefore, most UGS and other embedded systems resort to pre-placed keys (PPKs) or other naïve schemes which greatly reduce the security and effectiveness of the overall cryptographic approach. McQ has teamed with the Cryptographic Engineering Research Group (CERG) at George Mason University (GMU) to develop an approach using revolutionary cryptographic techniques that provides both authentication and encryption, but on resource constrained embedded devices, without the burden of large amounts of key distribution or storage.

Constrained Optimization Problems in Cost and Managerial Accounting--Spreadsheet Tools

Science.gov (United States)

Amlie, Thomas T.

2009-01-01

A common problem addressed in Managerial and Cost Accounting classes is that of selecting an optimal production mix given scarce resources. That is, if a firm produces a number of different products, and is faced with scarce resources (e.g., limitations on labor, materials, or machine time), what combination of products yields the greatest profit…

Optimal dispatch in dynamic security constrained open power market

International Nuclear Information System (INIS)

Singh, S.N.; David, A.K.

2002-01-01

Power system security is a new concern in the competitive power market operation, because the integration of the system controller and the generation owner has been broken. This paper presents an approach for dynamic security constrained optimal dispatch in restructured power market environment. The transient energy margin using transient energy function (TEF) approach has been used to calculate the stability margin of the system and a hybrid method is applied to calculate the approximate unstable equilibrium point (UEP) that is used to calculate the exact UEP and thus, the energy margin using TEF. The case study results illustrated on two systems shows that the operating mechanisms are compatible with the new business environment. (author)

Application of a Double-Sided Chance-Constrained Integer Linear Program for Optimization of the Incremental Value of Ecosystem Services in Jilin Province, China

Directory of Open Access Journals (Sweden)

Baofeng Cai

2017-08-01

Full Text Available The Interconnected River System Network Project (IRSNP is a significant water supply engineering project, which is capable of effectively utilizing flood resources to generate ecological value, by connecting 198 lakes and ponds in western Jilin, northeast China. In this article, an optimization research approach has been proposed to maximize the incremental value of IRSNP ecosystem services. A double-sided chance-constrained integer linear program (DCCILP method has been proposed to support the optimization, which can deal with uncertainties presented as integers or random parameters that appear on both sides of the decision variable at the same time. The optimal scheme indicates that after rational optimization, the total incremental value of ecosystem services from the interconnected river system network project increased 22.25%, providing an increase in benefits of 3.26 × 109 ¥ compared to the original scheme. Most of the functional area is swamp wetland, which provides the greatest ecological benefits. Adjustment services increased obviously, implying that the optimization scheme prioritizes ecological benefits rather than supply and production services.

An Optimization Framework for Dynamic, Distributed Real-Time Systems

Science.gov (United States)

Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara

2003-01-01

Abstract. This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.

Robust and Reliable Portfolio Optimization Formulation of a Chance Constrained Problem

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Sengupta Raghu Nandan

2017-02-01

Full Text Available We solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO method wherein financial script/asset loss return distributions are considered as extreme valued. The objective function is a convex combination of portfolio’s CVaR and expected value of loss return, subject to a set of randomly perturbed chance constraints with specified probability values. The robust deterministic counterpart of the model takes the form of Second Order Cone Programming (SOCP problem. Results from extensive simulation runs show the efficacy of our proposed models, as it helps the investor to (i utilize extensive simulation studies to draw insights into the effect of randomness in portfolio decision making process, (ii incorporate different risk appetite scenarios to find the optimal solutions for the financial portfolio allocation problem and (iii compare the risk and return profiles of the investments made in both deterministic as well as in uncertain and highly volatile financial markets.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

Science.gov (United States)

Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

2017-01-25

Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open

Resource-Constrained Optimal Scheduling of Synchronous Dataflow Graphs via Timed Automata

NARCIS (Netherlands)

Ahmad, W.; de Groote, Robert; Holzenspies, P.K.F.; Stoelinga, Mariëlle Ida Antoinette; van de Pol, Jan Cornelis

Synchronous dataflow (SDF) graphs are a widely used formalism for modelling, analysing and realising streaming applications, both on a single processor and in a multiprocessing context. Efficient schedules are essential to obtain maximal throughput under the constraint of available resources. This

Optimization of the box-girder of overhead crane with constrained ...

African Journals Online (AJOL)

haroun

Keywords: Overhead crane - Box-girder - New bat algorithm - level of ... much more efficiency and robustness compared to the genetic algorithm (GA) and PSO ...... optimization: developments, applications and resources," in Evolutionary.

Stability Constrained Efficiency Optimization for Droop Controlled DC-DC Conversion System

DEFF Research Database (Denmark)

Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.

2013-01-01

implementing tertiary regulation. Moreover, system dynamic is affected when shifting VRs. Therefore, the stability is considered in optimization by constraining the eigenvalues arising from dynamic state space model of the system. Genetic algorithm is used in searching for global efficiency optimum while....... As the efficiency of each converter changes with output power, virtual resistances (VRs) are set as decision variables for adjusting power sharing proportion among converters. It is noteworthy that apart from restoring the voltage deviation, secondary control plays an important role to stabilize dc bus voltage when...

Depletion mapping and constrained optimization to support managing groundwater extraction

Science.gov (United States)

Fienen, Michael N.; Bradbury, Kenneth R.; Kniffin, Maribeth; Barlow, Paul M.

2018-01-01

Groundwater models often serve as management tools to evaluate competing water uses including ecosystems, irrigated agriculture, industry, municipal supply, and others. Depletion potential mapping—showing the model-calculated potential impacts that wells have on stream baseflow—can form the basis for multiple potential management approaches in an oversubscribed basin. Specific management approaches can include scenarios proposed by stakeholders, systematic changes in well pumping based on depletion potential, and formal constrained optimization, which can be used to quantify the tradeoff between water use and stream baseflow. Variables such as the maximum amount of reduction allowed in each well and various groupings of wells using, for example, K-means clustering considering spatial proximity and depletion potential are considered. These approaches provide a potential starting point and guidance for resource managers and stakeholders to make decisions about groundwater management in a basin, spreading responsibility in different ways. We illustrate these approaches in the Little Plover River basin in central Wisconsin, United States—home to a rich agricultural tradition, with farmland and urban areas both in close proximity to a groundwater-dependent trout stream. Groundwater withdrawals have reduced baseflow supplying the Little Plover River below a legally established minimum. The techniques in this work were developed in response to engaged stakeholders with various interests and goals for the basin. They sought to develop a collaborative management plan at a watershed scale that restores the flow rate in the river in a manner that incorporates principles of shared governance and results in effective and minimally disruptive changes in groundwater extraction practices.

Fast optimization of statistical potentials for structurally constrained phylogenetic models

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Rodrigue Nicolas

2009-09-01

Full Text Available Abstract Background Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained (SC models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms. Results Here, we develop an alternative optimization procedure, based on a leave-one-out argument coupled to fast gradient descent algorithms. We assess that the leave-one-out potential yields very similar results to the joint approach developed previously, both in terms of the resulting potential parameters, and by Bayes factor evaluation in a phylogenetic context. On the other hand, the leave-one-out approach results in a considerable computational benefit (up to a 1,000 fold decrease in computational time for the optimization procedure. Conclusion Due to its computational speed, the optimization method we propose offers an attractive alternative for the design and empirical evaluation of alternative forms of potentials, using large data sets and high-dimensional parameterizations.

Generation and reserve dispatch in a competitive market using constrained particle swarm optimization

International Nuclear Information System (INIS)

Azadani, E. Nasr; Hosseinian, S.H.; Moradzadeh, B.

2010-01-01

Competitive bidding for energy and ancillary services is increasingly recognized as an important part of electricity markets. In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constraint handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive electricity market considering the system constraints. The proposed technique shows promising performance for smooth and non smooth cost function as well. Three different systems are examined to demonstrate the effectiveness and the accuracy of the proposed algorithm. (author)

Review of dynamic optimization methods in renewable natural resource management

Science.gov (United States)

Williams, B.K.

1989-01-01

In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. The applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.

Pricing Resources in LTE Networks through Multiobjective Optimization

Science.gov (United States)

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.

Science.gov (United States)

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.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

Science.gov (United States)

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Topology Optimization for Minimizing the Resonant Response of Plates with Constrained Layer Damping Treatment

Directory of Open Access Journals (Sweden)

Zhanpeng Fang

2015-01-01

Full Text Available A topology optimization method is proposed to minimize the resonant response of plates with constrained layer damping (CLD treatment under specified broadband harmonic excitations. The topology optimization problem is formulated and the square of displacement resonant response in frequency domain at the specified point is considered as the objective function. Two sensitivity analysis methods are investigated and discussed. The derivative of modal damp ratio is not considered in the conventional sensitivity analysis method. An improved sensitivity analysis method considering the derivative of modal damp ratio is developed to improve the computational accuracy of the sensitivity. The evolutionary structural optimization (ESO method is used to search the optimal layout of CLD material on plates. Numerical examples and experimental results show that the optimal layout of CLD treatment on the plate from the proposed topology optimization using the conventional sensitivity analysis or the improved sensitivity analysis can reduce the displacement resonant response. However, the optimization method using the improved sensitivity analysis can produce a higher modal damping ratio than that using the conventional sensitivity analysis and develop a smaller displacement resonant response.

Pareto-optimal estimates that constrain mean California precipitation change

Science.gov (United States)

Langenbrunner, B.; Neelin, J. D.

2017-12-01

Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

SU-E-I-23: A General KV Constrained Optimization of CNR for CT Abdominal Imaging

International Nuclear Information System (INIS)

Weir, V; Zhang, J

2015-01-01

Purpose: While Tube current modulation has been well accepted for CT dose reduction, kV adjusting in clinical settings is still at its early stage. This is mainly due to the limited kV options of most current CT scanners. kV adjusting can potentially reduce radiation dose and optimize image quality. This study is to optimize CT abdomen imaging acquisition based on the assumption of a continuous kV, with the goal to provide the best contrast to noise ratio (CNR). Methods: For a given dose (CTDIvol) level, the CNRs at different kV and pitches were measured with an ACR GAMMEX phantom. The phantom was scanned in a Siemens Sensation 64 scanner and a GE VCT 64 scanner. A constrained mathematical optimization was used to find the kV which led to the highest CNR for the anatomy and pitch setting. Parametric equations were obtained from polynomial fitting of plots of kVs vs CNRs. A suitable constraint region for optimization was chosen. Subsequent optimization yielded a peak CNR at a particular kV for different collimations and pitch setting. Results: The constrained mathematical optimization approach yields kV of 114.83 and 113.46, with CNRs of 1.27 and 1.11 at the pitch of 1.2 and 1.4, respectively, for the Siemens Sensation 64 scanner with the collimation of 32 x 0.625mm. An optimized kV of 134.25 and 1.51 CNR is obtained for a GE VCT 64 slice scanner with a collimation of 32 x 0.625mm and a pitch of 0.969. At 0.516 pitch and 32 x 0.625 mm an optimized kV of 133.75 and a CNR of 1.14 was found for the GE VCT 64 slice scanner. Conclusion: CNR in CT image acquisition can be further optimized with a continuous kV option instead of current discrete or fixed kV settings. A continuous kV option is a key for individualized CT protocols

SU-E-I-23: A General KV Constrained Optimization of CNR for CT Abdominal Imaging

Energy Technology Data Exchange (ETDEWEB)

Weir, V; Zhang, J [University of Kentucky, Lexington, KY (United States)

2015-06-15

Purpose: While Tube current modulation has been well accepted for CT dose reduction, kV adjusting in clinical settings is still at its early stage. This is mainly due to the limited kV options of most current CT scanners. kV adjusting can potentially reduce radiation dose and optimize image quality. This study is to optimize CT abdomen imaging acquisition based on the assumption of a continuous kV, with the goal to provide the best contrast to noise ratio (CNR). Methods: For a given dose (CTDIvol) level, the CNRs at different kV and pitches were measured with an ACR GAMMEX phantom. The phantom was scanned in a Siemens Sensation 64 scanner and a GE VCT 64 scanner. A constrained mathematical optimization was used to find the kV which led to the highest CNR for the anatomy and pitch setting. Parametric equations were obtained from polynomial fitting of plots of kVs vs CNRs. A suitable constraint region for optimization was chosen. Subsequent optimization yielded a peak CNR at a particular kV for different collimations and pitch setting. Results: The constrained mathematical optimization approach yields kV of 114.83 and 113.46, with CNRs of 1.27 and 1.11 at the pitch of 1.2 and 1.4, respectively, for the Siemens Sensation 64 scanner with the collimation of 32 x 0.625mm. An optimized kV of 134.25 and 1.51 CNR is obtained for a GE VCT 64 slice scanner with a collimation of 32 x 0.625mm and a pitch of 0.969. At 0.516 pitch and 32 x 0.625 mm an optimized kV of 133.75 and a CNR of 1.14 was found for the GE VCT 64 slice scanner. Conclusion: CNR in CT image acquisition can be further optimized with a continuous kV option instead of current discrete or fixed kV settings. A continuous kV option is a key for individualized CT protocols.

Mini-batch optimized full waveform inversion with geological constrained gradient filtering

Science.gov (United States)

Yang, Hui; Jia, Junxiong; Wu, Bangyu; Gao, Jinghuai

2018-05-01

High computation cost and generating solutions without geological sense have hindered the wide application of Full Waveform Inversion (FWI). Source encoding technique is a way to dramatically reduce the cost of FWI but subject to fix-spread acquisition setup requirement and slow convergence for the suppression of cross-talk. Traditionally, gradient regularization or preconditioning is applied to mitigate the ill-posedness. An isotropic smoothing filter applied on gradients generally gives non-geological inversion results, and could also introduce artifacts. In this work, we propose to address both the efficiency and ill-posedness of FWI by a geological constrained mini-batch gradient optimization method. The mini-batch gradient descent optimization is adopted to reduce the computation time by choosing a subset of entire shots for each iteration. By jointly applying the structure-oriented smoothing to the mini-batch gradient, the inversion converges faster and gives results with more geological meaning. Stylized Marmousi model is used to show the performance of the proposed method on realistic synthetic model.

Electrochemomechanical constrained multiobjective optimization of PPy/MWCNT actuators

International Nuclear Information System (INIS)

Khalili, N; Naguib, H E; Kwon, R H

2014-01-01

Polypyrrole (PPy) conducting polymers have shown a great potential for the fabrication of conjugated polymer-based actuating devices. Consequently, they have been a key point in developing many advanced emerging applications such as biomedical devices and biomimetic robotics. When designing an actuator, taking all of the related decision variables, their roles and relationships into consideration is of pivotal importance to determine the actuator’s final performance. Therefore, the central focus of this study is to develop an electrochemomechanical constrained multiobjective optimization model of a PPy/MWCNTs trilayer actuator. For this purpose, the objective functions are designed to capture the three main characteristics of these actuators, namely their tip vertical displacement, blocking force and response time. To obtain the optimum range of the designated decision variables within the feasible domain, a multiobjective optimization algorithm is applied while appropriate constraints are imposed. The optimum points form a Pareto surface on which they are consistently spread. The numerical results are presented; these results enable one to design an actuator with consideration to the desired output performances. For the experimental analysis, a multilayer bending-type actuator is fabricated, which is composed of a PVDF layer and two layers of PPy with an incorporated layer of multi-walled carbon nanotubes deposited on each side of the PVDF membrane. The numerical results are experimentally verified; in order to determine the performance of the fabricated actuator, its outputs are compared with a neat PPy actuator’s experimental and numerical counterparts. (paper)

Indoor climate optimization with limited resources

DEFF Research Database (Denmark)

Santos, A.; Gunnarsen, Lars Bo

This report presents experimental data and models for optimisation of the indoor climate parameters temperature, noise, draught and window opening. Results are based on experiments with human subjects performed in climate chambers at University of the Philippines. The report may assist building...... designers to balance attention and resources between the parameters of the indoor climate when resources are less than optimal....

Constrained multi-objective optimization of radial expanders in organic Rankine cycles by firefly algorithm

International Nuclear Information System (INIS)

Bahadormanesh, Nikrouz; Rahat, Shayan; Yarali, Milad

2017-01-01

Highlights: • A multi-objective optimization for radial expander in Organic Rankine Cycles is implemented. • By using firefly algorithm, Pareto front based on the size of turbine and thermal efficiency is produced. • Tension and vibration constrains have a significant effect on optimum design points. - Abstract: Organic Rankine Cycles are viable energy conversion systems in sustainable energy systems due to their compatibility with low-temperature heat sources. In the present study, one dimensional model of radial expanders in conjunction with a thermodynamic model of organic Rankine cycles is prepared. After verification, by defining thermal efficiency of the cycle and size parameter of a radial turbine as the objective functions, a multi-objective optimization was conducted regarding tension and vibration constraints for 4 different organic working fluids (R22, R245fa, R236fa and N-Pentane). In addition to mass flow rate, evaporator temperature, maximum pressure of cycle and turbo-machinery design parameters are selected as the decision variables. Regarding Pareto fronts, by a little increase in size of radial expanders, it is feasible to reach high efficiency. Moreover, by assessing the distribution of decision variables, the variables that play a major role in trending between the objective functions are found. Effects of mechanical and vibration constrains on optimum decision variables are investigated. The results of optimization can be considered as an initial values for design of radial turbines for Organic Rankine Cycles.

Constrained Quadratic Programming and Neurodynamics-Based Solver for Energy Optimization of Biped Walking Robots

Directory of Open Access Journals (Sweden)

Liyang Wang

2017-01-01

Full Text Available The application of biped robots is always trapped by their high energy consumption. This paper makes a contribution by optimizing the joint torques to decrease the energy consumption without changing the biped gaits. In this work, a constrained quadratic programming (QP problem for energy optimization is formulated. A neurodynamics-based solver is presented to solve the QP problem. Differing from the existing literatures, the proposed neurodynamics-based energy optimization (NEO strategy minimizes the energy consumption and guarantees the following three important constraints simultaneously: (i the force-moment equilibrium equation of biped robots, (ii frictions applied by each leg on the ground to hold the biped robot without slippage and tipping over, and (iii physical limits of the motors. Simulations demonstrate that the proposed strategy is effective for energy-efficient biped walking.

An Ameliorative Whale Optimization Algorithm for Multi-Objective Optimal Allocation of Water Resources in Handan, China

Directory of Open Access Journals (Sweden)

Zhihong Yan

2018-01-01

Full Text Available With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. How to allocate water resources optimally, rapidly, and effectively has become a challenging problem. Thus, this study employs a meta-heuristic swarm-based algorithm, the whale optimization algorithm (WOA. To overcome drawbacks like relatively low convergence precision and convergence rates, when applying the WOA algorithm to complex optimization problems, logistic mapping is used to initialize swarm location, and inertia weighting is employed to improve the algorithm. The resulting ameliorative whale optimization algorithm (AWOA shows substantially enhanced convergence rates and precision than the WOA and particle swarm optimization algorithms, demonstrating relatively high reliability and applicability. A water resource allocation optimization model with optimal economic efficiency and least total water shortage volume is established for Handan, China, and solved by the AWOA. The allocation results better reflect actual water usage in Handan. In 2030, the p = 50% total water shortage is forecast as 404.34 × 106 m3 or 14.8%. The shortage is mainly in the primary agricultural sector. The allocation results provide a reference for regional water resources management.

Multi-objective optimal dispatch of distributed energy resources

Science.gov (United States)

Longe, Ayomide

This thesis is composed of two papers which investigate the optimal dispatch for distributed energy resources. In the first paper, an economic dispatch problem for a community microgrid is studied. In this microgrid, each agent pursues an economic dispatch for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, a simple market structure is introduced as a framework for energy trades in a small community microgrid such as the Solar Village. It was found that both sellers and buyers benefited by participating in this market. In the second paper, Semidefinite Programming (SDP) for convex relaxation of power flow equations is used for optimal active and reactive dispatch for Distributed Energy Resources (DER). Various objective functions including voltage regulation, reduced transmission line power losses, and minimized reactive power charges for a microgrid are introduced. Combinations of these goals are attained by solving a multiobjective optimization for the proposed ORPD problem. Also, both centralized and distributed versions of this optimal dispatch are investigated. It was found that SDP made the optimal dispatch faster and distributed solution allowed for scalability.

Multivariate constrained shape optimization: Application to extrusion bell shape for pasta production

Science.gov (United States)

Sarghini, Fabrizio; De Vivo, Angela; Marra, Francesco

2017-10-01

Computational science and engineering methods have allowed a major change in the way products and processes are designed, as validated virtual models - capable to simulate physical, chemical and bio changes occurring during production processes - can be realized and used in place of real prototypes and performing experiments, often time and money consuming. Among such techniques, Optimal Shape Design (OSD) (Mohammadi & Pironneau, 2004) represents an interesting approach. While most classical numerical simulations consider fixed geometrical configurations, in OSD a certain number of geometrical degrees of freedom is considered as a part of the unknowns: this implies that the geometry is not completely defined, but part of it is allowed to move dynamically in order to minimize or maximize the objective function. The applications of optimal shape design (OSD) are uncountable. For systems governed by partial differential equations, they range from structure mechanics to electromagnetism and fluid mechanics or to a combination of the three. This paper presents one of possible applications of OSD, particularly how extrusion bell shape, for past production, can be designed by applying a multivariate constrained shape optimization.

Actions on climate change, Intended Reducing carbon emissions in China via optimal industry shifts: Toward hi-tech industries, cleaner resources and higher carbon shares in less-develop regions

International Nuclear Information System (INIS)

Fu, Xue; Lahr, Michael; Yaxiong, Zhang; Meng, Bo

2017-01-01

This paper uses an optimal interregional input-output model to focus on how interregional industrial shifts alone might enable China to reduce carbon intensity instead of national shifts. The optimal industry shifts assure integration of all regions by regional products and goods in which carbon emissions are embodied via energy consumption. Generally speaking, high-tech industries concentrate in affluent regions to replace construction. Selected services increase output shares across most of regions. Meanwhile, energy-intensive manufacturing, rather than agriculture, decrease their shares to achieve the national annual growth constrained by nation’s carbon targets. Due to the need to decelerate energy use, carbon intensity goal puts particularly extreme pressure on less-developed regions to shutter heavy industries. Explicit shifts toward cleaner resources and renewable energy appear to be quite important for coal mines in Central China. - Highlights: • The model optimizes GDP constrained by industry-based emissions targets. • Scenario on carbon intensity, growth rate, energy mix, and technology advance. • Interregional I-O table informs technology, industry mix, and interregional trade. • China could raise the output of high-tech in South Coast and of selected services. • Shifts toward cleaner resources and renewable energy are important in the Central.

Performance evaluation of Grain family and Espresso ciphers for applications on resource constrained devices

Directory of Open Access Journals (Sweden)

Subhrajyoti Deb

2018-03-01

Full Text Available A secure stream cipher is an effective security solution for applications running on resource-constrained devices. The Grain family of stream ciphers (Grain v1, Grain-128, and Grain-128a is a family of stream ciphers designed for low-end devices. Similarly, Espresso is a lightweight stream cipher that was developed recently for 5G wireless mobile communication. The randomness of the keystream produced by a stream cipher is a good indicator of its security strength. In this study, we have analyzed the randomness properties of the keystreams produced by both the Grain Family and Espresso ciphers using the statistical packages DieHarder and NIST STS. We also analyzed their performances in two constrained devices (ATmega328P and ESP8266 based on three attainable parameters, namely computation time, memory, and power consumption. Keywords: Stream cipher, Randomness, Dieharder, NIST STS

MIROS: a hybrid real-time energy-efficient operating system for the resource-constrained wireless sensor nodes.

Science.gov (United States)

Liu, Xing; Hou, Kun Mean; de Vaulx, Christophe; Shi, Hongling; El Gholami, Khalid

2014-09-22

Operating system (OS) technology is significant for the proliferation of the wireless sensor network (WSN). With an outstanding OS; the constrained WSN resources (processor; memory and energy) can be utilized efficiently. Moreover; the user application development can be served soundly. In this article; a new hybrid; real-time; memory-efficient; energy-efficient; user-friendly and fault-tolerant WSN OS MIROS is designed and implemented. MIROS implements the hybrid scheduler and the dynamic memory allocator. Real-time scheduling can thus be achieved with low memory consumption. In addition; it implements a mid-layer software EMIDE (Efficient Mid-layer Software for User-Friendly Application Development Environment) to decouple the WSN application from the low-level system. The application programming process can consequently be simplified and the application reprogramming performance improved. Moreover; it combines both the software and the multi-core hardware techniques to conserve the energy resources; improve the node reliability; as well as achieve a new debugging method. To evaluate the performance of MIROS; it is compared with the other WSN OSes (TinyOS; Contiki; SOS; openWSN and mantisOS) from different OS concerns. The final evaluation results prove that MIROS is suitable to be used even on the tight resource-constrained WSN nodes. It can support the real-time WSN applications. Furthermore; it is energy efficient; user friendly and fault tolerant.

MIROS: A Hybrid Real-Time Energy-Efficient Operating System for the Resource-Constrained Wireless Sensor Nodes

Science.gov (United States)

Liu, Xing; Hou, Kun Mean; de Vaulx, Christophe; Shi, Hongling; Gholami, Khalid El

2014-01-01

Operating system (OS) technology is significant for the proliferation of the wireless sensor network (WSN). With an outstanding OS; the constrained WSN resources (processor; memory and energy) can be utilized efficiently. Moreover; the user application development can be served soundly. In this article; a new hybrid; real-time; memory-efficient; energy-efficient; user-friendly and fault-tolerant WSN OS MIROS is designed and implemented. MIROS implements the hybrid scheduler and the dynamic memory allocator. Real-time scheduling can thus be achieved with low memory consumption. In addition; it implements a mid-layer software EMIDE (Efficient Mid-layer Software for User-Friendly Application Development Environment) to decouple the WSN application from the low-level system. The application programming process can consequently be simplified and the application reprogramming performance improved. Moreover; it combines both the software and the multi-core hardware techniques to conserve the energy resources; improve the node reliability; as well as achieve a new debugging method. To evaluate the performance of MIROS; it is compared with the other WSN OSes (TinyOS; Contiki; SOS; openWSN and mantisOS) from different OS concerns. The final evaluation results prove that MIROS is suitable to be used even on the tight resource-constrained WSN nodes. It can support the real-time WSN applications. Furthermore; it is energy efficient; user friendly and fault tolerant. PMID:25248069

Maintenance resources optimization applied to a manufacturing system

International Nuclear Information System (INIS)

Fiori de Castro, Helio; Lucchesi Cavalca, Katia

2006-01-01

This paper presents an availability optimization of an engineering system assembled in a series configuration, with redundancy of units and corrective maintenance resources as optimization parameters. The aim is to reach maximum availability, considering as constraints installation and corrective maintenance costs, weight and volume. The optimization method uses a Genetic Algorithm based on biological concepts of species evolution. It is a robust method, as it does not converge to a local optimum. It does not require the use of differential calculus, thus facilitating computational implementation. Results indicate that the methodology is suitable to solve a wide range of engineering design problems involving allocation of redundancies and maintenance resources

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

Science.gov (United States)

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming

Directory of Open Access Journals (Sweden)

Kai Yang

2016-01-01

Full Text Available This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higher-dimensional benchmark problems and is of potential for further applications.

Constrained Optimal Transport

Science.gov (United States)

Ekren, Ibrahim; Soner, H. Mete

2018-03-01

The classical duality theory of Kantorovich (C R (Doklady) Acad Sci URSS (NS) 37:199-201, 1942) and Kellerer (Z Wahrsch Verw Gebiete 67(4):399-432, 1984) for classical optimal transport is generalized to an abstract framework and a characterization of the dual elements is provided. This abstract generalization is set in a Banach lattice X with an order unit. The problem is given as the supremum over a convex subset of the positive unit sphere of the topological dual of X and the dual problem is defined on the bi-dual of X. These results are then applied to several extensions of the classical optimal transport.

Oil Reservoir Production Optimization using Optimal Control

DEFF Research Database (Denmark)

Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan

2011-01-01

Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%.......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...

Computer-aided resource planning and scheduling for radiological services

Science.gov (United States)

Garcia, Hong-Mei C.; Yun, David Y.; Ge, Yiqun; Khan, Javed I.

1996-05-01

There exists tremendous opportunity in hospital-wide resource optimization based on system integration. This paper defines the resource planning and scheduling requirements integral to PACS, RIS and HIS integration. An multi-site case study is conducted to define the requirements. A well-tested planning and scheduling methodology, called Constrained Resource Planning model, has been applied to the chosen problem of radiological service optimization. This investigation focuses on resource optimization issues for minimizing the turnaround time to increase clinical efficiency and customer satisfaction, particularly in cases where the scheduling of multiple exams are required for a patient. How best to combine the information system efficiency and human intelligence in improving radiological services is described. Finally, an architecture for interfacing a computer-aided resource planning and scheduling tool with the existing PACS, HIS and RIS implementation is presented.

Fuzzy resource optimization for safeguards

International Nuclear Information System (INIS)

Zardecki, A.; Markin, J.T.

1991-01-01

Authorization, enforcement, and verification -- three key functions of safeguards systems -- form the basis of a hierarchical description of the system risk. When formulated in terms of linguistic rather than numeric attributes, the risk can be computed through an algorithm based on the notion of fuzzy sets. Similarly, this formulation allows one to analyze the optimal resource allocation by maximizing the overall detection probability, regarded as a linguistic variable. After summarizing the necessary elements of the fuzzy sets theory, we outline the basic algorithm. This is followed by a sample computation of the fuzzy optimization. 10 refs., 1 tab

A market-based optimization approach to sensor and resource management

Science.gov (United States)

Schrage, Dan; Farnham, Christopher; Gonsalves, Paul G.

2006-05-01

Dynamic resource allocation for sensor management is a problem that demands solutions beyond traditional approaches to optimization. Market-based optimization applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents' bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. This paper makes two contributions to the field of market-based optimization: First, we develop a market protocol to handle heterogeneous goods in a dynamic setting. Second, we develop arbitrage agents to improve the efficiency in the market in light of its dynamic nature.

Design of a Circularly Polarized Galileo E6-Band Textile Antenna by Dedicated Multiobjective Constrained Pareto Optimization

Directory of Open Access Journals (Sweden)

Arnaut Dierck

2015-01-01

Full Text Available Designing textile antennas for real-life applications requires a design strategy that is able to produce antennas that are optimized over a wide bandwidth for often conflicting characteristics, such as impedance matching, axial ratio, efficiency, and gain, and, moreover, that is able to account for the variations that apply for the characteristics of the unconventional materials used in smart textile systems. In this paper, such a strategy, incorporating a multiobjective constrained Pareto optimization, is presented and applied to the design of a Galileo E6-band antenna with optimal return loss and wide-band axial ratio characteristics. Subsequently, different prototypes of the optimized antenna are fabricated and measured to validate the proposed design strategy.

Optimal Resource Allocation in Library Systems

Science.gov (United States)

Rouse, William B.

1975-01-01

Queueing theory is used to model processes as either waiting or balking processes. The optimal allocation of resources to these processes is defined as that which maximizes the expected value of the decision-maker's utility function. (Author)

An Adaptive Large Neighborhood Search Algorithm for the Resource-constrained Project Scheduling Problem

DEFF Research Database (Denmark)

Muller, Laurent Flindt

2009-01-01

We present an application of an Adaptive Large Neighborhood Search (ALNS) algorithm to the Resource-constrained Project Scheduling Problem (RCPSP). The ALNS framework was first proposed by Pisinger and Røpke [19] and can be described as a large neighborhood search algorithm with an adaptive layer......, where a set of destroy/repair neighborhoods compete to modify the current solution in each iteration of the algorithm. Experiments are performed on the wellknown J30, J60 and J120 benchmark instances, which show that the proposed algorithm is competitive and confirms the strength of the ALNS framework...

Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies

OpenAIRE

He, Longfei; Xu, Zhaoguang; Niu, Zhanwen

2014-01-01

We focus on the joint production planning of complex supply chains facing stochastic demands and being constrained by carbon emission reduction policies. We pick two typical carbon emission reduction policies to research how emission regulation influences the profit and carbon footprint of a typical supply chain. We use the input-output model to capture the interrelated demand link between an arbitrary pair of two nodes in scenarios without or with carbon emission constraints. We design optim...

Optimal taxation of exhaustible resource under monopoly

International Nuclear Information System (INIS)

Im, Jeong-Bin

2002-01-01

This paper deals with the problem of using taxes (or subsidies) to correct the inefficient resource allocation under monopoly. In this paper, the question raised is 'what would be the optimal tax on resource extraction under monopoly?' Ultimately, it is shown that taxes may be devised to generate price and extraction paths under monopoly that are identical to those under the competitive equilibrium. Tax policy can thus be used as an instrument for changing the distortionary resource allocation generated by the monopolist

A study of the Bienstock-Zuckerberg algorithm, Applications in Mining and Resource Constrained Project Scheduling

OpenAIRE

Muñoz, Gonzalo; Espinoza, Daniel; Goycoolea, Marcos; Moreno, Eduardo; Queyranne, Maurice; Rivera, Orlando

2016-01-01

We study a Lagrangian decomposition algorithm recently proposed by Dan Bienstock and Mark Zuckerberg for solving the LP relaxation of a class of open pit mine project scheduling problems. In this study we show that the Bienstock-Zuckerberg (BZ) algorithm can be used to solve LP relaxations corresponding to a much broader class of scheduling problems, including the well-known Resource Constrained Project Scheduling Problem (RCPSP), and multi-modal variants of the RCPSP that consider batch proc...

Job Resources and Work Engagement: Optimism as Moderator Among Finnish Managers

Directory of Open Access Journals (Sweden)

Stela Rumenova Salminen

2014-05-01

Full Text Available The aim of the present study was to investigate the moderating role of optimism in the relationship between job resources (organizational climate, job control and work engagement among Finnish young managers ('N' = 747. Hierarchical regression analyses showed that both job resources and optimism exerted a positive effect on work engagement and its three dimensions of vigor, dedication, and absorption. The moderation results showed that optimism can diminish the negative impact of low job resources on work engagement. These findings provide evidence to the importance of including personal resources in future research conducted on motivational process. Additionally, these findings provide significant suggestions for the utilization of these resources in organizational practice too, i.e., in staff recruitment, retention and development.

Constrained consumption shifting management in the distributed energy resources scheduling considering demand response

International Nuclear Information System (INIS)

Faria, Pedro; Vale, Zita; Baptista, Jose

2015-01-01

Highlights: • Consumption reduction and/or shift to several periods before and after. • Optimization problem for scheduling of demand response and distributed generation. • Minimization of the Virtual Power Player operation (remuneration) costs. • Demand response can be efficient to meet distributed generation shortages. • Consumers benefit with the remuneration of the participation in demand response. - Abstract: Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods

Teleportation of squeezing: Optimization using non-Gaussian resources

International Nuclear Information System (INIS)

Dell'Anno, Fabio; De Siena, Silvio; Illuminati, Fabrizio; Adesso, Gerardo

2010-01-01

We study the continuous-variable quantum teleportation of states, statistical moments of observables, and scale parameters such as squeezing. We investigate the problem both in ideal and imperfect Vaidman-Braunstein-Kimble protocol setups. We show how the teleportation fidelity is maximized and the difference between output and input variances is minimized by using suitably optimized entangled resources. Specifically, we consider the teleportation of coherent squeezed states, exploiting squeezed Bell states as entangled resources. This class of non-Gaussian states, introduced by Illuminati and co-workers [F. Dell'Anno, S. De Siena, L. Albano, and F. Illuminati, Phys. Rev. A 76, 022301 (2007); F. Dell'Anno, S. De Siena, and F. Illuminati, ibid. 81, 012333 (2010)], includes photon-added and photon-subtracted squeezed states as special cases. At variance with the case of entangled Gaussian resources, the use of entangled non-Gaussian squeezed Bell resources allows one to choose different optimization procedures that lead to inequivalent results. Performing two independent optimization procedures, one can either maximize the state teleportation fidelity, or minimize the difference between input and output quadrature variances. The two different procedures are compared depending on the degrees of displacement and squeezing of the input states and on the working conditions in ideal and nonideal setups.

Optimization of temporal networks under uncertainty

CERN Document Server

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

Optimal Pricing of Spectrum Resources in Wireless Opportunistic Access

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Hanna Bogucka

2012-01-01

Full Text Available We consider opportunistic access to spectrum resources in cognitive wireless networks. The users equipment, or the network nodes in general are able to sense the spectrum and adopt a subset of available resources (the spectrum and the power individually and independently in a distributed manner, that is, based on their local channel quality information and not knowing the Channel State Information (CSI of the other nodes' links in the considered network area. In such a network scenery, the competition of nodes for available resources is observed, which can be modeled as a game. To obtain spectrally efficient and fair spectrum allocation in this competitive environment with the nodes having no information on the other players, taxation of resources is applied to coerce desired behavior of the competitors. In the paper, we present mathematical formulation of the problem of finding the optimal taxation rate (common for all nodes and propose a reduced-complexity algorithm for this optimization. Simulation results for these derived optimal values in various scenarios are also provided.

Sows’ activity classification device using acceleration data – A resource constrained approach

DEFF Research Database (Denmark)

Marchioro, Gilberto Fernandes; Cornou, Cécile; Kristensen, Anders Ringgaard

2011-01-01

This paper discusses the main architectural alternatives and design decisions in order to implement a sows’ activity classification model on electronic devices. The different possibilities are analyzed in practical and technical aspects, focusing on the implementation metrics, like cost......, performance, complexity and reliability. The target architectures are divided into: server based, where the main processing element is a central computer; and embedded based, where the processing is distributed on devices attached to the animals. The initial classification model identifies the activities...... of a heuristic classification approach, focusing on the resource constrained characteristics of embedded systems. The new approach classifies the activities performed by the sows with accuracy close to 90%. It was implemented as a hardware module that can easily be instantiated to provide preprocessed...

Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

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Zunaira Nadeem

2018-04-01

Full Text Available In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO, genetic algorithm (GA, firefly algorithm (FA and optimal stopping rule (OSR theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

Optimal Computing Resource Management Based on Utility Maximization in Mobile Crowdsourcing

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Haoyu Meng

2017-01-01

Full Text Available Mobile crowdsourcing, as an emerging service paradigm, enables the computing resource requestor (CRR to outsource computation tasks to each computing resource provider (CRP. Considering the importance of pricing as an essential incentive to coordinate the real-time interaction among the CRR and CRPs, in this paper, we propose an optimal real-time pricing strategy for computing resource management in mobile crowdsourcing. Firstly, we analytically model the CRR and CRPs behaviors in form of carefully selected utility and cost functions, based on concepts from microeconomics. Secondly, we propose a distributed algorithm through the exchange of control messages, which contain the information of computing resource demand/supply and real-time prices. We show that there exist real-time prices that can align individual optimality with systematic optimality. Finally, we also take account of the interaction among CRPs and formulate the computing resource management as a game with Nash equilibrium achievable via best response. Simulation results demonstrate that the proposed distributed algorithm can potentially benefit both the CRR and CRPs. The coordinator in mobile crowdsourcing can thus use the optimal real-time pricing strategy to manage computing resources towards the benefit of the overall system.

Modified Covariance Matrix Adaptation – Evolution Strategy algorithm for constrained optimization under uncertainty, application to rocket design

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Chocat Rudy

2015-01-01

Full Text Available The design of complex systems often induces a constrained optimization problem under uncertainty. An adaptation of CMA-ES(λ, μ optimization algorithm is proposed in order to efficiently handle the constraints in the presence of noise. The update mechanisms of the parametrized distribution used to generate the candidate solutions are modified. The constraint handling method allows to reduce the semi-principal axes of the probable research ellipsoid in the directions violating the constraints. The proposed approach is compared to existing approaches on three analytic optimization problems to highlight the efficiency and the robustness of the algorithm. The proposed method is used to design a two stage solid propulsion launch vehicle.

The optimal exploitation of a natural resource when there is full complementarity

NARCIS (Netherlands)

Withagen, C.A.A.M.

1983-01-01

We analyse optimal growth for an economy in the possession of an exhaustible resource when the economy's non-resource output is produced by means of capital and the utilization of the resource. The optimal trajectories are sketched for the case where these factors of production are complements.

Teleportation of squeezing: Optimization using non-Gaussian resources

Science.gov (United States)

Dell'Anno, Fabio; de Siena, Silvio; Adesso, Gerardo; Illuminati, Fabrizio

2010-12-01

We study the continuous-variable quantum teleportation of states, statistical moments of observables, and scale parameters such as squeezing. We investigate the problem both in ideal and imperfect Vaidman-Braunstein-Kimble protocol setups. We show how the teleportation fidelity is maximized and the difference between output and input variances is minimized by using suitably optimized entangled resources. Specifically, we consider the teleportation of coherent squeezed states, exploiting squeezed Bell states as entangled resources. This class of non-Gaussian states, introduced by Illuminati and co-workers [F. Dell’Anno, S. De Siena, L. Albano, and F. Illuminati, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.76.022301 76, 022301 (2007); F. Dell’Anno, S. De Siena, and F. Illuminati, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.81.012333 81, 012333 (2010)], includes photon-added and photon-subtracted squeezed states as special cases. At variance with the case of entangled Gaussian resources, the use of entangled non-Gaussian squeezed Bell resources allows one to choose different optimization procedures that lead to inequivalent results. Performing two independent optimization procedures, one can either maximize the state teleportation fidelity, or minimize the difference between input and output quadrature variances. The two different procedures are compared depending on the degrees of displacement and squeezing of the input states and on the working conditions in ideal and nonideal setups.

Optimal allocation of resources in systems

International Nuclear Information System (INIS)

Derman, C.; Lieberman, G.J.; Ross, S.M.

1975-01-01

In the design of a new system, or the maintenance of an old system, allocation of resources is of prime consideration. In allocating resources it is often beneficial to develop a solution that yields an optimal value of the system measure of desirability. In the context of the problems considered in this paper the resources to be allocated are components already produced (assembly problems) and money (allocation in the construction or repair of systems). The measure of desirability for system assembly will usually be maximizing the expected number of systems that perform satisfactorily and the measure in the allocation context will be maximizing the system reliability. Results are presented for these two types of general problems in both a sequential (when appropriate) and non-sequential context

Optimal dynamic control of resources in a distributed system

Science.gov (United States)

Shin, Kang G.; Krishna, C. M.; Lee, Yann-Hang

1989-01-01

The authors quantitatively formulate the problem of controlling resources in a distributed system so as to optimize a reward function and derive optimal control strategies using Markov decision theory. The control variables treated are quite general; they could be control decisions related to system configuration, repair, diagnostics, files, or data. Two algorithms for resource control in distributed systems are derived for time-invariant and periodic environments, respectively. A detailed example to demonstrate the power and usefulness of the approach is provided.

Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

Science.gov (United States)

Crown, William; Buyukkaramikli, Nasuh; Thokala, Praveen; Morton, Alec; Sir, Mustafa Y; Marshall, Deborah A; Tosh, Jon; Padula, William V; Ijzerman, Maarten J; Wong, Peter K; Pasupathy, Kalyan S

2017-03-01

Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems - Poisson and convection-diffusion control

Czech Academy of Sciences Publication Activity Database

Axelsson, Owe; Farouq, S.; Neytcheva, M.

2016-01-01

Roč. 73, č. 3 (2016), s. 631-633 ISSN 1017-1398 R&D Projects: GA MŠk ED1.1.00/02.0070 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution methods Subject RIV: BA - General Mathematics Impact factor: 1.241, year: 2016 http://link.springer.com/article/10.1007%2Fs11075-016-0111-1

A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality

Science.gov (United States)

Cheung, KW; So, HC; Ma, W.-K.; Chan, YT

2006-12-01

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time-of-arrival (TOA), received signal strength (RSS), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the mobile station. In this paper, we present a constrained weighted least squares (CWLS) mobile positioning approach that encompasses all the above described measurement cases. The advantages of CWLS include performance optimality and capability of extension to hybrid measurement cases (e.g., mobile positioning using TDOA and AOA measurements jointly). Assuming zero-mean uncorrelated measurement errors, we show by mean and variance analysis that all the developed CWLS location estimators achieve zero bias and the Cramér-Rao lower bound approximately when measurement error variances are small. The asymptotic optimum performance is also confirmed by simulation results.

Chance constrained uncertain classification via robust optimization

NARCIS (Netherlands)

Ben-Tal, A.; Bhadra, S.; Bhattacharayya, C.; Saketha Nat, J.

2011-01-01

This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out

An Authentication and Key Management Mechanism for Resource Constrained Devices in IEEE 802.11-based IoT Access Networks

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Ki-Wook Kim

2017-09-01

Full Text Available Many Internet of Things (IoT services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM mechanism for resource constrained IoT devices using IEEE 802.11ah has not been proposed as yet. We therefore propose a new AKM mechanism for an IoT access network, which is based on IEEE 802.11 key management with the IEEE 802.1X authentication mechanism. The proposed AKM mechanism does not require any pre-configured security information between the access network domain and the IoT service domain. It considers the resource constraints of IoT devices, allowing IoT devices to delegate the burden of AKM processes to a powerful agent. The agent has sufficient power to support various authentication methods for the access point, and it performs cryptographic functions for the IoT devices. Performance analysis shows that the proposed mechanism greatly reduces computation costs, network costs, and memory usage of the resource-constrained IoT device as compared to the existing IEEE 802.11 Key Management with the IEEE 802.1X authentication mechanism.

An Authentication and Key Management Mechanism for Resource Constrained Devices in IEEE 802.11-based IoT Access Networks.

Science.gov (United States)

Kim, Ki-Wook; Han, Youn-Hee; Min, Sung-Gi

2017-09-21

Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM) mechanism for resource constrained IoT devices using IEEE 802.11ah has not been proposed as yet. We therefore propose a new AKM mechanism for an IoT access network, which is based on IEEE 802.11 key management with the IEEE 802.1X authentication mechanism. The proposed AKM mechanism does not require any pre-configured security information between the access network domain and the IoT service domain. It considers the resource constraints of IoT devices, allowing IoT devices to delegate the burden of AKM processes to a powerful agent. The agent has sufficient power to support various authentication methods for the access point, and it performs cryptographic functions for the IoT devices. Performance analysis shows that the proposed mechanism greatly reduces computation costs, network costs, and memory usage of the resource-constrained IoT device as compared to the existing IEEE 802.11 Key Management with the IEEE 802.1X authentication mechanism.

SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

Energy Technology Data Exchange (ETDEWEB)

Wei, J [City College of New York, New York, NY (United States); Chao, M [The Mount Sinai Medical Center, New York, NY (United States)

2016-06-15

Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

International Nuclear Information System (INIS)

Wei, J; Chao, M

2016-01-01

Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

Resource-based optimization of electric power production (in Iran)

International Nuclear Information System (INIS)

Sadeghzadeh, Mohammad

1999-01-01

This paper is about electric power production optimization and chiefly discusses on the types of resources available in Iran. The modeling has been based on the marginal cost of different energy resources and types of technologies used. the computed costs are the basic standards for optimization of the production system of energy. the costs associated with environmental pollution and also pollution control are considered. the present paper also studied gas fossil fuel, hydro, nuclear, renewable and co-generation of heat and power. The results are discussed and reported at the last of the paper

Optimization of Operations Resources via Discrete Event Simulation Modeling

Science.gov (United States)

Joshi, B.; Morris, D.; White, N.; Unal, R.

1996-01-01

The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

Observation on optimal transition from conventional energy with resource constraints to advanced energy with virtually unlimited resource

International Nuclear Information System (INIS)

Suzuki, Atsuyuki

1980-01-01

The paper is aimed at making a theoretical analysis on optimal shift from finite energy resources like presently used oil toward advanced energy sources like nuclear and solar. First, the value of conventional energy as a finite resource is derived based on the variational principle. Second, a simplified model on macroeconomy is used to obtain and optimal relationship between energy production and consumption and thereby the optimality on energy price is provided. Third, the meaning of research and development of advanced energy is shown by taking into account resource constraints and technological progress. Finally, an optimal timing of the shift from conventional to advanced energies is determined by making use of the maximum principle. The methematical model employed there is much simplified but can be used to conclude that in order to make an optimal shift some policy-oriented decision must be made prior to when an economically competitive condition comes and that, even with that decision made, some recession of energy demand is inevitable during the transitional phase. (author)

Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach

Directory of Open Access Journals (Sweden)

Aidin Delgoshaei

2016-09-01

Full Text Available Purpose: The issue resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating drawback is frequently seen after scheduling of a project in practice which causes a schedule to be useless. Modifying an over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a new and fast tracking method is proposed to schedule large scale projects which can help project engineers to schedule the project rapidly and with more confidence. Design/methodology/approach: In this article, a forward approach for maximizing net present value (NPV in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF is proposed. The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in forward mode. For this purpose, a Genetic Algorithm is applied to solve. Findings: The findings show that the proposed method is an effective way to maximize NPV in MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The results are then compared with branch and bound method and simulated annealing algorithm and it is found the proposed genetic algorithm can provide results with better quality. Then algorithm is then applied for scheduling a hospital in practice. Originality/value: The method can be used alone or as a macro in Microsoft Office Project® Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities after scheduling a project. This can help project engineers to schedule project activities rapidly with more accuracy in practice.

Maintenance modeling and optimization integrating human and material resources

International Nuclear Information System (INIS)

Martorell, S.; Villamizar, M.; Carlos, S.; Sanchez, A.

2010-01-01

Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization (MOP) problem where reliability, availability, maintainability and cost (RAM+C) act as decision criteria and maintenance strategies (i.e. maintenance tasks intervals) act as the only decision variables. However the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources (human and material) available to implement such strategies. Thus, the effect of the necessary resources on RAM+C needs to be modeled and accounted for in formulating the MOP affecting the set of objectives and constraints. In this paper RAM+C models to explicitly address the effect of human resources and material resources (spare parts) on RAM+C criteria are proposed. This extended model allows accounting for explicitly how the above decision criteria depends on the basic model parameters representing the type of strategies, maintenance intervals, durations, human resources and material resources. Finally, an application case is performed to optimize the maintenance plan of a motor-driven pump equipment considering as decision variables maintenance and test intervals and human and material resources.

Maintenance modeling and optimization integrating human and material resources

Energy Technology Data Exchange (ETDEWEB)

Martorell, S., E-mail: smartore@iqn.upv.e [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Villamizar, M.; Carlos, S. [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Sanchez, A. [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia (Spain)

2010-12-15

Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization (MOP) problem where reliability, availability, maintainability and cost (RAM+C) act as decision criteria and maintenance strategies (i.e. maintenance tasks intervals) act as the only decision variables. However the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources (human and material) available to implement such strategies. Thus, the effect of the necessary resources on RAM+C needs to be modeled and accounted for in formulating the MOP affecting the set of objectives and constraints. In this paper RAM+C models to explicitly address the effect of human resources and material resources (spare parts) on RAM+C criteria are proposed. This extended model allows accounting for explicitly how the above decision criteria depends on the basic model parameters representing the type of strategies, maintenance intervals, durations, human resources and material resources. Finally, an application case is performed to optimize the maintenance plan of a motor-driven pump equipment considering as decision variables maintenance and test intervals and human and material resources.

A model for optimal constrained adaptive testing

NARCIS (Netherlands)

van der Linden, Willem J.; Reese, Lynda M.

2001-01-01

A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

A model for optimal constrained adaptive testing

NARCIS (Netherlands)

van der Linden, Willem J.; Reese, Lynda M.

1997-01-01

A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

Adaptive multi-objective Optimization scheme for cognitive radio resource management

KAUST Repository

Alqerm, Ismail; Shihada, Basem

2014-01-01

configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance

Lightweight cryptography for constrained devices

DEFF Research Database (Denmark)

Alippi, Cesare; Bogdanov, Andrey; Regazzoni, Francesco

2014-01-01

Lightweight cryptography is a rapidly evolving research field that responds to the request for security in resource constrained devices. This need arises from crucial pervasive IT applications, such as those based on RFID tags where cost and energy constraints drastically limit the solution...... complexity, with the consequence that traditional cryptography solutions become too costly to be implemented. In this paper, we survey design strategies and techniques suitable for implementing security primitives in constrained devices....

Optimal Allocation of Power-Electronic Interfaced Wind Turbines Using a Genetic Algorithm - Monte Carlo Hybrid Optimization Method

DEFF Research Database (Denmark)

Chen, Peiyuan; Siano, Pierluigi; Chen, Zhe

2010-01-01

determined by the wind resource and geographic conditions, the location of wind turbines in a power system network may significantly affect the distribution of power flow, power losses, etc. Furthermore, modern WTs with power-electronic interface have the capability of controlling reactive power output...... limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor...... setting of WTs. The sequential MCS takes into account the stochastic behaviour of wind power generation and load. The proposed hybrid optimization method is demonstrated on an 11 kV 69-bus distribution system....

Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

Directory of Open Access Journals (Sweden)

R. Venkata Rao

2016-01-01

Full Text Available The teaching-learning-based optimization (TLBO algorithm is finding a large number of applications in different fields of engineering and science since its introduction in 2011. The major applications are found in electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics, chemistry, biotechnology and economics. This paper presents a review of applications of TLBO algorithm and a tutorial for solving the unconstrained and constrained optimization problems. The tutorial is expected to be useful to the beginners.

Evaluation of Reliability in Risk-Constrained Scheduling of Autonomous Microgrids with Demand Response and Renewable Resources

DEFF Research Database (Denmark)

Vahedipour-Dahraie, Mostafa; Anvari-Moghaddam, Amjad; Guerrero, Josep M.

2018-01-01

of microgrid. Moreover, the impacts of different VOLL and risk aversion parameter are illustrated on the system reliability. Extensive simulation results are also presented to illustrate the impact of risk aversion on system security issues with and without DR. Numerical results demonstrate the advantages......Uncertain natures of the renewable energy resources and consumers’ participation in demand response (DR) programs have introduced new challenges to the energy and reserve scheduling of microgrids, particularly in the autonomous mode. In this paper, a risk-constrained stochastic framework...... is presented to maximize the expected profit of a microgrid operator under uncertainties of renewable resources, demand load and electricity price. In the proposed model, the trade-off between maximizing the operator’s expected profit and the risk of getting low profits in undesired scenarios is modeled...

Integrated Medical Model (IMM) Optimization Version 4.0 Functional Improvements

Science.gov (United States)

Arellano, John; Young, M.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Goodenow, D. A.; Myers, J. G.

2016-01-01

The IMMs ability to assess mission outcome risk levels relative to available resources provides a unique capability to provide guidance on optimal operational medical kit and vehicle resources. Post-processing optimization allows IMM to optimize essential resources to improve a specific model outcome such as maximization of the Crew Health Index (CHI), or minimization of the probability of evacuation (EVAC) or the loss of crew life (LOCL). Mass and or volume constrain the optimized resource set. The IMMs probabilistic simulation uses input data on one hundred medical conditions to simulate medical events that may occur in spaceflight, the resources required to treat those events, and the resulting impact to the mission based on specific crew and mission characteristics. Because IMM version 4.0 provides for partial treatment for medical events, IMM Optimization 4.0 scores resources at the individual resource unit increment level as opposed to the full condition-specific treatment set level, as done in version 3.0. This allows the inclusion of as many resources as possible in the event that an entire set of resources called out for treatment cannot satisfy the constraints. IMM Optimization version 4.0 adds capabilities that increase efficiency by creating multiple resource sets based on differing constraints and priorities, CHI, EVAC, or LOCL. It also provides sets of resources that improve mission-related IMM v4.0 outputs with improved performance compared to the prior optimization. The new optimization represents much improved fidelity that will improve the utility of the IMM 4.0 for decision support.

Enhanced cognitive Radio Resource Management for LTE systems

KAUST Repository

Alqerm, Ismail

2013-10-01

The explosive growth in mobile Internet and related services has increased the need for more bandwidth in cellular networks. The Long-Term Evolution (LTE) technology is an attractive solution for operators and subscribers to meet such need since it provides high data rates and scalable bandwidth. Radio Resource Management (RRM) is essential for LTE to provide better communication quality and meet the application QoS requirements. Cognitive resource management is a promising solution for LTE RRM as it improves network efficiency by exploiting radio environment information, intelligent optimization algorithms to configure transmission parameters, and mitigate interference. In this paper, we propose a cognitive resource management scheme to adapt LTE network parameters to the environment conditions. The scheme optimizes resource blocks assignment, modulation selection and bandwidth selection to maximize throughput and minimize interference. The scheme uses constrained optimization for throughput maximization and interference control. It is also enhanced by learning mechanism to reduce the optimization complexity and improve the decision-making quality. Our evaluation results show that our scheme achieved significant improvements in throughput and LTE system capacity. Results also show the improvement in the user satisfaction over other techniques in LTE RRM.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

Science.gov (United States)

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Multidimensional optimal droop control for wind resources in DC microgrids

Science.gov (United States)

Bunker, Kaitlyn J.

Two important and upcoming technologies, microgrids and electricity generation from wind resources, are increasingly being combined. Various control strategies can be implemented, and droop control provides a simple option without requiring communication between microgrid components. Eliminating the single source of potential failure around the communication system is especially important in remote, islanded microgrids, which are considered in this work. However, traditional droop control does not allow the microgrid to utilize much of the power available from the wind. This dissertation presents a novel droop control strategy, which implements a droop surface in higher dimension than the traditional strategy. The droop control relationship then depends on two variables: the dc microgrid bus voltage, and the wind speed at the current time. An approach for optimizing this droop control surface in order to meet a given objective, for example utilizing all of the power available from a wind resource, is proposed and demonstrated. Various cases are used to test the proposed optimal high dimension droop control method, and demonstrate its function. First, the use of linear multidimensional droop control without optimization is demonstrated through simulation. Next, an optimal high dimension droop control surface is implemented with a simple dc microgrid containing two sources and one load. Various cases for changing load and wind speed are investigated using simulation and hardware-in-the-loop techniques. Optimal multidimensional droop control is demonstrated with a wind resource in a full dc microgrid example, containing an energy storage device as well as multiple sources and loads. Finally, the optimal high dimension droop control method is applied with a solar resource, and using a load model developed for a military patrol base application. The operation of the proposed control is again investigated using simulation and hardware-in-the-loop techniques.

Priority classes and weighted constrained equal awards rules for the claims problem

DEFF Research Database (Denmark)

Szwagrzak, Karol

2015-01-01

. They are priority-augmented versions of the standard weighted constrained equal awards rules, also known as weighted gains methods (Moulin, 2000): individuals are sorted into priority classes; the resource is distributed among the individuals in the first priority class using a weighted constrained equal awards...... rule; if some of the resource is left over, then it is distributed among the individuals in the second priority class, again using a weighted constrained equal awards rule; the distribution carries on in this way until the resource is exhausted. Our characterization extends to a generalized version...

Loading pattern optimization with maximum utilization of discharging fuel employing adaptively constrained discontinuous penalty function

International Nuclear Information System (INIS)

Park, T. K.; Joo, H. G.; Kim, C. H.

2010-01-01

In order to find the most economical loading pattern (LP) considering multi-cycle fuel loading, multi-objective fuel LP optimization problems are examined by employing an adaptively constrained discontinuous penalty function (ACDPF) method. This is an improved method to simplify the complicated acceptance logic of the original DPF method in that the stochastic effects caused by the different random number sequence can be reduced. The effectiveness of the multi-objective simulated annealing (SA) algorithm employing ACDPF is examined for the reload core LP of Cycle 4 of Yonggwang Nuclear Unit 4. Several optimization runs are performed with different numbers of objectives consisting of cycle length and average burnup of fuels to be discharged or reloaded. The candidate LPs obtained from the multi-objective optimization runs turn out to be better than the reference LP in the aspects of cycle length and utilization of given fuels. It is note that the proposed ACDPF based MOSA algorithm can be a practical method to obtain an economical LP considering multi-cycle fuel loading. (authors)

Thermodynamic optimization of mixed refrigerant Joule- Thomson systems constrained by heat transfer considerations

International Nuclear Information System (INIS)

Hinze, J F; Klein, S A; Nellis, G F

2015-01-01

Mixed refrigerant (MR) working fluids can significantly increase the cooling capacity of a Joule-Thomson (JT) cycle. The optimization of MRJT systems has been the subject of substantial research. However, most optimization techniques do not model the recuperator in sufficient detail. For example, the recuperator is usually assumed to have a heat transfer coefficient that does not vary with the mixture. Ongoing work at the University of Wisconsin-Madison has shown that the heat transfer coefficients for two-phase flow are approximately three times greater than for a single phase mixture when the mixture quality is between 15% and 85%. As a result, a system that optimizes a MR without also requiring that the flow be in this quality range may require an extremely large recuperator or not achieve the performance predicted by the model. To ensure optimal performance of the JT cycle, the MR should be selected such that it is entirely two-phase within the recuperator. To determine the optimal MR composition, a parametric study was conducted assuming a thermodynamically ideal cycle. The results of the parametric study are graphically presented on a contour plot in the parameter space consisting of the extremes of the qualities that exist within the recuperator. The contours show constant values of the normalized refrigeration power. This ‘map’ shows the effect of MR composition on the cycle performance and it can be used to select the MR that provides a high cooling load while also constraining the recuperator to be two phase. The predicted best MR composition can be used as a starting point for experimentally determining the best MR. (paper)

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.

Optimized maritime emergency resource allocation under dynamic demand.

Directory of Open Access Journals (Sweden)

Wenfen Zhang

Full Text Available Emergency resource is important for people evacuation and property rescue when accident occurs. The relief efforts could be promoted by a reasonable emergency resource allocation schedule in advance. As the marine environment is complicated and changeful, the place, type, severity of maritime accident is uncertain and stochastic, bringing about dynamic demand of emergency resource. Considering dynamic demand, how to make a reasonable emergency resource allocation schedule is challenging. The key problem is to determine the optimal stock of emergency resource for supplier centers to improve relief efforts. This paper studies the dynamic demand, and which is defined as a set. Then a maritime emergency resource allocation model with uncertain data is presented. Afterwards, a robust approach is developed and used to make sure that the resource allocation schedule performs well with dynamic demand. Finally, a case study shows that the proposed methodology is feasible in maritime emergency resource allocation. The findings could help emergency manager to schedule the emergency resource allocation more flexibly in terms of dynamic demand.

Optimal Resource Management in a Stochastic Schaefer Model

OpenAIRE

Richard Hartman

2008-01-01

This paper incorporates uncertainty into the growth function of the Schaefer model for the optimal management of a biological resource. There is a critical value for the biological stock, and it is optimal to do no harvesting if the biological stock is below that critical value and to exert whatever harvesting effort is necessary to prevent the stock from rising above that critical value. The introduction of uncertainty increases the critical value of the stock.

Optimal Financing Decisions of Two Cash-Constrained Supply Chains with Complementary Products

Directory of Open Access Journals (Sweden)

Yuting Li

2016-04-01

Full Text Available In recent years; financing difficulties have been obsessed small and medium enterprises (SMEs; especially emerging SMEs. Inter-members’ joint financing within a supply chain is one of solutions for SMEs. How about members’ joint financing of inter-supply chains? In order to answer the question, we firstly employ the Stackelberg game to propose three kinds of financing decision models of two cash-constrained supply chains with complementary products. Secondly, we analyze qualitatively these models and find the joint financing decision of the two supply chains is the most optimal one. Lastly, we conduct some numerical simulations not only to illustrate above results but also to find that the larger are cross-price sensitivity coefficients; the higher is the motivation for participants to make joint financing decisions; and the more are profits for them to gain.

A Practical and Robust Execution Time-Frame Procedure for the Multi-Mode Resource-Constrained Project Scheduling Problem with Minimal and Maximal Time Lags

Directory of Open Access Journals (Sweden)

Angela Hsiang-Ling Chen

2016-09-01

Full Text Available Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP, improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future growth. Nonetheless, despite all of the research dedicated to solving the RCPSP and its multi-mode variations, there is no standardized procedure that can guide project management practitioners in their scheduling tasks. This is mainly because many of the proposed approaches are either based on unrealistic/oversimplified scenarios or they propose solution procedures not easily applicable or even feasible in real-life situations. In this study, we solve a more true-to-life and complex model, Multimode RCPSP with minimal and maximal time lags (MRCPSP/max. The complexity of the model solved is presented, and the practicality of the proposed approach is justified depending on only information that is available for every project regardless of its industrial context. The results confirm that it is possible to determine a robust makespan and to calculate an execution time-frame with gaps lower than 11% between their lower and upper bounds. In addition, in many instances, the solved lower bound obtained was equal to the best-known optimum.

Linearly constrained minimax optimization

DEFF Research Database (Denmark)

Madsen, Kaj; Schjær-Jacobsen, Hans

1978-01-01

We present an algorithm for nonlinear minimax optimization subject to linear equality and inequality constraints which requires first order partial derivatives. The algorithm is based on successive linear approximations to the functions defining the problem. The resulting linear subproblems...

A Variant of the Topkis-Veinott Method for Solving Inequality Constrained Optimization Problems

International Nuclear Information System (INIS)

Birge, J. R.; Qi, L.; Wei, Z.

2000-01-01

In this paper we give a variant of the Topkis-Veinott method for solving inequality constrained optimization problems. This method uses a linearly constrained positive semidefinite quadratic problem to generate a feasible descent direction at each iteration. Under mild assumptions, the algorithm is shown to be globally convergent in the sense that every accumulation point of the sequence generated by the algorithm is a Fritz-John point of the problem. We introduce a Fritz-John (FJ) function, an FJ1 strong second-order sufficiency condition (FJ1-SSOSC), and an FJ2 strong second-order sufficiency condition (FJ2-SSOSC), and then show, without any constraint qualification (CQ), that (i) if an FJ point z satisfies the FJ1-SSOSC, then there exists a neighborhood N(z) of z such that, for any FJ point y element of N(z) {z } , f 0 (y) ≠ f 0 (z) , where f 0 is the objective function of the problem; (ii) if an FJ point z satisfies the FJ2-SSOSC, then z is a strict local minimum of the problem. The result (i) implies that the entire iteration point sequence generated by the method converges to an FJ point. We also show that if the parameters are chosen large enough, a unit step length can be accepted by the proposed algorithm

Resource allocation in IT projects: using schedule optimization

Directory of Open Access Journals (Sweden)

Michael Chilton

2014-01-01

Full Text Available Resource allocation is the process of assigning resources to tasks throughout the life of a project. Despite sophisticated software packages devoted to keeping track of tasks, resources and resource assignments, it is often the case that project managers find some resources over-allocated and therefore unable to complete the assigned work in the allotted amount of time. Most scheduling software has provisions for leveling resources, but the techniques for doing so simply add time to the schedule and may cause delays in tasks that are critical to the project in meeting deadlines. This paper presents a software application that ensures that resources are properly balanced at the beginning of the project and eliminates the situation in which resources become over-allocated. It can be used in a multi-project environment and reused throughout the project as tasks, resource assignments and availability, and the project scope change. The application utilizes the bounded enumeration technique to formulate an optimal schedule for which both the task sequence and resource availability are taken into account. It is run on a database server to reduce the running time and make it a viable application for practitioners.

Location constrained resource interconnection

International Nuclear Information System (INIS)

Hawkins, D.

2008-01-01

This presentation discussed issues related to wind integration from the perspective of the California Independent System Operator (ISO). Issues related to transmission, reliability, and forecasting were reviewed. Renewable energy sources currently used by the ISO were listed, and details of a new transmission financing plan designed to address the location constraints of renewable energy sources and provide for new transmission infrastructure was presented. The financing mechanism will be financed by participating transmission owners through revenue requirements. New transmission interconnections will include network facilities and generator tie-lines. Tariff revisions have also been implemented to recover the costs of new facilities and generators. The new transmission project will permit wholesale transmission access to areas where there are significant energy resources that are not transportable. A rate impact cap of 15 per cent will be imposed on transmission owners to mitigate short-term costs to ratepayers. The presentation also outlined energy resource area designation plans, renewable energy forecasts, and new wind technologies. Ramping issues were also discussed. It was concluded that the ISO expects to ensure that 20 per cent of its energy will be derived from renewable energy sources. tabs., figs

Optimal allocation of resources for suppressing epidemic spreading on networks

Science.gov (United States)

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.

Slope constrained Topology Optimization

DEFF Research Database (Denmark)

Petersson, J.; Sigmund, Ole

1998-01-01

The problem of minimum compliance topology optimization of an elastic continuum is considered. A general continuous density-energy relation is assumed, including variable thickness sheet models and artificial power laws. To ensure existence of solutions, the design set is restricted by enforcing...

Improved helicopter aeromechanical stability analysis using segmented constrained layer damping and hybrid optimization

Science.gov (United States)

Liu, Qiang; Chattopadhyay, Aditi

2000-06-01

Aeromechanical stability plays a critical role in helicopter design and lead-lag damping is crucial to this design. In this paper, the use of segmented constrained damping layer (SCL) treatment and composite tailoring is investigated for improved rotor aeromechanical stability using formal optimization technique. The principal load-carrying member in the rotor blade is represented by a composite box beam, of arbitrary thickness, with surface bonded SCLs. A comprehensive theory is used to model the smart box beam. A ground resonance analysis model and an air resonance analysis model are implemented in the rotor blade built around the composite box beam with SCLs. The Pitt-Peters dynamic inflow model is used in air resonance analysis under hover condition. A hybrid optimization technique is used to investigate the optimum design of the composite box beam with surface bonded SCLs for improved damping characteristics. Parameters such as stacking sequence of the composite laminates and placement of SCLs are used as design variables. Detailed numerical studies are presented for aeromechanical stability analysis. It is shown that optimum blade design yields significant increase in rotor lead-lag regressive modal damping compared to the initial system.

WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Watkins, W.T.; Siebers, J.V. [University of Virginia, Charlottesville, VA (United States)

2016-06-15

Purpose: To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. Methods: For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanar Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithm’s ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. Results: The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). Conclusion: The qcMCO method can conform to quasi-constrained objectives while revealing

WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

International Nuclear Information System (INIS)

Watkins, W.T.; Siebers, J.V.

2016-01-01

Purpose: To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. Methods: For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanar Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithm’s ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. Results: The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). Conclusion: The qcMCO method can conform to quasi-constrained objectives while revealing

Resource planning and scheduling of payload for satellite with particle swarm optimization

Science.gov (United States)

Li, Jian; Wang, Cheng

2007-11-01

The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite's functions and the payload's resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces. A particle swarm optimization algorithm is proposed to address the problem with an adaptive mutation operator selection, where the swarm is divided into groups with different probabilities to employ various mutation operators viz., differential evolution, Gaussian and random mutation operators. The probabilities are adjusted adaptively by comparing the effectiveness of the groups to select a proper operator. The simulation results have shown the feasibility and effectiveness of the method.

Handling Uncertain Gross Margin and Water Demand in Agricultural Water Resources Management using Robust Optimization

Science.gov (United States)

Chaerani, D.; Lesmana, E.; Tressiana, N.

2018-03-01

In this paper, an application of Robust Optimization in agricultural water resource management problem under gross margin and water demand uncertainty is presented. Water resource management is a series of activities that includes planning, developing, distributing and managing the use of water resource optimally. Water resource management for agriculture can be one of the efforts to optimize the benefits of agricultural output. The objective function of agricultural water resource management problem is to maximizing total benefits by water allocation to agricultural areas covered by the irrigation network in planning horizon. Due to gross margin and water demand uncertainty, we assume that the uncertain data lies within ellipsoidal uncertainty set. We employ robust counterpart methodology to get the robust optimal solution.

Optimal Energy Management for a Smart Grid using Resource-Aware Utility Maximization

Science.gov (United States)

Abegaz, Brook W.; Mahajan, Satish M.; Negeri, Ebisa O.

2016-06-01

Heterogeneous energy prosumers are aggregated to form a smart grid based energy community managed by a central controller which could maximize their collective energy resource utilization. Using the central controller and distributed energy management systems, various mechanisms that harness the power profile of the energy community are developed for optimal, multi-objective energy management. The proposed mechanisms include resource-aware, multi-variable energy utility maximization objectives, namely: (1) maximizing the net green energy utilization, (2) maximizing the prosumers' level of comfortable, high quality power usage, and (3) maximizing the economic dispatch of energy storage units that minimize the net energy cost of the energy community. Moreover, an optimal energy management solution that combines the three objectives has been implemented by developing novel techniques of optimally flexible (un)certainty projection and appliance based pricing decomposition in an IBM ILOG CPLEX studio. A real-world, per-minute data from an energy community consisting of forty prosumers in Amsterdam, Netherlands is used. Results show that each of the proposed mechanisms yields significant increases in the aggregate energy resource utilization and welfare of prosumers as compared to traditional peak-power reduction methods. Furthermore, the multi-objective, resource-aware utility maximization approach leads to an optimal energy equilibrium and provides a sustainable energy management solution as verified by the Lagrangian method. The proposed resource-aware mechanisms could directly benefit emerging energy communities in the world to attain their energy resource utilization targets.

Optimization and management in manufacturing engineering resource collaborative optimization and management through the Internet of Things

CERN Document Server

Liu, Xinbao; Liu, Lin; Cheng, Hao; Zhou, Mi; Pardalos, Panos M

2017-01-01

Problems facing manufacturing clusters that intersect information technology, process management, and optimization within the Internet of Things (IoT) are examined in this book. Recent advances in information technology have transformed the use of resources and data exchange, often leading to management and optimization problems attributable to technology limitations and strong market competition. This book discusses several problems and concepts which makes significant connections in the areas of information sharing, organization management, resource operations, and performance assessment. Geared toward practitioners and researchers, this treatment deepens the understanding between resource collaborative management and advanced information technology. Those in manufacturing will utilize the numerous mathematical models and methods offered to solve practical problems related to cutting stock, supply chain scheduling, and inventory management.  Academics and students with a basic knowledge of manufacturing, c...

Depletion of forest resources in Sudan. Intervention options for optimal control

International Nuclear Information System (INIS)

Hassan, Rashid; Hertzler, Greg; Benhin, James K.A.

2009-01-01

Agricultural expansion and over-cutting of trees for fuelwood are important causes of deforestation in arid and semi-arid countries such as Sudan. The consequence is increased desertification and high erosion and loss of soil nutrients leading to declining agricultural productivity. However, the social costs of the deforestation externality are not taken into account in present forest management and land use planning in Sudan leading to under-pricing and over-exploitation of the country's forest resources. This study evaluated the suitability of approaches commonly used by most forest resource management agencies for prediction of the state and control of harvesting of forest resources against alternative empirical simulation models using relevant information about economic behaviour of trading agents in the fuelwood market. Results showed the clear superiority of models integrating market behaviour over current approaches in the ability to better simulate real trends of wood consumption and hence depletion rates. The study also adopted an optimal control model to derive socially optimal forest harvesting regimes. The results showed that current rates of forest resource rent recovery and reforestation efforts are very far from optimal. Results also suggest that, in addition to optimal pricing and higher reforestation efforts, promotion and availability of fuel substitutes and investment in wood energy conversion efficiencies have a strong potential for curbing the problem of deforestation in Sudan. (author)

Depletion of forest resources in Sudan. Intervention options for optimal control

Energy Technology Data Exchange (ETDEWEB)

Hassan, Rashid [Centre for Environmental Economics and Policy in Africa (CEEPA), Faculty of Natural and Agricultural Sciences, University of Pretoria, 0002 Pretoria (South Africa); Hertzler, Greg [Agricultural and Resource Economics, Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW 2006 (Australia); Benhin, James K.A. [Marine and Coastal Environmental Economics, Business School, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA (United Kingdom)

2009-04-15

Agricultural expansion and over-cutting of trees for fuelwood are important causes of deforestation in arid and semi-arid countries such as Sudan. The consequence is increased desertification and high erosion and loss of soil nutrients leading to declining agricultural productivity. However, the social costs of the deforestation externality are not taken into account in present forest management and land use planning in Sudan leading to under-pricing and over-exploitation of the country's forest resources. This study evaluated the suitability of approaches commonly used by most forest resource management agencies for prediction of the state and control of harvesting of forest resources against alternative empirical simulation models using relevant information about economic behaviour of trading agents in the fuelwood market. Results showed the clear superiority of models integrating market behaviour over current approaches in the ability to better simulate real trends of wood consumption and hence depletion rates. The study also adopted an optimal control model to derive socially optimal forest harvesting regimes. The results showed that current rates of forest resource rent recovery and reforestation efforts are very far from optimal. Results also suggest that, in addition to optimal pricing and higher reforestation efforts, promotion and availability of fuel substitutes and investment in wood energy conversion efficiencies have a strong potential for curbing the problem of deforestation in Sudan. (author)

Depletion of forest resources in Sudan: Intervention options for optimal control

Energy Technology Data Exchange (ETDEWEB)

Hassan, Rashid [Centre for Environmental Economics and Policy in Africa (CEEPA), Faculty of Natural and Agricultural Sciences, University of Pretoria, 0002 Pretoria (South Africa)], E-mail: rashid.hassan@up.ac.za; Hertzler, Greg [Agricultural and Resource Economics, Faculty of Agriculture, Food and Natural Resources, University of Sydney, Sydney, NSW 2006 (Australia); Benhin, James K.A. [Marine and Coastal Environmental Economics, Business School, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA (United Kingdom)

2009-04-15

Agricultural expansion and over-cutting of trees for fuelwood are important causes of deforestation in arid and semi-arid countries such as Sudan. The consequence is increased desertification and high erosion and loss of soil nutrients leading to declining agricultural productivity. However, the social costs of the deforestation externality are not taken into account in present forest management and land use planning in Sudan leading to under-pricing and over-exploitation of the country's forest resources. This study evaluated the suitability of approaches commonly used by most forest resource management agencies for prediction of the state and control of harvesting of forest resources against alternative empirical simulation models using relevant information about economic behaviour of trading agents in the fuelwood market. Results showed the clear superiority of models integrating market behaviour over current approaches in the ability to better simulate real trends of wood consumption and hence depletion rates. The study also adopted an optimal control model to derive socially optimal forest harvesting regimes. The results showed that current rates of forest resource rent recovery and reforestation efforts are very far from optimal. Results also suggest that, in addition to optimal pricing and higher reforestation efforts, promotion and availability of fuel substitutes and investment in wood energy conversion efficiencies have a strong potential for curbing the problem of deforestation in Sudan.

A novel constrained H2 optimization algorithm for mechatronics design in flexure-linked biaxial gantry.

Science.gov (United States)

Ma, Jun; Chen, Si-Lu; Kamaldin, Nazir; Teo, Chek Sing; Tay, Arthur; Mamun, Abdullah Al; Tan, Kok Kiong

2017-11-01

The biaxial gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers from breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a flexure-linked biaxial gantry is designed to allow a small rotation angle of the cross-arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end-effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-to-point reference trajectories with same dynamics but different steps. From here, an H 2 optimization problem with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal step. Comparative experimental results obtained on the testbed are presented to verify the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

Science.gov (United States)

Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

2012-12-01

Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis

An Optimization Algorithm for Multipath Parallel Allocation for Service Resource in the Simulation Task Workflow

Directory of Open Access Journals (Sweden)

Zhiteng Wang

2014-01-01

Full Text Available Service oriented modeling and simulation are hot issues in the field of modeling and simulation, and there is need to call service resources when simulation task workflow is running. How to optimize the service resource allocation to ensure that the task is complete effectively is an important issue in this area. In military modeling and simulation field, it is important to improve the probability of success and timeliness in simulation task workflow. Therefore, this paper proposes an optimization algorithm for multipath service resource parallel allocation, in which multipath service resource parallel allocation model is built and multiple chains coding scheme quantum optimization algorithm is used for optimization and solution. The multiple chains coding scheme quantum optimization algorithm is to extend parallel search space to improve search efficiency. Through the simulation experiment, this paper investigates the effect for the probability of success in simulation task workflow from different optimization algorithm, service allocation strategy, and path number, and the simulation result shows that the optimization algorithm for multipath service resource parallel allocation is an effective method to improve the probability of success and timeliness in simulation task workflow.

A Diagnostic Assessment of Evolutionary Multiobjective Optimization for Water Resources Systems

Science.gov (United States)

Reed, P.; Hadka, D.; Herman, J.; Kasprzyk, J.; Kollat, J.

2012-04-01

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

Security constrained optimal power flow by modern optimization tools

African Journals Online (AJOL)

The main objective of an optimal power flow (OPF) functions is to optimize .... It is characterized as propagation of plants and this happens by gametes union. ... ss and different variables, for example, wind, nearby fertilization can have a critic.

Recent advances in computational optimization

CERN Document Server

2013-01-01

Optimization is part of our everyday life. We try to organize our work in a better way and optimization occurs in minimizing time and cost or the maximization of the profit, quality and efficiency. Also many real world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization. This book presents recent advances in computational optimization. The volume includes important real world problems like parameter settings for con- trolling processes in bioreactor, robot skin wiring, strip packing, project scheduling, tuning of PID controller and so on. Some of them can be solved by applying traditional numerical methods, but others need a huge amount of computational resources. For them it is shown that is appropriate to develop algorithms based on metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming etc...

Integrating job scheduling and constrained network routing

DEFF Research Database (Denmark)

Gamst, Mette

2010-01-01

This paper examines the NP-hard problem of scheduling jobs on resources such that the overall profit of executed jobs is maximized. Job demand must be sent through a constrained network to the resource before execution can begin. The problem has application in grid computing, where a number...

Optimal Resources Planning of Residential Complex Energy System in a Day-ahead Market Based on Invasive Weed Optimization Algorithm

Directory of Open Access Journals (Sweden)

P. Αhmadi

2017-10-01

Full Text Available This paper deals with optimal resources planning in a residential complex energy system, including FC (fuel cell, PV (Photovoltaic panels and the battery. A day-ahead energy management system (EMS based on invasive weed optimization (IWO algorithm is defined for managing different resources to determine an optimal operation schedule for the energy resources at each time interval to minimize the operation cost of a smart residential complex energy system. Moreover, in this paper the impacts of the sell to grid and purchase from grid are also considered. All practical constraints of the each energy resources and utility policies are taken into account. Moreover, sensitivity analysis are conducted on electricity prices and sell to grid factor (SGF, in order to improve understanding the impact of key parameters on residential CHP systems economy. It is shown that proposed system can meet all electrical and thermal demands with economic point of view. Also enhancement of electricity price leads to substantial growth in utilization of proposed CHP system.

The WHO/PEPFAR collaboration to prepare an operations manual for HIV prevention, care, and treatment at primary health centers in high-prevalence, resource-constrained settings: defining laboratory services.

Science.gov (United States)

Spira, Thomas; Lindegren, Mary Lou; Ferris, Robert; Habiyambere, Vincent; Ellerbrock, Tedd

2009-06-01

The expansion of HIV/AIDS care and treatment in resource-constrained countries, especially in sub-Saharan Africa, has generally developed in a top-down manner. Further expansion will involve primary health centers where human and other resources are limited. This article describes the World Health Organization/President's Emergency Plan for AIDS Relief collaboration formed to help scale up HIV services in primary health centers in high-prevalence, resource-constrained settings. It reviews the contents of the Operations Manual developed, with emphasis on the Laboratory Services chapter, which discusses essential laboratory services, both at the center and the district hospital level, laboratory safety, laboratory testing, specimen transport, how to set up a laboratory, human resources, equipment maintenance, training materials, and references. The chapter provides specific information on essential tests and generic job aids for them. It also includes annexes containing a list of laboratory supplies for the health center and sample forms.

SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

International Nuclear Information System (INIS)

An, Y; Liang, J; Liu, W

2015-01-01

Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios with certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with

Optimal production resource reallocation for CO2 emissions reduction in manufacturing sectors

OpenAIRE

Fujii, Hidemichi; Managi, Shunsuke

2015-01-01

To mitigate the effects of climate change, countries worldwide are advancing technologies to reduce greenhouse gas emissions. This paper proposes and measures optimal production resource reallocation using data envelopment analysis. This research attempts to clarify the effect of optimal production resource reallocation on CO2 emissions reduction, focusing on regional and industrial characteristics. We use finance, energy, and CO2 emissions data from 13 industrial sectors in 39 countries from...

Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting

Science.gov (United States)

Gilman, Robert H.; Sanchez-Abanto, Jose R.; Study Group, CRONICAS Cohort

2016-01-01

Objective. To develop and validate a risk score for detecting cases of undiagnosed diabetes in a resource-constrained country. Methods. Two population-based studies in Peruvian population aged ≥35 years were used in the analysis: the ENINBSC survey (n = 2,472) and the CRONICAS Cohort Study (n = 2,945). Fasting plasma glucose ≥7.0 mmol/L was used to diagnose diabetes in both studies. Coefficients for risk score were derived from the ENINBSC data and then the performance was validated using both baseline and follow-up data of the CRONICAS Cohort Study. Results. The prevalence of undiagnosed diabetes was 2.0% in the ENINBSC survey and 2.9% in the CRONICAS Cohort Study. Predictors of undiagnosed diabetes were age, diabetes in first-degree relatives, and waist circumference. Score values ranged from 0 to 4, with an optimal cutoff ≥2 and had a moderate performance when applied in the CRONICAS baseline data (AUC = 0.68; 95% CI: 0.62–0.73; sensitivity 70%; specificity 59%). When predicting incident cases, the AUC was 0.66 (95% CI: 0.61–0.71), with a sensitivity of 69% and specificity of 59%. Conclusions. A simple nonblood based risk score based on age, diabetes in first-degree relatives, and waist circumference can be used as a simple screening tool for undiagnosed and incident cases of diabetes in Peru. PMID:27689096

Analysis in nuclear power accident emergency based on random network and particle swarm optimization

International Nuclear Information System (INIS)

Gong Dichen; Fang Fang; Ding Weicheng; Chen Zhi

2014-01-01

The GERT random network model of nuclear power accident emergency was built in this paper, and the intelligent computation was combined with the random network based on the analysis of Fukushima nuclear accident in Japan. The emergency process was divided into the series link and parallel link, and the parallel link was the part of series link. The overall allocation of resources was firstly optimized, and then the parallel link was analyzed. The effect of the resources for emergency used in different links was analyzed, and it was put forward that the corresponding particle velocity vector was limited under the condition of limited emergency resources. The resource-constrained particle swarm optimization was obtained by using velocity projection matrix to correct the motion of particles. The optimized allocation of resources in emergency process was obtained and the time consumption of nuclear power accident emergency was reduced. (authors)

Constrained Optimization of MIMO Training Sequences

Directory of Open Access Journals (Sweden)

Coon Justin P

2007-01-01

Full Text Available Multiple-input multiple-output (MIMO systems have shown a huge potential for increased spectral efficiency and throughput. With an increasing number of transmitting antennas comes the burden of providing training for channel estimation for coherent detection. In some special cases optimal, in the sense of mean-squared error (MSE, training sequences have been designed. However, in many practical systems it is not feasible to analytically find optimal solutions and numerical techniques must be used. In this paper, two systems (unique word (UW single carrier and OFDM with nulled subcarriers are considered and a method of designing near-optimal training sequences using nonlinear optimization techniques is proposed. In particular, interior-point (IP algorithms such as the barrier method are discussed. Although the two systems seem unrelated, the cost function, which is the MSE of the channel estimate, is shown to be effectively the same for each scenario. Also, additional constraints, such as peak-to-average power ratio (PAPR, are considered and shown to be easily included in the optimization process. Numerical examples illustrate the effectiveness of the designed training sequences, both in terms of MSE and bit-error rate (BER.

optimization of water resources allocation in semi-arid region

African Journals Online (AJOL)

Eng Obi Ibeje

This study is aimed at achieving optimal water resources allocation .... (2005) points out, in his discussions of non- cooperative games model ... the linear and dynamic programming model which many ... e.g. Institute of Water and Hydropower.

Optimization Analysis of Supply Chain Resource Allocation in Customized Online Shopping Service Mode

Directory of Open Access Journals (Sweden)

Jianming Yao

2015-01-01

Full Text Available For an online-shopping company, whether it can provide its customers with customized service is the key to enhance its customers’ experience value and its own competence. A good customized service requires effective integration and reasonable allocation of the company’s supply chain resources running in the background. Based on the analysis of the allocation of supply chain resources in the customized online shopping service mode and its operational characteristics, this paper puts forward an optimization model for the resource allocation and builds an improved ant algorithm to solve it. Finally, the effectiveness and feasibility of the optimization method and algorithm are demonstrated by a numerical simulation. This paper finds that the special online shopping environments lead to many dynamic and uncertain characters of the service demands. Different customized service patterns and their combination patterns should match with different supply chain resource allocations. The optimization model not only reflects the required service cost and delivery time in the objective function, but also considers the service scale effect optimization and the relations of integration benefits and risks. The improved ant algorithm has obvious advantages in flexibly balancing the multiobjective optimizations, adjusting the convergence speed, and adjusting the operation parameters.

Two-stage stochastic day-ahead optimal resource scheduling in a distribution network with intensive use of distributed energy resources

DEFF Research Database (Denmark)

Sousa, Tiago; Ghazvini, Mohammad Ali Fotouhi; Morais, Hugo

2015-01-01

The integration of renewable sources and electric vehicles will introduce new uncertainties to the optimal resource scheduling, namely at the distribution level. These uncertainties are mainly originated by the power generated by renewables sources and by the electric vehicles charge requirements....... This paper proposes a two-state stochastic programming approach to solve the day-ahead optimal resource scheduling problem. The case study considers a 33-bus distribution network with 66 distributed generation units and 1000 electric vehicles....

Observation on optimal transition from conventional energy with resource constraints to advanced energy with virtually unlimited resource, (2)

International Nuclear Information System (INIS)

Ohkubo, Hiroo; Suzuki, Atsuyuki; Kiyose, Ryohei

1983-01-01

This is an extension of the Suzuki model (base model) on optimal transition from resource-limited energy (oil) to advanced energy with virtually unlimited resource. The finite length of plant life, fuel cost, technological progress factor of advanced energy and the upper limit upon annual consumption rate of oil are taken into account for such an extension. The difference in optimal solutions obtained from extended and base models is shown by an application of the maximum principle. The implication of advanced energy R and D andenergy conservation effort is also discussed. (author)

Dynamic Optimization of Constrained Layer Damping Structure for the Headstock of Machine Tools with Modal Strain Energy Method

Directory of Open Access Journals (Sweden)

Yakai Xu

2017-01-01

Full Text Available Dynamic stiffness and damping of the headstock, which is a critical component of precision horizontal machining center, are two main factors that influence machining accuracy and surface finish quality. Constrained Layer Damping (CLD structure is proved to be effective in raising damping capacity for the thin plate and shell structures. In this paper, one kind of high damping material is utilized on the headstock to improve damping capacity. The dynamic characteristic of the hybrid headstock is investigated analytically and experimentally. The results demonstrate that the resonant response amplitudes of the headstock with damping material can decrease significantly compared to original cast structure. To obtain the optimal configuration of damping material, a topology optimization method based on the Evolutionary Structural Optimization (ESO is implemented. Modal Strain Energy (MSE method is employed to analyze the damping and to derive the sensitivity of the modal loss factor. The optimization results indicate that the added weight of damping material decreases by 50%; meanwhile the first two orders of modal loss factor decrease by less than 23.5% compared to the original structure.

Design optimization of shell-and-tube heat exchangers using single objective and multiobjective particle swarm optimization

International Nuclear Information System (INIS)

Elsays, Mostafa A.; Naguib Aly, M; Badawi, Alya A.

2010-01-01

The Particle Swarm Optimization (PSO) algorithm is used to optimize the design of shell-and-tube heat exchangers and determine the optimal feasible solutions so as to eliminate trial-and-error during the design process. The design formulation takes into account the area and the total annual cost of heat exchangers as two objective functions together with operating as well as geometrical constraints. The Nonlinear Constrained Single Objective Particle Swarm Optimization (NCSOPSO) algorithm is used to minimize and find the optimal feasible solution for each of the nonlinear constrained objective functions alone, respectively. Then, a novel Nonlinear Constrained Mult-objective Particle Swarm Optimization (NCMOPSO) algorithm is used to minimize and find the Pareto optimal solutions for both of the nonlinear constrained objective functions together. The experimental results show that the two algorithms are very efficient, fast and can find the accurate optimal feasible solutions of the shell and tube heat exchangers design optimization problem. (orig.)

Optimizing Resource Utilization in Grid Batch Systems

International Nuclear Information System (INIS)

Gellrich, Andreas

2012-01-01

On Grid sites, the requirements of the computing tasks (jobs) to computing, storage, and network resources differ widely. For instance Monte Carlo production jobs are almost purely CPU-bound, whereas physics analysis jobs demand high data rates. In order to optimize the utilization of the compute node resources, jobs must be distributed intelligently over the nodes. Although the job resource requirements cannot be deduced directly, jobs are mapped to POSIX UID/GID according to the VO, VOMS group and role information contained in the VOMS proxy. The UID/GID then allows to distinguish jobs, if users are using VOMS proxies as planned by the VO management, e.g. ‘role=production’ for Monte Carlo jobs. It is possible to setup and configure batch systems (queuing system and scheduler) at Grid sites based on these considerations although scaling limits were observed with the scheduler MAUI. In tests these limitations could be overcome with a home-made scheduler.

Assessment of grid-friendly collective optimization framework for distributed energy resources

Energy Technology Data Exchange (ETDEWEB)

Pensini, Alessandro; Robinson, Matthew; Heine, Nicholas; Stadler, Michael; Mammoli, Andrea

2015-11-04

Distributed energy resources have the potential to provide services to facilities and buildings at lower cost and environmental impact in comparison to traditional electric-gridonly services. The reduced cost could result from a combination of higher system efficiency and exploitation of electricity tariff structures. Traditionally, electricity tariffs are designed to encourage the use of ‘off peak’ power and discourage the use of ‘onpeak’ power, although recent developments in renewable energy resources and distributed generation systems (such as their increasing levels of penetration and their increased controllability) are resulting in pressures to adopt tariffs of increasing complexity. Independently of the tariff structure, more or less sophisticated methods exist that allow distributed energy resources to take advantage of such tariffs, ranging from simple pre-planned schedules to Software-as-a-Service schedule optimization tools. However, as the penetration of distributed energy resources increases, there is an increasing chance of a ‘tragedy of the commons’ mechanism taking place, where taking advantage of tariffs for local benefit can ultimately result in degradation of service and higher energy costs for all. In this work, we use a scheduling optimization tool, in combination with a power distribution system simulator, to investigate techniques that could mitigate the deleterious effect of ‘selfish’ optimization, so that the high-penetration use of distributed energy resources to reduce operating costs remains advantageous while the quality of service and overall energy cost to the community is not affected.

Exact and heuristic solution approaches for the Integrated Job Scheduling and Constrained Network Routing Problem

DEFF Research Database (Denmark)

Gamst, M.

2014-01-01

problem. The methods are computationally evaluated on test instances arising from telecommunications with up to 500 jobs and 500 machines. Results show that solving the integrated job scheduling and constrained network routing problem to optimality is very difficult. The exact solution approach performs......This paper examines the problem of scheduling a number of jobs on a finite set of machines such that the overall profit of executed jobs is maximized. Each job has a certain demand, which must be sent to the executing machine via constrained paths. A job cannot start before all its demands have...... arrived at the machine. Furthermore, two resource demand transmissions cannot use the same edge in the same time period. The problem has application in grid computing, where a number of geographically distributed machines work together for solving large problems. The machines are connected through...

Comparison of Heuristic Methods Applied for Optimal Operation of Water Resources

Directory of Open Access Journals (Sweden)

Alireza Borhani Dariane

2009-01-01

Full Text Available Water resources optimization problems are usually complex and hard to solve using the ordinary optimization methods, or they are at least  not economically efficient. A great number of studies have been conducted in quest of suitable methods capable of handling such problems. In recent years, some new heuristic methods such as genetic and ant algorithms have been introduced in systems engineering. Preliminary applications of these methods in water resources problems have shown that some of them are powerful tools, capable of solving complex problems. In this paper, the application of such heuristic methods as Genetic Algorithm (GA and Ant Colony Optimization (ACO have been studied for optimizing reservoir operation. The Dez Dam reservoir inIranwas chosen for a case study. The methods were applied and compared using short-term (one year and long-term models. Comparison of the results showed that GA outperforms both DP and ACO in finding true global optimum solutions and operating rules.

Using the World Health Organization's 4S-Framework to Strengthen National Strategies, Policies and Services to Address Mental Health Problems in Adolescents in Resource-Constrained Settings

Directory of Open Access Journals (Sweden)

Cabral de Mello Meena

2011-09-01

Full Text Available Abstract Background Most adolescents live in resource-constrained countries and their mental health has been less well recognised than other aspects of their health. The World Health Organization's 4-S Framework provides a structure for national initiatives to improve adolescent health through: gathering and using strategic information; developing evidence-informed policies; scaling up provision and use of health services; and strengthening linkages with other government sectors. The aim of this paper is to discuss how the findings of a recent systematic review of mental health problems in adolescents in resource-constrained settings might be applied using the 4-S Framework. Method Analysis of the implications of the findings of a systematic search of the English-language literature for national strategies, policies, services and cross-sectoral linkages to improve the mental health of adolescents in resource-constrained settings. Results Data are available for only 33/112 [29%] resource-constrained countries, but in all where data are available, non-psychotic mental health problems in adolescents are identifiable, prevalent and associated with reduced quality of life, impaired participation and compromised development. In the absence of evidence about effective interventions in these settings expert opinion is that a broad public policy response which addresses direct strategies for prevention, early intervention and treatment; health service and health workforce requirements; social inclusion of marginalised groups of adolescents; and specific education is required. Specific endorsed strategies include public education, parent education, training for teachers and primary healthcare workers, psycho-educational curricula, identification through periodic screening of the most vulnerable and referral for care, and the availability of counsellors or other identified trained staff members in schools from whom adolescents can seek assistance for

Pole shifting with constrained output feedback

International Nuclear Information System (INIS)

Hamel, D.; Mensah, S.; Boisvert, J.

1984-03-01

The concept of pole placement plays an important role in linear, multi-variable, control theory. It has received much attention since its introduction, and several pole shifting algorithms are now available. This work presents a new method which allows practical and engineering constraints such as gain limitation and controller structure to be introduced right into the pole shifting design strategy. This is achieved by formulating the pole placement problem as a constrained optimization problem. Explicit constraints (controller structure and gain limits) are defined to identify an admissible region for the feedback gain matrix. The desired pole configuration is translated into an appropriate cost function which must be closed-loop minimized. The resulting constrained optimization problem can thus be solved with optimization algorithms. The method has been implemented as an algorithmic interactive module in a computer-aided control system design package, MVPACK. The application of the method is illustrated to design controllers for an aircraft and an evaporator. The results illustrate the importance of controller structure on overall performance of a control system

MO-FG-CAMPUS-TeP2-01: A Graph Form ADMM Algorithm for Constrained Quadratic Radiation Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Liu, X; Belcher, AH; Wiersma, R [The University of Chicago, Chicago, IL (United States)

2016-06-15

Purpose: In radiation therapy optimization the constraints can be either hard constraints which must be satisfied or soft constraints which are included but do not need to be satisfied exactly. Currently the voxel dose constraints are viewed as soft constraints and included as a part of the objective function and approximated as an unconstrained problem. However in some treatment planning cases the constraints should be specified as hard constraints and solved by constrained optimization. The goal of this work is to present a computation efficiency graph form alternating direction method of multipliers (ADMM) algorithm for constrained quadratic treatment planning optimization and compare it with several commonly used algorithms/toolbox. Method: ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. Various proximal operators were first constructed as applicable to quadratic IMRT constrained optimization and the problem was formulated in a graph form of ADMM. A pre-iteration operation for the projection of a point to a graph was also proposed to further accelerate the computation. Result: The graph form ADMM algorithm was tested by the Common Optimization for Radiation Therapy (CORT) dataset including TG119, prostate, liver, and head & neck cases. Both unconstrained and constrained optimization problems were formulated for comparison purposes. All optimizations were solved by LBFGS, IPOPT, Matlab built-in toolbox, CVX (implementing SeDuMi) and Mosek solvers. For unconstrained optimization, it was found that LBFGS performs the best, and it was 3–5 times faster than graph form ADMM. However, for constrained optimization, graph form ADMM was 8 – 100 times faster than the other solvers. Conclusion: A graph form ADMM can be applied to constrained quadratic IMRT optimization. It is more computationally efficient than several other commercial and noncommercial optimizers and it also

Constrained Dynamic Optimality and Binomial Terminal Wealth

DEFF Research Database (Denmark)

Pedersen, J. L.; Peskir, G.

2018-01-01

with interest rate $r \\in {R}$). Letting $P_{t,x}$ denote a probability measure under which $X^u$ takes value $x$ at time $t,$ we study the dynamic version of the nonlinear optimal control problem $\\inf_u\\, Var{t,X_t^u}(X_T^u)$ where the infimum is taken over admissible controls $u$ subject to $X_t^u \\ge e...... a martingale method combined with Lagrange multipliers, we derive the dynamically optimal control $u_*^d$ in closed form and prove that the dynamically optimal terminal wealth $X_T^d$ can only take two values $g$ and $\\beta$. This binomial nature of the dynamically optimal strategy stands in sharp contrast...... with other known portfolio selection strategies encountered in the literature. A direct comparison shows that the dynamically optimal (time-consistent) strategy outperforms the statically optimal (time-inconsistent) strategy in the problem....

Pricing and lot sizing optimization in a two-echelon supply chain with a constrained Logit demand function

Directory of Open Access Journals (Sweden)

Yeison Díaz-Mateus

2017-07-01

Full Text Available Decision making in supply chains is influenced by demand variations, and hence sales, purchase orders and inventory levels are therefore concerned. This paper presents a non-linear optimization model for a two-echelon supply chain, for a unique product. In addition, the model includes the consumers’ maximum willingness to pay, taking socioeconomic differences into account. To do so, the constrained multinomial logit for discrete choices is used to estimate demand levels. Then, a metaheuristic approach based on particle swarm optimization is proposed to determine the optimal product sales price and inventory coordination variables. To validate the proposed model, a supply chain of a technological product was chosen and three scenarios are analyzed: discounts, demand segmentation and demand overestimation. Results are analyzed on the basis of profits, lotsizing and inventory turnover and market share. It can be concluded that the maximum willingness to pay must be taken into consideration, otherwise fictitious profits may mislead decision making, and although the market share would seem to improve, overall profits are not in fact necessarily better.

Optimization of Water Resources and Agricultural Activities for Economic Benefit in Colorado

Science.gov (United States)

LIM, J.; Lall, U.

2017-12-01

The limited water resources available for irrigation are a key constraint for the important agricultural sector of Colorado's economy. As climate change and groundwater depletion reshape these resources, it is essential to understand the economic potential of water resources under different agricultural production practices. This study uses a linear programming optimization at the county spatial scale and annual temporal scales to study the optimal allocation of water withdrawal and crop choices. The model, AWASH, reflects streamflow constraints between different extraction points, six field crops, and a distinct irrigation decision for maize and wheat. The optimized decision variables, under different environmental, social, economic, and physical constraints, provide long-term solutions for ground and surface water distribution and for land use decisions so that the state can generate the maximum net revenue. Colorado, one of the largest agricultural producers, is tested as a case study and the sensitivity on water price and on climate variability is explored.

Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression

Directory of Open Access Journals (Sweden)

Maokuan Zheng

2017-01-01

Full Text Available The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2. The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.

Simultaneous allocation of distributed resources using improved teaching learning based optimization

International Nuclear Information System (INIS)

Kanwar, Neeraj; Gupta, Nikhil; Niazi, K.R.; Swarnkar, Anil

2015-01-01

Highlights: • Simultaneous allocation of distributed energy resources in distribution networks. • Annual energy loss reduction is optimized using a multi-level load profile. • A new penalty factor approach is suggested to check node voltage deviations. • An improved TLBO is proposed by suggesting several modifications in standard TLBO. • An intelligent search is proposed to enhance the performance of solution technique. - Abstract: Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of Teaching Learning Based Optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results

Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

Directory of Open Access Journals (Sweden)

Kiran Teeparthi

2017-04-01

Full Text Available In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

Parallel Harmony Search Based Distributed Energy Resource Optimization

Energy Technology Data Exchange (ETDEWEB)

Ceylan, Oguzhan [ORNL; Liu, Guodong [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)

2015-01-01

This paper presents a harmony search based parallel optimization algorithm to minimize voltage deviations in three phase unbalanced electrical distribution systems and to maximize active power outputs of distributed energy resources (DR). The main contribution is to reduce the adverse impacts on voltage profile during a day as photovoltaics (PVs) output or electrical vehicles (EVs) charging changes throughout a day. The IEEE 123- bus distribution test system is modified by adding DRs and EVs under different load profiles. The simulation results show that by using parallel computing techniques, heuristic methods may be used as an alternative optimization tool in electrical power distribution systems operation.

On the optimal identification of tag sets in time-constrained RFID configurations.

Science.gov (United States)

Vales-Alonso, Javier; Bueno-Delgado, María Victoria; Egea-López, Esteban; Alcaraz, Juan José; Pérez-Mañogil, Juan Manuel

2011-01-01

In Radio Frequency Identification facilities the identification delay of a set of tags is mainly caused by the random access nature of the reading protocol, yielding a random identification time of the set of tags. In this paper, the cumulative distribution function of the identification time is evaluated using a discrete time Markov chain for single-set time-constrained passive RFID systems, namely those ones where a single group of tags is assumed to be in the reading area and only for a bounded time (sojourn time) before leaving. In these scenarios some tags in a set may leave the reader coverage area unidentified. The probability of this event is obtained from the cumulative distribution function of the identification time as a function of the sojourn time. This result provides a suitable criterion to minimize the probability of losing tags. Besides, an identification strategy based on splitting the set of tags in smaller subsets is also considered. Results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags.

Statistical mechanics of budget-constrained auctions

OpenAIRE

Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

2009-01-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). Based on the cavity method of statistical mechanics, we introduce a message passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution,...

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

Science.gov (United States)

Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

2011-02-01

This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

On the Water-Food Nexus: an Optimization Approach for Water and Food Security

Science.gov (United States)

Mortada, Sarah; Abou Najm, Majdi; Yassine, Ali; Alameddine, Ibrahim; El-Fadel, Mutasem

2016-04-01

Water and food security is facing increased challenges with population increase, climate and land use change, as well as resource depletion coupled with pollution and unsustainable practices. Coordinated and effective management of limited natural resources have become an imperative to meet these challenges by optimizing the usage of resources under various constraints. In this study, an optimization model is developed for optimal resource allocation towards sustainable water and food security under nutritional, socio-economic, agricultural, environmental, and natural resources constraints. The core objective of this model is to maximize the composite water-food security status by recommending an optimal water and agricultural strategy. The model balances between the healthy nutritional demand side and the constrained supply side while considering the supply chain in between. It equally ensures that the population achieves recommended nutritional guidelines and population food-preferences by quantifying an optimum agricultural and water policy through transforming optimum food demands into optimum cropping policy given the water and land footprints of each crop or agricultural product. Through this process, water and food security are optimized considering factors that include crop-food transformation (food processing), water footprints, crop yields, climate, blue and green water resources, irrigation efficiency, arable land resources, soil texture, and economic policies. The model performance regarding agricultural practices and sustainable food and water security was successfully tested and verified both at a hypothetical and pilot scale levels.

Success probability orientated optimization model for resource allocation of the technological innovation multi-project system

Institute of Scientific and Technical Information of China (English)

Weixu Dai; Weiwei Wu; Bo Yu; Yunhao Zhu

2016-01-01

A success probability orientated optimization model for resource al ocation of the technological innovation multi-project system is studied. Based on the definition of the technological in-novation multi-project system, the leveling optimization of cost and success probability is set as the objective of resource al ocation. The cost function and the probability function of the optimization model are constructed. Then the objective function of the model is constructed and the solving process is explained. The model is applied to the resource al ocation of an enterprise’s technological innovation multi-project system. The results show that the pro-posed model is more effective in rational resource al ocation, and is more applicable in maximizing the utility of the technological innovation multi-project system.

Convex optimisation approach to constrained fuel optimal control of spacecraft in close relative motion

Science.gov (United States)

Massioni, Paolo; Massari, Mauro

2018-05-01

This paper describes an interesting and powerful approach to the constrained fuel-optimal control of spacecraft in close relative motion. The proposed approach is well suited for problems under linear dynamic equations, therefore perfectly fitting to the case of spacecraft flying in close relative motion. If the solution of the optimisation is approximated as a polynomial with respect to the time variable, then the problem can be approached with a technique developed in the control engineering community, known as "Sum Of Squares" (SOS), and the constraints can be reduced to bounds on the polynomials. Such a technique allows rewriting polynomial bounding problems in the form of convex optimisation problems, at the cost of a certain amount of conservatism. The principles of the techniques are explained and some application related to spacecraft flying in close relative motion are shown.

Optimal Sequential Resource Sharing and Exchange in Multi-Agent Systems

OpenAIRE

Xiao, Yuanzhang

2014-01-01

Central to the design of many engineering systems and social networks is to solve the underlying resource sharing and exchange problems, in which multiple decentralized agents make sequential decisions over time to optimize some long-term performance metrics. It is challenging for the decentralized agents to make optimal sequential decisions because of the complicated coupling among the agents and across time. In this dissertation, we mainly focus on three important classes of multi-agent seq...

Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies

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Longfei He

2014-01-01

Full Text Available We focus on the joint production planning of complex supply chains facing stochastic demands and being constrained by carbon emission reduction policies. We pick two typical carbon emission reduction policies to research how emission regulation influences the profit and carbon footprint of a typical supply chain. We use the input-output model to capture the interrelated demand link between an arbitrary pair of two nodes in scenarios without or with carbon emission constraints. We design optimization algorithm to obtain joint optimal production quantities combination for maximizing overall profit under regulatory policies, respectively. Furthermore, numerical studies by featuring exponentially distributed demand compare systemwide performances in various scenarios. We build the “carbon emission elasticity of profit (CEEP” index as a metric to evaluate the impact of regulatory policies on both chainwide emissions and profit. Our results manifest that by facilitating the mandatory emission cap in proper installation within the network one can balance well effective emission reduction and associated acceptable profit loss. The outcome that CEEP index when implementing Carbon emission tax is elastic implies that the scale of profit loss is greater than that of emission reduction, which shows that this policy is less effective than mandatory cap from industry standpoint at least.

Maxillofacial prostheses challenges in resource constrained regions.

Science.gov (United States)

Tetteh, Sophia; Bibb, Richard J; Martin, Simon J

2017-10-24

This study reviewed the current state of maxillofacial rehabilitation in resource-limited nations. A rigorous literature review was undertaken using several technical and clinical databases using a variety of key words pertinent to maxillofacial prosthetic rehabilitation and resource-limited areas. In addition, interviews were conducted with researchers, clinicians and prosthetists that had direct experience of volunteering or working in resource-limited countries. Results from the review and interviews suggest rehabilitating patients in resource-limited countries remains challenging and efforts to improve the situation requires a multifactorial approach. In conclusion, public health awareness programmes to reduce the causation of injuries and bespoke maxillofacial prosthetics training programmes to suit these countries, as opposed to attempting to replicate Western training programmes. It is also possible that usage of locally sourced and cheaper materials and the use of low-cost technologies could greatly improve maxillofacial rehabilitation efforts in these localities. Implications for Rehabilitation More information and support needs to be provided to maxillofacial defect/injuries patients and to their families or guardians in a culturally sensitive manner by governments. The health needs, economic and psychological needs of the patients need to be taken into account during the rehabilitation process by clinicians and healthcare organizations. The possibility of developing training programs to suit these resource limited countries and not necessarily follow conventional fabrication methods must be looked into further by educational entities.

Models of resource allocation optimization when solving the control problems in organizational systems

Science.gov (United States)

Menshikh, V.; Samorokovskiy, A.; Avsentev, O.

2018-03-01

The mathematical model of optimizing the allocation of resources to reduce the time for management decisions and algorithms to solve the general problem of resource allocation. The optimization problem of choice of resources in organizational systems in order to reduce the total execution time of a job is solved. This problem is a complex three-level combinatorial problem, for the solving of which it is necessary to implement the solution to several specific problems: to estimate the duration of performing each action, depending on the number of performers within the group that performs this action; to estimate the total execution time of all actions depending on the quantitative composition of groups of performers; to find such a distribution of the existing resource of performers in groups to minimize the total execution time of all actions. In addition, algorithms to solve the general problem of resource allocation are proposed.

An Authentication and Key Management Mechanism for Resource Constrained Devices in IEEE 802.11-based IoT Access Networks

OpenAIRE

Kim, Ki-Wook; Han, Youn-Hee; Min, Sung-Gi

2017-01-01

Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM) mechanism for resource constrained IoT devices using IEEE 802.11ah has not been proposed as yet. We therefore propose a new AKM mechanism for an IoT access network, which is based on IEEE 802.11 key management with the IEEE 802.1X aut...

Exploring the impact of constraints in quantum optimal control through a kinematic formulation

International Nuclear Information System (INIS)

Donovan, Ashley; Beltrani, Vincent; Rabitz, Herschel

2013-01-01

Highlights: • This work lays a foundation for studying constraints in quantum control simulations. • The underlying quantum control landscape in the presence of constraints is explored. • Constrained controls can encounter suboptimal traps in the landscape. • The controls are kinematic stand-ins for dynamic time-dependent controls. • A method is developed to transfer between constrained kinematic and dynamic controls. - Abstract: The control of quantum dynamics with tailored laser fields is finding growing experimental success. In practice, experiments will be subject to constraints on the controls that may prevent full optimization of the objective. A framework is presented for systematically investigating the impact of constraints in quantum optimal control simulations using a two-stage process starting with simple time-independent kinematic controls, which act as stand-ins for the traditional dynamic controls. The objective is a state-to-state transition probability, and constraints are introduced by restricting the kinematic control variables during optimization. As a second stage, the means to map from kinematic to dynamic controls is presented, thus enabling a simplified overall procedure for exploring how limited resources affect the ability to optimize the objective. A demonstration of the impact of imposing several types of kinematic constraints is investigated, thereby offering insight into constrained quantum controls

Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

International Nuclear Information System (INIS)

Martorell, S.; Sanchez, A.; Carlos, S.; Serradell, V.

2002-01-01

One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS and M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS and M-related parameters in a plant are associated with controlling risk or with satisfying requirements, and are candidate to be evaluated for their resource effectiveness in risk-informed applications. The resource versus risk-control effectiveness principles formally enter in optimization problems where the cost or the burden for the plant staff is to be minimized while the risk or the availability of the safety equipment is constrained to be at a given level, and vice versa. Optimization of TS and M has been found interesting from the very beginning. However, the resolution of such a kind of optimization problem has been limited to focus on only individual TS and M-related parameters (STI, AOT, PM frequency, etc.) and/or adopting an individual optimization criterion (availability, costs, plant risks, etc.). Nevertheless, a number of reasons exist (e.g. interaction, similar scope, etc.) that justify the growing interest in the last years to focus on the simultaneous and multi-criteria optimization of TS and M. In the simultaneous optimization of TS and M-related parameters based on risk (or unavailability) and cost, like in many other engineering optimization problems, one normally faces multi-modal and non-linear objective functions and a variety of both linear and non-linear constraints. Genetic algorithms (GAs) have

Using Optimization Models for Scheduling in Enterprise Resource Planning Systems

Directory of Open Access Journals (Sweden)

Frank Herrmann

2016-03-01

Full Text Available Companies often use specially-designed production systems and change them from time to time. They produce small batches in order to satisfy specific demands with the least tardiness. This imposes high demands on high-performance scheduling algorithms which can be rapidly adapted to changes in the production system. As a solution, this paper proposes a generic approach: solutions were obtained using a widely-used commercially-available tool for solving linear optimization models, which is available in an Enterprise Resource Planning System (in the SAP system for example or can be connected to it. In a real-world application of a flow shop with special restrictions this approach is successfully used on a standard personal computer. Thus, the main implication is that optimal scheduling with a commercially-available tool, incorporated in an Enterprise Resource Planning System, may be the best approach.

Far-from-equilibrium bidirectional transport system with constrained entrances competing for pool of limited resources

Science.gov (United States)

Verma, Atul Kumar; Sharma, Natasha; Gupta, Arvind Kumar

2018-02-01

Motivated by the wide occurrence of limited resources in many real-life systems, we investigate two-lane totally asymmetric simple exclusion process with constrained entrances under finite supply of particles. We analyze the system within the framework of mean-field theory and examine various complex phenomena, including phase separation, phase transition, and symmetry breaking. Based on the theoretical analysis, we analytically derive the phase boundaries for various symmetric as well as asymmetric phases. It has been observed that the symmetry-breaking phenomenon initiates even for very small number of particles in the system. The phases with broken symmetry originates as shock-low density phase under limited resources, which is in contrast to the scenario with infinite number of particles. As expected, the symmetry breaking continues to persist even for higher values of system particles. Seven stationary phases are observed, with three of them exhibiting symmetry-breaking phenomena. The critical values of a total number of system particles, beyond which various symmetrical and asymmetrical phases appear and disappear are identified. Theoretical outcomes are supported by extensive Monte Carlo simulations. Finally, the size-scaling effect and symmetry-breaking phenomenon on the simulation results have also been examined based on particle density histograms.

Optimization of rootkit revealing system resources – A game theoretic approach

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K. Muthumanickam

2015-10-01

Full Text Available Malicious rootkit is a collection of programs designed with the intent of infecting and monitoring the victim computer without the user’s permission. After the victim has been compromised, the remote attacker can easily cause further damage. In order to infect, compromise and monitor, rootkits adopt Native Application Programming Interface (API hooking technique. To reveal the hidden rootkits, current rootkit detection techniques check different data structures which hold reference to Native APIs. To verify these data structures, a large amount of system resources are required. This is because of the number of APIs in these data structures being quite large. Game theoretic approach is a useful mathematical tool to simulate network attacks. In this paper, a mathematical model is framed to optimize resource consumption using game-theory. To the best of our knowledge, this is the first work to be proposed for optimizing resource consumption while revealing rootkit presence using game theory. Non-cooperative game model is taken to discuss the problem. Analysis and simulation results show that our game theoretic model can effectively reduce the resource consumption by selectively monitoring the number of APIs in windows platform.

Optimal allocation of testing resources for statistical simulations

Science.gov (United States)

Quintana, Carolina; Millwater, Harry R.; Singh, Gulshan; Golden, Patrick

2015-07-01

Statistical estimates from simulation involve uncertainty caused by the variability in the input random variables due to limited data. Allocating resources to obtain more experimental data of the input variables to better characterize their probability distributions can reduce the variance of statistical estimates. The methodology proposed determines the optimal number of additional experiments required to minimize the variance of the output moments given single or multiple constraints. The method uses multivariate t-distribution and Wishart distribution to generate realizations of the population mean and covariance of the input variables, respectively, given an amount of available data. This method handles independent and correlated random variables. A particle swarm method is used for the optimization. The optimal number of additional experiments per variable depends on the number and variance of the initial data, the influence of the variable in the output function and the cost of each additional experiment. The methodology is demonstrated using a fretting fatigue example.

Building sustainable organizational capacity to deliver HIV programs in resource-constrained settings: stakeholder perspectives.

Science.gov (United States)

Sharma, Anjali; Chiliade, Philippe; Michael Reyes, E; Thomas, Kate K; Collens, Stephen R; Rafael Morales, José

2013-12-13

In 2008, the US government mandated that HIV/AIDS care and treatment programs funded by the US President's Emergency Plan for AIDS Relief (PEPFAR) should shift from US-based international partners (IPs) to registered locally owned organizations (local partners, or LPs). The US Health Resources and Services Administration (HRSA) developed the Clinical Assessment for Systems Strengthening (ClASS) framework for technical assistance in resource-constrained settings. The ClASS framework involves all stakeholders in the identification of LPs' strengths and needs for technical assistance. This article examines the role of ClASS in building capacity of LPs that can endure and adapt to changing financial and policy environments. All stakeholders (n=68) in Kenya, Zambia, and Nigeria who had participated in the ClASS from LPs and IPs, the US Centers for Disease Control and Prevention (CDC), and, in Nigeria, HIV/AIDS treatment facilities (TFs) were interviewed individually or in groups (n=42) using an open-ended interview guide. Thematic analysis revealed stakeholder perspectives on ClASS-initiated changes and their sustainability. Local organizations were motivated to make changes in internal operations with the ClASS approach, PEPFAR's competitive funding climate, organizational goals, and desired patient health outcomes. Local organizations drew on internal resources and, if needed, technical assistance from IPs. Reportedly, ClASS-initiated changes and remedial action plans made LPs more competitive for PEPFAR funding. LPs also attributed their successful funding applications to their preexisting systems and reputation. Bureaucracy, complex and competing tasks, and staff attrition impeded progress toward the desired changes. Although CDC continues to provide technical assistance through IPs, declining PEPFAR funds threaten the consolidation of gains, smooth program transition, and continuity of treatment services. The well-timed adaptation and implementation of Cl

Building sustainable organizational capacity to deliver HIV programs in resource-constrained settings: stakeholder perspectives

Directory of Open Access Journals (Sweden)

Anjali Sharma

2013-12-01

Full Text Available Background: In 2008, the US government mandated that HIV/AIDS care and treatment programs funded by the US President's Emergency Plan for AIDS Relief (PEPFAR should shift from US-based international partners (IPs to registered locally owned organizations (local partners, or LPs. The US Health Resources and Services Administration (HRSA developed the Clinical Assessment for Systems Strengthening (ClASS framework for technical assistance in resource-constrained settings. The ClASS framework involves all stakeholders in the identification of LPs’ strengths and needs for technical assistance. Objective: This article examines the role of ClASS in building capacity of LPs that can endure and adapt to changing financial and policy environments. Design: All stakeholders (n=68 in Kenya, Zambia, and Nigeria who had participated in the ClASS from LPs and IPs, the US Centers for Disease Control and Prevention (CDC, and, in Nigeria, HIV/AIDS treatment facilities (TFs were interviewed individually or in groups (n=42 using an open-ended interview guide. Thematic analysis revealed stakeholder perspectives on ClASS-initiated changes and their sustainability. Results: Local organizations were motivated to make changes in internal operations with the ClASS approach, PEPFAR's competitive funding climate, organizational goals, and desired patient health outcomes. Local organizations drew on internal resources and, if needed, technical assistance from IPs. Reportedly, ClASS-initiated changes and remedial action plans made LPs more competitive for PEPFAR funding. LPs also attributed their successful funding applications to their preexisting systems and reputation. Bureaucracy, complex and competing tasks, and staff attrition impeded progress toward the desired changes. Although CDC continues to provide technical assistance through IPs, declining PEPFAR funds threaten the consolidation of gains, smooth program transition, and continuity of treatment services

Evolutionary optimization methods for accelerator design

Science.gov (United States)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained

Optimal Allocation of Water Resources Based on Water Supply Security

Directory of Open Access Journals (Sweden)

Jianhua Wang

2016-06-01

Full Text Available Under the combined impacts of climate change and human activities, a series of water issues, such as water shortages, have arisen all over the world. According to current studies in Science and Nature, water security has become a frontier critical topic. Water supply security (WSS, which is the state of water resources and their capacity and their capacity to meet the demand of water users by water supply systems, is an important part of water security. Currently, WSS is affected by the amount of water resources, water supply projects, water quality and water management. Water shortages have also led to water supply insecurity. WSS is now evaluated based on the balance of the supply and demand under a single water resources condition without considering the dynamics of the varying conditions of water resources each year. This paper developed an optimal allocation model for water resources that can realize the optimal allocation of regional water resources and comprehensively evaluate WSS. The objective of this model is to minimize the duration of water shortages in the long term, as characterized by the Water Supply Security Index (WSSI, which is the assessment value of WSS, a larger WSSI value indicates better results. In addition, the simulation results of the model can determine the change process and dynamic evolution of the WSS. Quanzhou, a city in China with serious water shortage problems, was selected as a case study. The allocation results of the current year and target year of planning demonstrated that the level of regional comprehensive WSS was significantly influenced by the capacity of water supply projects and the conditions of the natural water resources. The varying conditions of the water resources allocation results in the same year demonstrated that the allocation results and WSSI were significantly affected by reductions in precipitation, decreases in the water yield coefficient, and changes in the underlying surface.

Optimizing IEEE 802.11i resource and security essentials for mobile and stationary devices

CERN Document Server

Amiri, IS; Saberi, Iman

2014-01-01

In the past decade, the number of wireless devices has grown exponentially. Decades ago, all systems were wired computer systems. Wireless technology was not accessible in mobile and portable devices until in recent years, and has followed a variety of methods for encryption and resource management. The purpose of the research in Optimizing IEE 802.11i Resources and Security Essentials is to determine the issues of the performance in current encryption methods in AES-CCMP in different types of devices and handle it so that an optimized resource usage would be achieved with the required securi

Microbial decomposers not constrained by climate history along a Mediterranean climate gradient in southern California.

Science.gov (United States)

Baker, Nameer R; Khalili, Banafshe; Martiny, Jennifer B H; Allison, Steven D

2018-06-01

Microbial decomposers mediate the return of CO 2 to the atmosphere by producing extracellular enzymes to degrade complex plant polymers, making plant carbon available for metabolism. Determining if and how these decomposer communities are constrained in their ability to degrade plant litter is necessary for predicting how carbon cycling will be affected by future climate change. We analyzed mass loss, litter chemistry, microbial biomass, extracellular enzyme activities, and enzyme temperature sensitivities in grassland litter transplanted along a Mediterranean climate gradient in southern California. Microbial community composition was manipulated by caging litter within bags made of nylon membrane that prevent microbial immigration. To test whether grassland microbes were constrained by climate history, half of the bags were inoculated with local microbial communities native to each gradient site. We determined that temperature and precipitation likely interact to limit microbial decomposition in the extreme sites along our gradient. Despite their unique climate history, grassland microbial communities were not restricted in their ability to decompose litter under different climate conditions across the gradient, although microbial communities across our gradient may be restricted in their ability to degrade different types of litter. We did find some evidence that local microbial communities were optimized based on climate, but local microbial taxa that proliferated after inoculation into litterbags did not enhance litter decomposition. Our results suggest that microbial community composition does not constrain C-cycling rates under climate change in our system, but optimization to particular resource environments may act as more general constraints on microbial communities. © 2018 by the Ecological Society of America.

Optimizing Resource and Energy Recovery for Municipal Solid Waste Management

Science.gov (United States)

Significant reductions of carbon emissions and air quality impacts can be achieved by optimizing municipal solid waste (MSW) as a resource. Materials and discards management were found to contribute ~40% of overall U.S. GHG emissions as a result of materials extraction, transpo...

Modular production line optimization: The exPLORE architecture

Directory of Open Access Journals (Sweden)

Spinellis Diomidis D.

2000-01-01

Full Text Available The general design problem in serial production lines concerns the allocation of resources such as the number of servers, their service rates, and buffers given production-specific constraints, associated costs, and revenue projections. We describe the design of exPLOre: a modular, object-oriented, production line optimization software architecture. An abstract optimization module can be instantiated using a variety of stochastic optimization methods such as simulated annealing and genetic algorithms. Its search space is constrained by a constraint checker while its search direction is guided by a cost analyser which combines the output of a throughput evaluator with the business model. The throughput evaluator can be instantiated using Markovian, generalised queueing network methods, a decomposition, or an expansion method algorithm.

Mobile learning in resource-constrained environments: a case study of medical education.

Science.gov (United States)

Pimmer, Christoph; Linxen, Sebastian; Gröhbiel, Urs; Jha, Anil Kumar; Burg, Günter

2013-05-01

The achievement of the millennium development goals may be facilitated by the use of information and communication technology in medical and health education. This study intended to explore the use and impact of educational technology in medical education in resource-constrained environments. A multiple case study was conducted in two Nepalese teaching hospitals. The data were analysed using activity theory as an analytical basis. There was little evidence for formal e-learning, but the findings indicate that students and residents adopted mobile technologies, such as mobile phones and small laptops, as cultural tools for surprisingly rich 'informal' learning in a very short time. These tools allowed learners to enhance (a) situated learning, by immediately connecting virtual information sources to their situated experiences; (b) cross-contextual learning by documenting situated experiences in the form of images and videos and re-using the material for later reflection and discussion and (c) engagement with educational content in social network communities. By placing the students and residents at the centre of the new learning activities, this development has begun to affect the overall educational system. Leveraging these tools is closely linked to the development of broad media literacy, including awareness of ethical and privacy issues.

Optimized Management of Groundwater Resources in Kish Island: A Sensitivity Analysis of Optimal Strategies in Response to Environmental Changes

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Davood Mahmoodzadeh

2016-05-01

Full Text Available Groundwater in coastal areas is an essential source of freshwater that warrants protection from seawater intrusion as a priority based on an optimal management plan. Proper optimal management strategies can be developed using a variety of decision-making models. The present study aims to investigate the impacts of environmental changes on groundwater resources. For this purpose, a combined simulation-optimization model is employed that incorporates the SUTRA numerical model and the evolutionaty method of ant colony optimization. The fresh groundwater lens in Kish Island is used as a case study and different scenarios are considered for the likely enviromental changes. Results indicate that while variations in recharge rate form an important factor in the fresh groundwater lens, land-surface inundation due to rises in seawater level, especially in low-lying lands, is the major factor affecting the lens. Furthermore, impacts of environmental changes when effected into the Kish Island aquifer optimization management plan have led to a reduction of more than 20% in the allowable water extraction, indicating the high sensitivity of groundwater resources management plans in small islands to such variations.

Heuristics for NP-hard optimization problems - simpler is better!?

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Žerovnik Janez

2015-11-01

Full Text Available We provide several examples showing that local search, the most basic metaheuristics, may be a very competitive choice for solving computationally hard optimization problems. In addition, generation of starting solutions by greedy heuristics should be at least considered as one of very natural possibilities. In this critical survey, selected examples discussed include the traveling salesman, the resource-constrained project scheduling, the channel assignment, and computation of bounds for the Shannon capacity.

Sustaining Lesson Study: Resources and Factors that Support and Constrain Mathematics Teachers' Ability to Continue After the Grant Ends

Science.gov (United States)

Druken, Bridget Kinsella

Lesson study, a teacher-led vehicle for inquiring into teacher practice through creating, enacting, and reflecting on collaboratively designed research lessons, has been shown to improve mathematics teacher practice in the United States, such as improving knowledge about mathematics, changing teacher practice, and developing communities of teachers. Though it has been described as a sustainable form of professional development, little research exists on what might support teachers in continuing to engage in lesson study after a grant ends. This qualitative and multi-case study investigates the sustainability of lesson study as mathematics teachers engage in a district scale-up lesson study professional experience after participating in a three-year California Mathematics Science Partnership (CaMSP) grant to improve algebraic instruction. To do so, I first provide a description of material (e.g. curricular materials and time), human (attending district trainings and interacting with mathematics coaches), and social (qualities like trust, shared values, common goals, and expectations developed through relationships with others) resources present in the context of two school districts as reported by participants. I then describe practices of lesson study reported to have continued. I also report on teachers' conceptions of what it means to engage in lesson study. I conclude by describing how these results suggest factors that supported and constrained teachers' in continuing lesson study. To accomplish this work, I used qualitative methods of grounded theory informed by a modified sustainability framework on interview, survey, and case study data about teachers, principals, and Teachers on Special Assignment (TOSAs). Four cases were selected to show the varying levels of lesson study practices that continued past the conclusion of the grant. Analyses reveal varying levels of integration, linkage, and synergy among both formally and informally arranged groups of

Web-based public health geographic information systems for resources-constrained environment using scalable vector graphics technology: a proof of concept applied to the expanded program on immunization data

Directory of Open Access Journals (Sweden)

Kamadjeu Raoul

2006-06-01

Full Text Available Abstract Background Geographic Information Systems (GIS are powerful communication tools for public health. However, using GIS requires considerable skill and, for this reason, is sometimes limited to experts. Web-based GIS has emerged as a solution to allow a wider audience to have access to geospatial information. Unfortunately the cost of implementing proprietary solutions may be a limiting factor in the adoption of a public health GIS in a resource-constrained environment. Scalable Vector Graphics (SVG is used to define vector-based graphics for the internet using XML (eXtensible Markup Language; it is an open, platform-independent standard maintained by the World Wide Web Consortium (W3C since 2003. In this paper, we summarize our methodology and demonstrate the potential of this free and open standard to contribute to the dissemination of Expanded Program on Immunization (EPI information by providing interactive maps to a wider audience through the Internet. Results We used SVG to develop a database driven web-based GIS applied to EPI data from three countries of WHO AFRO (World Health Organization – African Region. The system generates interactive district-level country immunization coverage maps and graphs. The approach we describe can be expanded to cover other public health GIS demanding activities, including the design of disease atlases in a resources-constrained environment. Conclusion Our system contributes to accumulating evidence demonstrating the potential of SVG technology to develop web-based public health GIS in resources-constrained settings.

Web-based public health geographic information systems for resources-constrained environment using scalable vector graphics technology: a proof of concept applied to the expanded program on immunization data.

Science.gov (United States)

Kamadjeu, Raoul; Tolentino, Herman

2006-06-03

Geographic Information Systems (GIS) are powerful communication tools for public health. However, using GIS requires considerable skill and, for this reason, is sometimes limited to experts. Web-based GIS has emerged as a solution to allow a wider audience to have access to geospatial information. Unfortunately the cost of implementing proprietary solutions may be a limiting factor in the adoption of a public health GIS in a resource-constrained environment. Scalable Vector Graphics (SVG) is used to define vector-based graphics for the internet using XML (eXtensible Markup Language); it is an open, platform-independent standard maintained by the World Wide Web Consortium (W3C) since 2003. In this paper, we summarize our methodology and demonstrate the potential of this free and open standard to contribute to the dissemination of Expanded Program on Immunization (EPI) information by providing interactive maps to a wider audience through the Internet. We used SVG to develop a database driven web-based GIS applied to EPI data from three countries of WHO AFRO (World Health Organization - African Region). The system generates interactive district-level country immunization coverage maps and graphs. The approach we describe can be expanded to cover other public health GIS demanding activities, including the design of disease atlases in a resources-constrained environment. Our system contributes to accumulating evidence demonstrating the potential of SVG technology to develop web-based public health GIS in resources-constrained settings.

Constraining neutron guide optimizations with phase-space considerations

Energy Technology Data Exchange (ETDEWEB)

Bertelsen, Mads, E-mail: mads.bertelsen@gmail.com; Lefmann, Kim

2016-09-11

We introduce a method named the Minimalist Principle that serves to reduce the parameter space for neutron guide optimization when the required beam divergence is limited. The reduced parameter space will restrict the optimization to guides with a minimal neutron intake that are still theoretically able to deliver the maximal possible performance. The geometrical constraints are derived using phase-space propagation from moderator to guide and from guide to sample, while assuming that the optimized guides will achieve perfect transport of the limited neutron intake. Guide systems optimized using these constraints are shown to provide performance close to guides optimized without any constraints, however the divergence received at the sample is limited to the desired interval, even when the neutron transport is not limited by the supermirrors used in the guide. As the constraints strongly limit the parameter space for the optimizer, two control parameters are introduced that can be used to adjust the selected subspace, effectively balancing between maximizing neutron transport and avoiding background from unnecessary neutrons. One parameter is needed to describe the expected focusing abilities of the guide to be optimized, going from perfectly focusing to no correlation between position and velocity. The second parameter controls neutron intake into the guide, so that one can select exactly how aggressively the background should be limited. We show examples of guides optimized using these constraints which demonstrates the higher signal to noise than conventional optimizations. Furthermore the parameter controlling neutron intake is explored which shows that the simulated optimal neutron intake is close to the analytically predicted, when assuming that the guide is dominated by multiple scattering events.

Resource-Aware Load Balancing Scheme using Multi-objective Optimization in Cloud Computing

OpenAIRE

Kavita Rana; Vikas Zandu

2016-01-01

Cloud computing is a service based, on-demand, pay per use model consisting of an interconnected and virtualizes resources delivered over internet. In cloud computing, usually there are number of jobs that need to be executed with the available resources to achieve optimal performance, least possible total time for completion, shortest response time, and efficient utilization of resources etc. Hence, job scheduling is the most important concern that aims to ensure that use’s requirement are ...

Improved solution for ill-posed linear systems using a constrained optimization ruled by a penalty: evaluation in nuclear medicine tomography

International Nuclear Information System (INIS)

Walrand, Stephan; Jamar, François; Pauwels, Stanislas

2009-01-01

Ill-posed linear systems occur in many different fields. A class of regularization methods, called constrained optimization, aims to determine the extremum of a penalty function whilst constraining an objective function to a likely value. We propose here a novel heuristic way to screen the local extrema satisfying the discrepancy principle. A modified version of the Landweber algorithm is used for the iteration process. After finding a local extremum, a bound is performed to the 'farthest' estimate in the data space still satisfying the discrepancy principle. Afterwards, the modified Landweber algorithm is again applied to find a new local extremum. This bound-iteration process is repeated until a satisfying solution is reached. For evaluation in nuclear medicine tomography, a novel penalty function that preserves the edge steps in the reconstructed solution was evaluated on Monte Carlo simulations and using real SPECT acquisitions as well. Surprisingly, the first bound always provided a significantly better solution in a wide range of statistics

Optimizing the Use of Resources of Technogenic Deposits Taking into Account Uncertainties

Directory of Open Access Journals (Sweden)

Ivan Mikhaylovich Potravny

2017-12-01

Full Text Available The article discusses the problem of resource deterioration and the exhaustion of natural resources as well as the involvement in economic circulation of waste production, resources of technogenic deposits in order to maintain natural capital and support “green” economic growth. This necessitates the development of the mechanism for the environmental management optimization. This mechanism aims at using technogenic deposits in the economy to decrease of both the nature intensity of production and the cost of production. Furthermore, the environmental management optimization should reduce the negative impact of production on the environment. The authors propose to construct a model of economic relevance for the use of waste based on the theory of sustainable development and the theory of substitution of primary natural resources. Under substitutes, we consider useful products, resources from technogenic deposits, resulting from past economic activities. The article considers the problem of accumulation of municipal solid waste and industrial wastes in the regions of Russia in terms of forming and operating the ever-growing technogenic deposits. The authors propose a set of models for the optimum exploitation of technogenic deposits taking into account various factors of the external and internal environment as well as the time factor. The proposed models allow to substantiate and choose the best technologies for the processing of accumulated waste in terms of the reduction of pollution and “green” revenues from the exploitation of technogenic deposits. To account the probabilistic assessments of the geological structure of the technogenic deposits, we propose to use a combination of Monte-Carlo method and of developed optimization models. The authors describe the calculation results and the prospects for the development of a comprehensive model using regional technogenic deposits. The results of the research allow forming an optimal set

Self-Similarity Superresolution for Resource-Constrained Image Sensor Node in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yuehai Wang

2014-01-01

Full Text Available Wireless sensor networks, in combination with image sensors, open up a grand sensing application field. It is a challenging problem to recover a high resolution (HR image from its low resolution (LR counterpart, especially for low-cost resource-constrained image sensors with limited resolution. Sparse representation-based techniques have been developed recently and increasingly to solve this ill-posed inverse problem. Most of these solutions are based on an external dictionary learned from huge image gallery, consequently needing tremendous iteration and long time to match. In this paper, we explore the self-similarity inside the image itself, and propose a new combined self-similarity superresolution (SR solution, with low computation cost and high recover performance. In the self-similarity image super resolution model (SSIR, a small size sparse dictionary is learned from the image itself by the methods such as KSVD. The most similar patch is searched and specially combined during the sparse regulation iteration. Detailed information, such as edge sharpness, is preserved more faithfully and clearly. Experiment results confirm the effectiveness and efficiency of this double self-learning method in the image super resolution.

Efficient and Optimal Capital Accumulation under a Non Renewable Resource Constraint

OpenAIRE

Amigues, Jean-Pierre; Moreaux, Michel

2008-01-01

Usual resource models with capital accumulation focus upon simple one to one process transforming output either into some consumption good or into some capitalgood. We consider a bisectoral model where the capital good, labor and a non renewable resource are used to produce the consumption good and the capital good. Capitalaccumulation is an irreversible process and capital is depreciating over time. In thisframework we reconsider the usual results of the efficient and optimal growth theoryun...

Optimizing the allocation of resources for genomic selection in one breeding cycle.

Science.gov (United States)

Riedelsheimer, Christian; Melchinger, Albrecht E

2013-11-01

We developed a universally applicable planning tool for optimizing the allocation of resources for one cycle of genomic selection in a biparental population. The framework combines selection theory with constraint numerical optimization and considers genotype  ×  environment interactions. Genomic selection (GS) is increasingly implemented in plant breeding programs to increase selection gain but little is known how to optimally allocate the resources under a given budget. We investigated this problem with model calculations by combining quantitative genetic selection theory with constraint numerical optimization. We assumed one selection cycle where both the training and prediction sets comprised double haploid (DH) lines from the same biparental population. Grain yield for testcrosses of maize DH lines was used as a model trait but all parameters can be adjusted in a freely available software implementation. An extension of the expected selection accuracy given by Daetwyler et al. (2008) was developed to correctly balance between the number of environments for phenotyping the training set and its population size in the presence of genotype × environment interactions. Under small budget, genotyping costs mainly determine whether GS is superior over phenotypic selection. With increasing budget, flexibility in resource allocation increases greatly but selection gain leveled off quickly requiring balancing the number of populations with the budget spent for each population. The use of an index combining phenotypic and GS predicted values in the training set was especially beneficial under limited resources and large genotype × environment interactions. Once a sufficiently high selection accuracy is achieved in the prediction set, further selection gain can be achieved most efficiently by massively expanding its size. Thus, with increasing budget, reducing the costs for producing a DH line becomes increasingly crucial for successfully exploiting the

Space engineering modeling and optimization with case studies

CERN Document Server

Pintér, János

2016-01-01

This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...

An approach involving dynamic group search optimization for allocating resources in OFDM-based cognitive radio system

Directory of Open Access Journals (Sweden)

Sameer Suresh Nanivadekar

2018-03-01

Full Text Available Allocation of channel resources in a cognitive radio system for achieving minimized transmission energy at an increased transmission rate is a challenging research. This paper proposes a resource allocation algorithm based on the meta-heuristic search principle. The proposed algorithm is an improved version of the Group Search Optimizer (GSO, which is a currently developed optimization algorithm that works through imitating the searching behaviour of the animals. The improvement is accomplished through introducing dynamics in the maximum pursuit angle of the GSO members. A cognitive radio system, relying on Orthogonal Frequency Division Multiplexing (OFDM for its operation, is simulated and the experimentations are carried out for sub-channel allocation. The proposed algorithm is experimentally compared with five renowned optimization algorithms, namely, conventional GSO, Particle Swarm Optimization, Genetic Algorithm, Firefly Algorithm and Artificial Bee Colony algorithm. The obtained results assert the competing performance of the proposed algorithm over the other algorithms. Keywords: Cognitive radio, OFDM, Resource, Allocation, Optimization, GSO

Selection and storage of perceptual groups is constrained by a discrete resource in working memory.

Science.gov (United States)

Anderson, David E; Vogel, Edward K; Awh, Edward

2013-06-01

Perceptual grouping can lead observers to perceive a multielement scene as a smaller number of hierarchical units. Past work has shown that grouping enables more elements to be stored in visual working memory (WM). Although this may appear to contradict so-called discrete resource models that argue for fixed item limits in WM storage, it is also possible that grouping reduces the effective number of "items" in the display. To test this hypothesis, we examined how mnemonic resolution declined as the number of items to be stored increased. Discrete resource models predict that precision will reach a stable plateau at relatively early set sizes, because no further items can be stored once putative item limits are exceeded. Thus, we examined whether the precision by set size function was bilinear when storage was enhanced via perceptual grouping. In line with the hypothesis that each perceptual group counted as a single "item," precision still reached a clear plateau at a set size determined by the number of stored groups. Moreover, the maximum number of elements stored was doubled, and electrophysiological measures showed that selection and storage-related neural responses were the same for a single element and a multielement perceptual group. Thus, perceptual grouping allows more elements to be held in working memory while storage is still constrained by a discrete item limit.

3Es System Optimization under Uncertainty Using Hybrid Intelligent Algorithm: A Fuzzy Chance-Constrained Programming Model

Directory of Open Access Journals (Sweden)

Jiekun Song

2016-01-01

Full Text Available Harmonious development of 3Es (economy-energy-environment system is the key to realize regional sustainable development. The structure and components of 3Es system are analyzed. Based on the analysis of causality diagram, GDP and industrial structure are selected as the target parameters of economy subsystem, energy consumption intensity is selected as the target parameter of energy subsystem, and the emissions of COD, ammonia nitrogen, SO2, and NOX and CO2 emission intensity are selected as the target parameters of environment system. Fixed assets investment of three industries, total energy consumption, and investment in environmental pollution control are selected as the decision variables. By regarding the parameters of 3Es system optimization as fuzzy numbers, a fuzzy chance-constrained goal programming (FCCGP model is constructed, and a hybrid intelligent algorithm including fuzzy simulation and genetic algorithm is proposed for solving it. The results of empirical analysis on Shandong province of China show that the FCCGP model can reflect the inherent relationship and evolution law of 3Es system and provide the effective decision-making support for 3Es system optimization.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

Science.gov (United States)

Xu, Y; Li, N

2014-09-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems

International Nuclear Information System (INIS)

Xu, Y; Li, N

2014-01-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)

Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow

Science.gov (United States)

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.

On the Integrated Job Scheduling and Constrained Network Routing Problem

DEFF Research Database (Denmark)

Gamst, Mette

This paper examines the NP-hard problem of scheduling a number of jobs on a finite set of machines such that the overall profit of executed jobs is maximized. Each job demands a number of resources, which must be sent to the executing machine via constrained paths. Furthermore, two resource demand...

Development of Optimal Water-Resources Management Strategies for Kaidu-Kongque Watershed under Multiple Uncertainties

Directory of Open Access Journals (Sweden)

Y. Zhou

2013-01-01

Full Text Available In this study, an interval-stochastic fractile optimization (ISFO model is advanced for developing optimal water-resources management strategies under multiple uncertainties. The ISFO model can not only handle uncertainties presented in terms of probability distributions and intervals with possibility distribution boundary, but also quantify subjective information (i.e., expected system benefit preference and risk-averse attitude from different decision makers. The ISFO model is then applied to a real case of water-resources systems planning in Kaidu-kongque watershed, China, and a number of scenarios with different ecological water-allocation policies under varied p-necessity fractiles are analyzed. Results indicate that different policies for ecological water allocation can lead to varied water supplies, economic penalties, and system benefits. The solutions obtained can help decision makers identify optimized water-allocation alternatives, alleviate the water supply-demand conflict, and achieve socioeconomic and ecological sustainability, particularly when limited water resources are available for multiple competing users.

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

OpenAIRE

Luo, Zihan; Armour, Simon; McGeehan, Joe

2015-01-01

This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genet...

Application of a COTS Resource Optimization Framework to the SSN Sensor Tasking Domain - Part I: Problem Definition

Science.gov (United States)

Tran, T.

With the onset of the SmallSat era, the RSO catalog is expected to see continuing growth in the near future. This presents a significant challenge to the current sensor tasking of the SSN. The Air Force is in need of a sensor tasking system that is robust, efficient, scalable, and able to respond in real-time to interruptive events that can change the tracking requirements of the RSOs. Furthermore, the system must be capable of using processed data from heterogeneous sensors to improve tasking efficiency. The SSN sensor tasking can be regarded as an economic problem of supply and demand: the amount of tracking data needed by each RSO represents the demand side while the SSN sensor tasking represents the supply side. As the number of RSOs to be tracked grows, demand exceeds supply. The decision-maker is faced with the problem of how to allocate resources in the most efficient manner. Braxton recently developed a framework called Multi-Objective Resource Optimization using Genetic Algorithm (MOROUGA) as one of its modern COTS software products. This optimization framework took advantage of the maturing technology of evolutionary computation in the last 15 years. This framework was applied successfully to address the resource allocation of an AFSCN-like problem. In any resource allocation problem, there are five key elements: (1) the resource pool, (2) the tasks using the resources, (3) a set of constraints on the tasks and the resources, (4) the objective functions to be optimized, and (5) the demand levied on the resources. In this paper we explain in detail how the design features of this optimization framework are directly applicable to address the SSN sensor tasking domain. We also discuss our validation effort as well as present the result of the AFSCN resource allocation domain using a prototype based on this optimization framework.

Security-Constrained Resource Planning in Electricity Market

International Nuclear Information System (INIS)

Roh, Jae Hyung; Shahidehpour, Mohammad; Yong Fu

2007-06-01

We propose a market-based competitive generation resource planning model in electricity markets. The objective of the model is to introduce the impact of transmission security in a multi-GENCO generation resource planning. The proposed approach is based on effective decomposition and coordination strategies. Lagrangian relaxation and Benders decomposition like structure are applied to the model. Locational price signal and capacity signal are defined for the simulation of competition among GENCOs and the coordination of security between GENCOs and the regulatory body (ISO). The numerical examples exhibit the effectiveness of the proposed generation planning model in electricity markets.

Vision-based coaching: Optimizing resources for leader development

Directory of Open Access Journals (Sweden)

Angela M. Passarelli

2015-04-01

Full Text Available Leaders develop in the direction of their dreams, not in the direction of their deficits. Yet many coaching interactions intended to promote a leader’s development fail to leverage the developmental benefits of the individual’s personal vision. Drawing on Intentional Change Theory, this article postulates that coaching interactions that emphasize a leader’s personal vision (future aspirations and core identity evoke a psychophysiological state characterized by positive emotions, cognitive openness, and optimal neurobiological functioning for complex goal pursuit. Vision-based coaching, via this psychophysiological state, generates a host of relational and motivational resources critical to the developmental process. These resources include: formation of a positive coaching relationship, expansion of the leader’s identity, increased vitality, activation of learning goals, and a promotion-orientation. Organizational outcomes as well as limitations to vision-based coaching are discussed.

Vision-based coaching: optimizing resources for leader development

Science.gov (United States)

Passarelli, Angela M.

2015-01-01

Leaders develop in the direction of their dreams, not in the direction of their deficits. Yet many coaching interactions intended to promote a leader’s development fail to leverage the benefits of the individual’s personal vision. Drawing on intentional change theory, this article postulates that coaching interactions that emphasize a leader’s personal vision (future aspirations and core identity) evoke a psychophysiological state characterized by positive emotions, cognitive openness, and optimal neurobiological functioning for complex goal pursuit. Vision-based coaching, via this psychophysiological state, generates a host of relational and motivational resources critical to the developmental process. These resources include: formation of a positive coaching relationship, expansion of the leader’s identity, increased vitality, activation of learning goals, and a promotion–orientation. Organizational outcomes as well as limitations to vision-based coaching are discussed. PMID:25926803

Optimal exploitation of spatially distributed trophic resources and population stability

Science.gov (United States)

Basset, A.; Fedele, M.; DeAngelis, D.L.

2002-01-01

The relationships between optimal foraging of individuals and population stability are addressed by testing, with a spatially explicit model, the effect of patch departure behaviour on individual energetics and population stability. A factorial experimental design was used to analyse the relevance of the behavioural factor in relation to three factors that are known to affect individual energetics; i.e. resource growth rate (RGR), assimilation efficiency (AE), and body size of individuals. The factorial combination of these factors produced 432 cases, and 1000 replicate simulations were run for each case. Net energy intake rates of the modelled consumers increased with increasing RGR, consumer AE, and consumer body size, as expected. Moreover, through their patch departure behaviour, by selecting the resource level at which they departed from the patch, individuals managed to substantially increase their net energy intake rates. Population stability was also affected by the behavioural factors and by the other factors, but with highly non-linear responses. Whenever resources were limiting for the consumers because of low RGR, large individual body size or low AE, population density at the equilibrium was directly related to the patch departure behaviour; on the other hand, optimal patch departure behaviour, which maximised the net energy intake at the individual level, had a negative influence on population stability whenever resource availability was high for the consumers. The consumer growth rate (r) and numerical dynamics, as well as the spatial and temporal fluctuations of resource density, which were the proximate causes of population stability or instability, were affected by the behavioural factor as strongly or even more strongly than by the others factors considered here. Therefore, patch departure behaviour can act as a feedback control of individual energetics, allowing consumers to optimise a potential trade-off between short-term individual fitness

Optimizing MPBSM Resource Allocation Based on Revenue Management: A China Mobile Sichuan Case

Directory of Open Access Journals (Sweden)

Xu Chen

2015-01-01

Full Text Available The key to determining the network service level of telecom operators is resource allocation for mobile phone base station maintenance (MPBSM. Given intense market competition and higher consumer requirements for network service levels, an increasing proportion of resources have been allocated to MPBSM. Maintenance costs account for the rising fraction of direct costs, and the management of MPBSM resource allocation presents special challenges to telecom operators. China Mobile is the largest telecom operator in the world. Its subsidiary, China Mobile Sichuan, is the first in China to use revenue management in improving MPBSM resource allocation. On the basis of comprehensive revenue (including both economic revenue and social revenue, the subsidiary established a classification model of its base stations. The model scientifically classifies more than 25,000 base stations according to comprehensive revenue. China Mobile Sichuan also conducted differentiation allocation of MPBSM resources on the basis of the classification results. Furthermore, it optimized the assessment system of the telecom base stations to establish an assurance system for the use of MPBSM resources. After half-year implementation, the cell availability of both VIP base stations and total base stations significantly improved. The optimization also reduced economic losses to RMB 10.134 million, and enhanced customer satisfaction with network service by 3.2%.

A Fuzzy Max–Min Decision Bi-Level Fuzzy Programming Model for Water Resources Optimization Allocation under Uncertainty

Directory of Open Access Journals (Sweden)

Chongfeng Ren

2018-04-01

Full Text Available Water competing conflict among water competing sectors from different levels should be taken under consideration during the optimization allocation of water resources. Furthermore, uncertainties are inevitable in the optimization allocation of water resources. In order to deal with the above problems, this study developed a fuzzy max–min decision bi-level fuzzy programming model. The developed model was then applied to a case study in Wuwei, Gansu Province, China. In this study, the net benefit and yield were regarded as the upper-level and lower-level objectives, respectively. Optimal water resource plans were obtained under different possibility levels of fuzzy parameters, which could deal with water competing conflict between the upper level and the lower level effectively. The obtained results are expected to make great contribution in helping local decision-makers to make decisions on dealing with the water competing conflict between the upper and lower level and the optimal use of water resources under uncertainty.

Exact methods for time constrained routing and related scheduling problems

DEFF Research Database (Denmark)

Kohl, Niklas

1995-01-01

of customers. In the VRPTW customers must be serviced within a given time period - a so called time window. The objective can be to minimize operating costs (e.g. distance travelled), fixed costs (e.g. the number of vehicles needed) or a combination of these component costs. During the last decade optimization......This dissertation presents a number of optimization methods for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW is a generalization of the well known capacity constrained Vehicle Routing Problem (VRP), where a fleet of vehicles based at a central depot must service a set...... of J?rnsten, Madsen and S?rensen (1986), which has been tested computationally by Halse (1992). Both methods decompose the problem into a series of time and capacity constrained shotest path problems. This yields a tight lower bound on the optimal objective, and the dual gap can often be closed...

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

Science.gov (United States)

Feng, Yen-Yi; Wu, I-Chin; Chen, Tzu-Li

2017-03-01

The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

Optimizing strategy for repetitive construction projects within multi-mode resources

Directory of Open Access Journals (Sweden)

Remon Fayek Aziz

2013-03-01

Full Text Available Estimating tender data for specific project is the most essential part in construction areas as of a contractor’s view such as: proposed project duration with corresponding gross value and cash flows. Cash flow analysis of construction projects has a long history and has been an important topic in construction management. Determination of project cash flows is very sensitive, especially for repetitive construction projects. This paper focuses on how to calculate tender data for repetitive construction projects such as: project duration, project cost, project/bid price, project cash flows, project maximum working capital and project net present value that is equivalent to net profit at the beginning of the project. A simplified multi-objective optimization formulation will be presented that creates best tender data to contractor comparing with more feasible options that are generated from multi-mode resources in a given project. This mathematical formulation is intended to give more scenarios which provide a practical support for typical construction contractors who need to optimize resource utilization in order to minimize project duration, project/bid price and project maximum working capital while maximizing its net present value simultaneously. At the end of the paper, an illustrative example will be presented to demonstrate the applications of proposed technique to an optimization expressway of repetitive construction project.

Sequential unconstrained minimization algorithms for constrained optimization

International Nuclear Information System (INIS)

Byrne, Charles

2008-01-01

The problem of minimizing a function f(x):R J → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G k (x)=f(x)+g k (x), to obtain x k . The auxiliary functions g k (x):D subset of R J → R + are nonnegative on the set D, each x k is assumed to lie within D, and the objective is to minimize the continuous function f:R J → R over x in the set C = D-bar, the closure of D. We assume that such minimizers exist, and denote one such by x-circumflex. We assume that the functions g k (x) satisfy the inequalities 0≤g k (x)≤G k-1 (x)-G k-1 (x k-1 ), for k = 2, 3, .... Using this assumption, we show that the sequence {(x k )} is decreasing and converges to f(x-circumflex). If the restriction of f(x) to D has bounded level sets, which happens if x-circumflex is unique and f(x) is closed, proper and convex, then the sequence {x k } is bounded, and f(x*)=f(x-circumflex), for any cluster point x*. Therefore, if x-circumflex is unique, x* = x-circumflex and {x k } → x-circumflex. When x-circumflex is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton–Raphson method. The proof techniques used for SUMMA can be extended to obtain related results

Thermo-economic evaluation and optimization of the thermo-chemical conversion of biomass into methanol

International Nuclear Information System (INIS)

Peduzzi, Emanuela; Tock, Laurence; Boissonnet, Guillaume; Maréchal, François

2013-01-01

In a carbon and resources constrained world, thermo-chemical conversion of lignocellulosic biomass into fuels and chemicals is regarded as a promising alternative to fossil resources derived products. Methanol is one potential product which can be used for the synthesis of various chemicals or as a fuel in fuel cells and internal combustion engines. This study focuses on the evaluation and optimization of the thermodynamic and economic performance of methanol production from biomass by applying process integration and optimization techniques. Results reveal the importance of the energy integration and in particular of the cogeneration of electricity for the efficient use of biomass. - Highlights: • A thermo-economic model for biomass conversion into methanol is developed. • Process integration and multi-objective optimization techniques are applied. • Results reveal the importance of energy integration for electricity co-generation

E-learning in medical education in resource constrained low- and middle-income countries.

Science.gov (United States)

Frehywot, Seble; Vovides, Yianna; Talib, Zohray; Mikhail, Nadia; Ross, Heather; Wohltjen, Hannah; Bedada, Selam; Korhumel, Kristine; Koumare, Abdel Karim; Scott, James

2013-02-04

In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC), and presents the spectrum of tools and strategies used. Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators "AND" and "OR" to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Of the relevant articles found, most referred to e-learning in Brazil (14 articles), India (14), Egypt (10) and South Africa (10). While e-learning has been used by a variety of health workers in LMICs, the majority (58%) reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles) of which computer-assisted learning (CAL) comprised the majority (45 articles). Other approaches included simulations and the use of multimedia software (20 articles), web-based learning (14 articles), and eTutor/eMentor programs (3 articles). Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities. E-learning in medical education is a means to an end

Software Defined Resource Orchestration System for Multitask Application in Heterogeneous Mobile Cloud Computing

Directory of Open Access Journals (Sweden)

Qi Qi

2016-01-01

Full Text Available The mobile cloud computing (MCC that combines mobile computing and cloud concept takes wireless access network as the transmission medium and uses mobile devices as the client. When offloading the complicated multitask application to the MCC environment, each task executes individually in terms of its own computation, storage, and bandwidth requirement. Due to user’s mobility, the provided resources contain different performance metrics that may affect the destination choice. Nevertheless, these heterogeneous MCC resources lack integrated management and can hardly cooperate with each other. Thus, how to choose the appropriate offload destination and orchestrate the resources for multitask is a challenge problem. This paper realizes a programming resource provision for heterogeneous energy-constrained computing environments, where a software defined controller is responsible for resource orchestration, offload, and migration. The resource orchestration is formulated as multiobjective optimal problem that contains the metrics of energy consumption, cost, and availability. Finally, a particle swarm algorithm is used to obtain the approximate optimal solutions. Simulation results show that the solutions for all of our studied cases almost can hit Pareto optimum and surpass the comparative algorithm in approximation, coverage, and execution time.

Optimizing strategy software for repetitive construction projects within multi-mode resources

Directory of Open Access Journals (Sweden)

Remon Fayek Aziz

2013-09-01

Full Text Available Estimating tender data for specific project is the most essential part in construction areas as of contractor’s view such as: proposed project duration with corresponding gross value and cash flows. This paper focuses on how to calculate tender data using Optimizing Strategy Software (OSS for repetitive construction projects with identical activity’s duration in case of single number of crew such as: project duration, project/bid price, project maximum working capital, and project net present value of the studied project. A simplified multi-objective optimization software (OSS will be presented that creates best tender data to contractor compared with more feasible options generated from multi-mode resources in a given project. OSS is intended to give more scenarios which provide practical support for typical construction contractors who need to optimize resource utilization in order to minimize project duration, project/bid price, and project maximum working capital while maximizing its net present value simultaneously. OSS is designed by java programing code system to provide a number of new and unique capabilities, including: (1 Ranking the obtained optimal plans according to a set of planner specified weights representing the relative importance of duration, price, maximum working capital and net present value in the analyzed project; (2 Visualizing and viewing the generated optimal trade-off; and (3 Providing seamless integration with available project management calculations. In order to provide the aforementioned capabilities of OSS, the system is implemented and developed in four main modules: (1 A user interface module; (2 A database module; (3 A running module; (4 A connecting module. At the end of the paper, an illustrative example will be presented to demonstrate and verify the applications of the proposed software (OSS to an optimization expressway of repetitive construction project.

Multivariable controller for discrete stochastic amplitude-constrained systems

Directory of Open Access Journals (Sweden)

Hannu T. Toivonen

1983-04-01

Full Text Available A sub-optimal multivariable controller for discrete stochastic amplitude-constrained systems is presented. In the approach the regulator structure is restricted to the class of linear saturated feedback laws. The stationary covariances of the controlled system are evaluated by approximating the stationary probability distribution of the state by a gaussian distribution. An algorithm for minimizing a quadratic loss function is given, and examples are presented to illustrate the performance of the sub-optimal controller.

An algorithm for mass matrix calculation of internally constrained molecular geometries

International Nuclear Information System (INIS)

Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

2008-01-01

Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model

An algorithm for mass matrix calculation of internally constrained molecular geometries.

Science.gov (United States)

Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

2008-01-28

Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model.

Sustainability is possible despite greed - Exploring the nexus between profitability and sustainability in common pool resource systems.

Science.gov (United States)

Osten, Friedrich Burkhard von der; Kirley, Michael; Miller, Tim

2017-05-23

The sustainable use of common pool resources has become a significant global challenge. It is now widely accepted that specific mechanisms such as community-based management strategies, institutional responses such as resource privatization, information availability and emergent social norms can be used to constrain individual 'harvesting' to socially optimal levels. However, there is a paucity of research focused specifically on aligning profitability and sustainability goals. In this paper, an integrated mathematical model of a common pool resource game is developed to explore the nexus between the underlying costs and benefits of harvesting decisions and the sustainable level of a shared, dynamic resource. We derive optimal harvesting efforts analytically and then use numerical simulations to show that individuals in a group can learn to make harvesting decisions that lead to the globally optimal levels. Individual agents make their decision based on signals received and a trade-off between economic and ecological sustainability. When the balance is weighted towards profitability, acceptable economic and social outcomes emerge. However, if individual agents are solely driven by profit, the shared resource is depleted in the long run - sustainability is possible despite some greed, but too much will lead to over-exploitation.

Generalized DSS shell for developing simulation and optimization hydro-economic models of complex water resources systems

Science.gov (United States)

Pulido-Velazquez, Manuel; Lopez-Nicolas, Antonio; Harou, Julien J.; Andreu, Joaquin

2013-04-01

Hydrologic-economic models allow integrated analysis of water supply, demand and infrastructure management at the river basin scale. These models simultaneously analyze engineering, hydrology and economic aspects of water resources management. Two new tools have been designed to develop models within this approach: a simulation tool (SIM_GAMS), for models in which water is allocated each month based on supply priorities to competing uses and system operating rules, and an optimization tool (OPT_GAMS), in which water resources are allocated optimally following economic criteria. The characterization of the water resource network system requires a connectivity matrix representing the topology of the elements, generated using HydroPlatform. HydroPlatform, an open-source software platform for network (node-link) models, allows to store, display and export all information needed to characterize the system. Two generic non-linear models have been programmed in GAMS to use the inputs from HydroPlatform in simulation and optimization models. The simulation model allocates water resources on a monthly basis, according to different targets (demands, storage, environmental flows, hydropower production, etc.), priorities and other system operating rules (such as reservoir operating rules). The optimization model's objective function is designed so that the system meets operational targets (ranked according to priorities) each month while following system operating rules. This function is analogous to the one used in the simulation module of the DSS AQUATOOL. Each element of the system has its own contribution to the objective function through unit cost coefficients that preserve the relative priority rank and the system operating rules. The model incorporates groundwater and stream-aquifer interaction (allowing conjunctive use simulation) with a wide range of modeling options, from lumped and analytical approaches to parameter-distributed models (eigenvalue approach). Such

Improving the Performance of Highly Constrained Water Resource Systems using Multiobjective Evolutionary Algorithms and RiverWare

Science.gov (United States)

Smith, R.; Kasprzyk, J. R.; Zagona, E. A.

2015-12-01

Instead of building new infrastructure to increase their supply reliability, water resource managers are often tasked with better management of current systems. The managers often have existing simulation models that aid their planning, and lack methods for efficiently generating and evaluating planning alternatives. This presentation discusses how multiobjective evolutionary algorithm (MOEA) decision support can be used with the sophisticated water infrastructure model, RiverWare, in highly constrained water planning environments. We first discuss a study that performed a many-objective tradeoff analysis of water supply in the Tarrant Regional Water District (TRWD) in Texas. RiverWare is combined with the Borg MOEA to solve a seven objective problem that includes systemwide performance objectives and individual reservoir storage reliability. Decisions within the formulation balance supply in multiple reservoirs and control pumping between the eastern and western parts of the system. The RiverWare simulation model is forced by two stochastic hydrology scenarios to inform how management changes in wet versus dry conditions. The second part of the presentation suggests how a broader set of RiverWare-MOEA studies can inform tradeoffs in other systems, especially in political situations where multiple actors are in conflict over finite water resources. By incorporating quantitative representations of diverse parties' objectives during the search for solutions, MOEAs may provide support for negotiations and lead to more widely beneficial water management outcomes.

Binary classification posed as a quadratically constrained quadratic ...

Indian Academy of Sciences (India)

Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to forma quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or ...

Assessment of grid-friendly collective optimization framework for distributed energy resources

DEFF Research Database (Denmark)

Pensini, Alessandro; Robinson, Matthew; Heine, Nicholas

2016-01-01

for reducing their energy bills. However, as the penetration of distributed energy resources increases, there is an increasing chance of a “tragedy of the commons” mechanism taking place, where taking advantage of tariffs for local benefit can ultimately result in power quality degradation. In this work, we...... use a scheduling optimization tool, in combination with a distribution feeder simulator, to investigate techniques that could mitigate the deleterious effect of “selfish” optimization, so that the high-penetration use of DERs to reduce operating costs remains advantageous while the quality of service...

Feedback-Based Projected-Gradient Method For Real-Time Optimization of Aggregations of Energy Resources: Preprint

Energy Technology Data Exchange (ETDEWEB)

Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bernstein, Andrey [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Simonetto, Andrea [IBM Research Center Ireland

2017-11-27

This paper develops an online optimization method to maximize the operational objectives of distribution-level distributed energy resources (DERs) while adjusting the aggregate power generated (or consumed) in response to services requested by grid operators. The design of the online algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements from the distribution network and the DERs. By virtue of this approach, the resultant algorithm can cope with inaccuracies in the representation of the AC power, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Optimality claims are established in terms of tracking of the solution of a well-posed time-varying optimization problem.

Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements

International Nuclear Information System (INIS)

Liu, Fan; Bie, Zhaohong; Liu, Shiyu; Ding, Tao

2017-01-01

Highlights: • Analyzing zonal reserve requirements for wind integrated power system. • Modeling day-ahead optimal dispatch solved by chance constrained programming theory. • Determining optimal zonal reserve demand with minimum confidence interval. • Analyzing numerical results on test and large-scale real-life power systems. - Abstract: Large-scale integration of renewable power presents a great challenge for day-ahead dispatch to manage renewable resources while provide available reserve for system security. Considering zonal reserve is an effective way to ensure reserve deliverability when network congested, a random day-ahead dispatch optimization of wind integrated power system for a least operational cost is modeled including zonal reserve requirements and N − 1 security constraints. The random model is transformed into a deterministic one based on the theory of chance constrained programming and a determination method of optimal zonal reserve demand is proposed using the minimum confidence interval. After solving the deterministic model, the stochastic simulation is conducted to verify the validity of solution. Numerical tests and results on the IEEE 39 bus system and a large-scale real-life power system demonstrate the optimal day-ahead dispatch scheme is available and the proposed method is effective for improving reserve deliverability and reducing load shedding after large-capacity power outage.

Optimal allocation of International Atomic Energy Agency inspection resources

International Nuclear Information System (INIS)

Markin, J.T.

1987-12-01

The Safeguards Department of the International Atomic Energy Agency (IAEA) conducts inspections to assure the peaceful use of a state's nuclear materials and facilities. Because of limited resources for conducting inspections, the careful disposition of inspection effort among these facilities is essential if the IAEA is to attain its safeguards goals. This report describes an optimization procedure for assigning an inspection effort to maximize attainment of IAEA goals. The procedure does not require quantitative estimates of safeguards effectiveness, material value, or facility importance. Instead, the optimization is based on qualitative, relative prioritizations of inspection activities and materials to be safeguarded. This allocation framework is applicable to an arbitrary group of facilities such as a state's fuel cycle, the facilities inspected by an operations division, or all of the facilities inspected by the IAEA

Post-approval monitoring and oversight of U.S.-initiated human subjects research in resource-constrained countries.

Science.gov (United States)

Brown, Brandon; Kinsler, Janni; Folayan, Morenike O; Allen, Karen; Cáceres, Carlos F

2014-06-01

The history of human subjects research and controversial procedures in relation to it has helped form the field of bioethics. Ethically questionable elements may be identified during research design, research implementation, management at the study site, or actions by a study's investigator or other staff. Post-approval monitoring (PAM) may prevent violations from occurring or enable their identification at an early stage. In U.S.-initiated human subjects research taking place in resource-constrained countries with limited development of research regulatory structures, arranging a site visit from a U.S. research ethics committee (REC) becomes difficult, thus creating a potential barrier to regulatory oversight by the parent REC. However, this barrier may be overcome through the use of digital technologies, since much of the world has at least remote access to the Internet. Empirical research is needed to pilot test the use of these technologies for research oversight to ensure the protection of human subjects taking part in research worldwide.

Optimal planning and operation of aggregated distributed energy resources with market participation

International Nuclear Information System (INIS)

Calvillo, C.F.; Sánchez-Miralles, A.; Villar, J.; Martín, F.

2016-01-01

Highlights: • Price-maker optimization model for planning and operation of aggregated DER. • 3 Case studies are proposed, considering different electricity pricing scenarios. • Analysis of benefits and effect on electricity prices produced by DER aggregation. • Results showed considerable benefits even for relatively small aggregations. • Results suggest that the impact on prices should not be overlooked. - Abstract: This paper analyzes the optimal planning and operation of aggregated distributed energy resources (DER) with participation in the electricity market. Aggregators manage their portfolio of resources in order to obtain the maximum benefit from the grid, while participating in the day-ahead wholesale electricity market. The goal of this paper is to propose a model for aggregated DER systems planning, considering its participation in the electricity market and its impact on the market price. The results are the optimal planning and management of DER systems, and the appropriate energy transactions for the aggregator in the wholesale day-ahead market according to the size of its aggregated resources. A price-maker approach based on representing the market competitors with residual demand curves is followed, and the impact on the price is assessed to help in the decision of using price-maker or price-taker approaches depending on the size of the aggregated resources. A deterministic programming problem with two case studies (the average scenario and the most likely scenario from the stochastic ones), and a stochastic one with a case study to account for the market uncertainty are described. For both models, market scenarios have been built from historical data of the Spanish system. The results suggest that when the aggregated resources have enough size to follow a price-maker approach and the uncertainty of the markets is considered in the planning process, the DER systems can achieve up to 50% extra economic benefits, depending on the market

Foraging optimally for home ranges

Science.gov (United States)

Mitchell, Michael S.; Powell, Roger A.

2012-01-01

Economic models predict behavior of animals based on the presumption that natural selection has shaped behaviors important to an animal's fitness to maximize benefits over costs. Economic analyses have shown that territories of animals are structured by trade-offs between benefits gained from resources and costs of defending them. Intuitively, home ranges should be similarly structured, but trade-offs are difficult to assess because there are no costs of defense, thus economic models of home-range behavior are rare. We present economic models that predict how home ranges can be efficient with respect to spatially distributed resources, discounted for travel costs, under 2 strategies of optimization, resource maximization and area minimization. We show how constraints such as competitors can influence structure of homes ranges through resource depression, ultimately structuring density of animals within a population and their distribution on a landscape. We present simulations based on these models to show how they can be generally predictive of home-range behavior and the mechanisms that structure the spatial distribution of animals. We also show how contiguous home ranges estimated statistically from location data can be misleading for animals that optimize home ranges on landscapes with patchily distributed resources. We conclude with a summary of how we applied our models to nonterritorial black bears (Ursus americanus) living in the mountains of North Carolina, where we found their home ranges were best predicted by an area-minimization strategy constrained by intraspecific competition within a social hierarchy. Economic models can provide strong inference about home-range behavior and the resources that structure home ranges by offering falsifiable, a priori hypotheses that can be tested with field observations.

Optimal Extraction and Taxation of Strategic Natural Resources: A Differential Game Approach

OpenAIRE

Pemy, Moustapha

2016-01-01

This paper studies the optimal extraction and taxation of nonrenewable natural resources. It is well known the market values of the main strategic resources such as oil, natural gas, uranium, copper,...,etc, fluctuate randomly following global and seasonal macro-economic parameters, these values are modeled using Markov switching L\\'evy processes. We formulate this problem as a differential game where the two players are the mining company whose aim is to maximize the revenues generated from ...

Optimal exploitation of a renewable resource with stochastic nonconvex technology: An analysis of extinction and survival

International Nuclear Information System (INIS)

Mitra, Tapan; Roy, Santanu

1992-11-01

This paper analyzes the possibilities of extinction and survival of a renewable resource whose technology of reproduction is both stochastic and nonconvex. In particular, the production function is subject to random shocks over time and is allowed to be nonconcave, though it eventually exhibits bounded growth. The existence of a minimum biomass below which the resource can only decrease, is allowed for. Society harvests a part of the current stock every time period over an infinite horizon so as to maximize the expected discounted sum of one period social utilities from the harvested resource. The social utility function is strictly concave. The stochastic process of optimal stocks generated by the optimal stationary policy is analyzed. The nonconvexity in the optimization problem implies that the optimal policy functions are not 'well behaved'. The behaviour of the probability of extinction (and the expected time to extinction), as a function of initial stock, is characterized for various possible configurations of the optimal policy and the technology. Sufficient conditions on the utility and production functions and the rate of impatience, are specified in order to ensure survival of the resource with probability one from some stock level (the minimum safe standard of conservation). Sufficient conditions for almost sure extinction and almost sure survival from all stock levels are also specified. These conditions are related to the corresponding conditions derived in models with deterministic and/or convex technology. 4 figs., 29 refs

Optimal exploitation of a renewable resource with stochastic nonconvex technology: An analysis of extinction and survival

Energy Technology Data Exchange (ETDEWEB)

Mitra, Tapan [Department of Economics, Cornell University, Ithaca, NY (United States); Roy, Santanu [Econometric Institute, Erasmus University, Rotterdam (Netherlands)

1992-11-01

This paper analyzes the possibilities of extinction and survival of a renewable resource whose technology of reproduction is both stochastic and nonconvex. In particular, the production function is subject to random shocks over time and is allowed to be nonconcave, though it eventually exhibits bounded growth. The existence of a minimum biomass below which the resource can only decrease, is allowed for. Society harvests a part of the current stock every time period over an infinite horizon so as to maximize the expected discounted sum of one period social utilities from the harvested resource. The social utility function is strictly concave. The stochastic process of optimal stocks generated by the optimal stationary policy is analyzed. The nonconvexity in the optimization problem implies that the optimal policy functions are not `well behaved`. The behaviour of the probability of extinction (and the expected time to extinction), as a function of initial stock, is characterized for various possible configurations of the optimal policy and the technology. Sufficient conditions on the utility and production functions and the rate of impatience, are specified in order to ensure survival of the resource with probability one from some stock level (the minimum safe standard of conservation). Sufficient conditions for almost sure extinction and almost sure survival from all stock levels are also specified. These conditions are related to the corresponding conditions derived in models with deterministic and/or convex technology. 4 figs., 29 refs.

Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization

Directory of Open Access Journals (Sweden)

Carrie Ka Yuk Lin

2017-01-01

Full Text Available This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.

Optimally managing water resources in large river basins for an uncertain future

Science.gov (United States)

Edwin A. Roehl, Jr.; Conrads, Paul

2014-01-01

Managers of large river basins face conflicting needs for water resources such as wildlife habitat, water supply, wastewater assimilative capacity, flood control, hydroelectricity, and recreation. The Savannah River Basin for example, has experienced three major droughts since 2000 that resulted in record low water levels in its reservoirs, impacting local economies for years. The Savannah River Basin’s coastal area contains municipal water intakes and the ecologically sensitive freshwater tidal marshes of the Savannah National Wildlife Refuge. The Port of Savannah is the fourth busiest in the United States, and modifications to the harbor have caused saltwater to migrate upstream, reducing the freshwater marsh’s acreage more than 50 percent since the 1970s. There is a planned deepening of the harbor that includes flow-alteration features to minimize further migration of salinity. The effectiveness of the flow-alteration features will only be known after they are constructed. One of the challenges of basin management is the optimization of water use through ongoing development, droughts, and climate change. This paper describes a model of the Savannah River Basin designed to continuously optimize regulated flow to meet prioritized objectives set by resource managers and stakeholders. The model was developed from historical data by using machine learning, making it more accurate and adaptable to changing conditions than traditional models. The model is coupled to an optimization routine that computes the daily flow needed to most efficiently meet the water-resource management objectives. The model and optimization routine are packaged in a decision support system that makes it easy for managers and stakeholders to use. Simulation results show that flow can be regulated to significantly reduce salinity intrusions in the Savannah National Wildlife Refuge while conserving more water in the reservoirs. A method for using the model to assess the effectiveness of the

Prospective Optimization with Limited Resources.

Science.gov (United States)

Snider, Joseph; Lee, Dongpyo; Poizner, Howard; Gepshtein, Sergei

2015-09-01

The future is uncertain because some forthcoming events are unpredictable and also because our ability to foresee the myriad consequences of our own actions is limited. Here we studied how humans select actions under such extrinsic and intrinsic uncertainty, in view of an exponentially expanding number of prospects on a branching multivalued visual stimulus. A triangular grid of disks of different sizes scrolled down a touchscreen at a variable speed. The larger disks represented larger rewards. The task was to maximize the cumulative reward by touching one disk at a time in a rapid sequence, forming an upward path across the grid, while every step along the path constrained the part of the grid accessible in the future. This task captured some of the complexity of natural behavior in the risky and dynamic world, where ongoing decisions alter the landscape of future rewards. By comparing human behavior with behavior of ideal actors, we identified the strategies used by humans in terms of how far into the future they looked (their "depth of computation") and how often they attempted to incorporate new information about the future rewards (their "recalculation period"). We found that, for a given task difficulty, humans traded off their depth of computation for the recalculation period. The form of this tradeoff was consistent with a complete, brute-force exploration of all possible paths up to a resource-limited finite depth. A step-by-step analysis of the human behavior revealed that participants took into account very fine distinctions between the future rewards and that they abstained from some simple heuristics in assessment of the alternative paths, such as seeking only the largest disks or avoiding the smaller disks. The participants preferred to reduce their depth of computation or increase the recalculation period rather than sacrifice the precision of computation.

Prospective Optimization with Limited Resources.

Directory of Open Access Journals (Sweden)

Joseph Snider

2015-09-01

Full Text Available The future is uncertain because some forthcoming events are unpredictable and also because our ability to foresee the myriad consequences of our own actions is limited. Here we studied how humans select actions under such extrinsic and intrinsic uncertainty, in view of an exponentially expanding number of prospects on a branching multivalued visual stimulus. A triangular grid of disks of different sizes scrolled down a touchscreen at a variable speed. The larger disks represented larger rewards. The task was to maximize the cumulative reward by touching one disk at a time in a rapid sequence, forming an upward path across the grid, while every step along the path constrained the part of the grid accessible in the future. This task captured some of the complexity of natural behavior in the risky and dynamic world, where ongoing decisions alter the landscape of future rewards. By comparing human behavior with behavior of ideal actors, we identified the strategies used by humans in terms of how far into the future they looked (their "depth of computation" and how often they attempted to incorporate new information about the future rewards (their "recalculation period". We found that, for a given task difficulty, humans traded off their depth of computation for the recalculation period. The form of this tradeoff was consistent with a complete, brute-force exploration of all possible paths up to a resource-limited finite depth. A step-by-step analysis of the human behavior revealed that participants took into account very fine distinctions between the future rewards and that they abstained from some simple heuristics in assessment of the alternative paths, such as seeking only the largest disks or avoiding the smaller disks. The participants preferred to reduce their depth of computation or increase the recalculation period rather than sacrifice the precision of computation.

Toward cognitively constrained models of language processing : A review

NARCIS (Netherlands)

Vogelzang, Margreet; Mills, Anne C.; Reitter, David; van Rij, Jacolien; Hendriks, Petra; van Rijn, Hedderik

2017-01-01

Language processing is not an isolated capacity, but is embedded in other aspects of our cognition. However, it is still largely unexplored to what extent and how language processing interacts with general cognitive resources. This question can be investigated with cognitively constrained

Constraining Glacial Runoff Contributions to Water Resources in the Cordillera Real, Bolivia using Environmental Tracers

Science.gov (United States)

Guido, Z.; McIntosh, J. C.; Papuga, S. A.

2013-12-01

Warming temperatures in recent decades have contributed to substantial reductions in glaciers in many mountain regions around the globe, including the South American Andes. Melting of these glaciers taps water resources accumulated in past climates, and the diminishing ice marks a decrease in a nonrenewable water source that begs the question: how will future water supplies be impacted by climate change. Water resource management and climate adaptation efforts can be informed by knowledge of the extent to which glaciers contribute to seasonal streamflows, but remote locations and scant monitoring often limit this quantification. In Bolivia, more than two million people draw water from watersheds fed, in part, by glaciers. The amount to which these glaciers contribute to the water supply, however, is not well constrained. We apply elemental and isotopic tracers in an end-member mixing model to quantify glacial runoff contributions to local water supplies. We present oxygen and deuterium isotopes and major anion concentrations (sulfate and chloride) of shallow groundwater, streams, reservoirs, small arroyos, and glacial runoff. Isotopic and anion mixing models suggest between 45-67% of the water measured in high altitude streams originated from within the glacial footprint during the 2011 wet season, while glacial runoff contributed about 42-53% of the water in reservoirs in the 2012 dry season. Data also show that shallow groundwater is connected to glacial-fed streams. Any future decrease in glacial runoff may contribute to a reduction in surface water supplies and lower groundwater levels downstream, perhaps below the depth of hand-dug wells common in rural communities.

Managing Deadline-constrained Bag-of-Tasks Jobs on Hybrid Clouds

OpenAIRE

Wang, Bo; Song, Ying; Sun, Yuzhong; Liu, Jun

2016-01-01

Outsourcing jobs to a public cloud is a cost-effective way to address the problem of satisfying the peak resource demand when the local cloud has insufficient resources. In this paper, we study on managing deadline-constrained bag-of-tasks jobs on hybrid clouds. We present a binary nonlinear programming (BNP) problem to model the hybrid cloud management where the utilization of physical machines (PMs) in the local cloud/cluster is maximized when the local resources are enough to satisfy the d...

A decentralized multidistrict optimization framework for system-wide pavement maintenance resource allocation

Directory of Open Access Journals (Sweden)

J. Farhan

2016-05-01

Full Text Available Historically, pavement maintenance funds have been allocated based on a centralized programme development process. Such practice, though seemingly convenient, does not lead to optimal allocation of funds since districts generally have different priorities based on the state of development and condition of their respective road networks. This paper proposes a decentralized two-phased optimization framework for pavement maintenance fund allocation considering multiple objectives and cross-district trade-off at the network level. In the proposed two-phased analysis approach, Phase-I focuses on establishing the needs and funds requirements of individual districts given multiple performance targets or objectives, while a system-wide fund appropriation strategy is selected, in Phase-II, given budget and equity constraints across competing districts. The proposed approach is illustrated using a numerical example problem for appropriating funds to three districts. The results indicated that the proposed approach is not only able to evaluate the extent to which various performance targets are achieved at the central and district level, but also maintains equity in distribution of financial resources across districts. Keywords: Pavement maintenance programming, Multiobjective, Multidistrict, Optimized resource allocation, Genetic algorithm, Dynamic programming

Mobile devices and computing cloud resources allocation for interactive applications

Directory of Open Access Journals (Sweden)

Krawczyk Henryk

2017-06-01

Full Text Available Using mobile devices such as smartphones or iPads for various interactive applications is currently very common. In the case of complex applications, e.g. chess games, the capabilities of these devices are insufficient to run the application in real time. One of the solutions is to use cloud computing. However, there is an optimization problem of mobile device and cloud resources allocation. An iterative heuristic algorithm for application distribution is proposed. The algorithm minimizes the energy cost of application execution with constrained execution time.

E-learning in medical education in resource constrained low- and middle-income countries

Science.gov (United States)

2013-01-01

Background In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC), and presents the spectrum of tools and strategies used. Methods Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators “AND” and “OR” to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Results Of the relevant articles found, most referred to e-learning in Brazil (14 articles), India (14), Egypt (10) and South Africa (10). While e-learning has been used by a variety of health workers in LMICs, the majority (58%) reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles) of which computer-assisted learning (CAL) comprised the majority (45 articles). Other approaches included simulations and the use of multimedia software (20 articles), web-based learning (14 articles), and eTutor/eMentor programs (3 articles). Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities. Conclusions E

E-learning in medical education in resource constrained low- and middle-income countries

Directory of Open Access Journals (Sweden)

Frehywot Seble

2013-02-01

Full Text Available Abstract Background In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC, and presents the spectrum of tools and strategies used. Methods Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators “AND” and “OR” to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Results Of the relevant articles found, most referred to e-learning in Brazil (14 articles, India (14, Egypt (10 and South Africa (10. While e-learning has been used by a variety of health workers in LMICs, the majority (58% reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles of which computer-assisted learning (CAL comprised the majority (45 articles. Other approaches included simulations and the use of multimedia software (20 articles, web-based learning (14 articles, and eTutor/eMentor programs (3 articles. Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities

A review of inexact optimization modeling and its application to integrated water resources management

Science.gov (United States)

Wang, Ran; Li, Yin; Tan, Qian

2015-03-01

Water is crucial in supporting people's daily life and the continual quest for socio-economic development. It is also a fundamental resource for ecosystems. Due to the associated complexities and uncertainties, as well as intensive competition over limited water resources between human beings and ecosystems, decision makers are facing increased pressure to respond effectively to various water-related issues and conflicts from an integrated point of view. This quandary requires a focused effort to resolve a wide range of issues related to water resources, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty that successfully address interactions, complexities, uncertainties, and changing conditions associated with water resources, human activities, and ecological conditions are desired, which requires a systematic investigation of the previous studies in relevant areas. Systems analysis and optimization modeling for integrated water resources management under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to stochastic, fuzzy, and interval mathematical optimization modeling are examined. Then, their applications to integrated water resources management are presented. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns over uncertainty, necessity of post-modeling analysis, and the usefulness of the development of simulation techniques.

A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

International Nuclear Information System (INIS)

Kefayat, M.; Lashkar Ara, A.; Nabavi Niaki, S.A.

2015-01-01

Highlights: • A probabilistic optimization framework incorporated with uncertainty is proposed. • A hybrid optimization approach combining ACO and ABC algorithms is proposed. • The problem is to deal with technical, environmental and economical aspects. • A fuzzy interactive approach is incorporated to solve the multi-objective problem. • Several strategies are implemented to compare with literature methods. - Abstract: In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods

Thermo economical optimization of sugar plants with environmental constraints

Energy Technology Data Exchange (ETDEWEB)

Colombo, Mauricio; Mele, Fernando Daniel; Hernandez, Maria Rosa [Universidad Nacional de Tucuman (UNT), Tucuman (Argentina). Facultad de Ciencias Exactas y Tecnologia], Email: macolombo@herrera.unt.edu.ar; Gatica, Jorge [Cleveland State University (CSU), Cleveland, OH (United States). Dept. of Chemical and Biomedical Engineering], Email: j.gatica@csuohio.edu; Silveira, Jose Luz [Universidade Estadual Paulista (FEG/UNESP), Guaratingueta, SP (Brazil). Faculdade de Engenharia. Dept. de Energia], Email: joseluz@feg.unesp.br

2009-07-01

This paper highlights the need for analysis and optimization techniques which can be applied to new energy systems and include considerations for environmental issues. These techniques have proven indispensable in dealing with the constrained optimization problem of finite natural resources and growing demands of energy. Within this framework, thermo economical optimization has gradually been brought to the forefront as a powerful tool in assisting the decision-making process. This work uses the technique of Life Cycle Analysis (LCA) as a means to include environmental indexes in the optimization process. While most of the environmental approaches formulate the optimization problem aiming to reduce residue generation without assessing the impact of this reduction on related processes, LCA considers environmental issues as an integral part of the optimization problem. A sugar cane processing plant located in Tucuman (Argentina) is selected as a case study. This example serves to highlight the importance of formulating solutions that ensure an efficient use of a common fuel to meet useful heat, shaft power, and electricity demands. (author)

Medical Optimization Network for Space Telemedicine Resources

Science.gov (United States)

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

Statistical mechanics of budget-constrained auctions

International Nuclear Information System (INIS)

Altarelli, F; Braunstein, A; Realpe-Gomez, J; Zecchina, R

2009-01-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise

Statistical mechanics of budget-constrained auctions

Science.gov (United States)

Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

2009-07-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.

Constrained variational calculus for higher order classical field theories

Energy Technology Data Exchange (ETDEWEB)

Campos, Cedric M; De Leon, Manuel; De Diego, David MartIn, E-mail: cedricmc@icmat.e, E-mail: mdeleon@icmat.e, E-mail: david.martin@icmat.e [Instituto de Ciencias Matematicas, CSIC-UAM-UC3M-UCM, Serrano 123, 28006 Madrid (Spain)

2010-11-12

We develop an intrinsic geometrical setting for higher order constrained field theories. As a main tool we use an appropriate generalization of the classical Skinner-Rusk formalism. Some examples of applications are studied, in particular to the geometrical description of optimal control theory for partial differential equations.

Constrained variational calculus for higher order classical field theories

International Nuclear Information System (INIS)

Campos, Cedric M; De Leon, Manuel; De Diego, David MartIn

2010-01-01

We develop an intrinsic geometrical setting for higher order constrained field theories. As a main tool we use an appropriate generalization of the classical Skinner-Rusk formalism. Some examples of applications are studied, in particular to the geometrical description of optimal control theory for partial differential equations.

Optimized use of resources in the context of the North-South tensions

Energy Technology Data Exchange (ETDEWEB)

Giesel, H B [Gesamtverband des deutschen Steinkohlenbergbaus, Essen (Germany)

1992-01-01

Worldwide, there is no scarcity of energy resources, however, not all can be recovered cost-effectively, but those concentrated in the Third World, however, are being intensively and increasingly exploited by the industrialized countries. The industrialized countries themselves own abundant energy resources (e.g. 'non-conventional' oils) which, however, cannot be cost-effectively recovered unless a substantially higher energy price level assures economic viability. This paper analyses fundamental questions concerning an optimized balance of interests in the field of utilization of resources between the North (rich countries) and the South (poor countries, need for energy; increasing overpopulation). Alternative solutions aiming at better conservation of cost-effectively recoverable resources to the advantage of the Third World countries are discussed as well as viable instruments to be used within the framework of an international energy policy (strategy). 10 refs.

An integrated model of water resources optimization allocation based on projection pursuit model - Grey wolf optimization method in a transboundary river basin

Science.gov (United States)

Yu, Sen; Lu, Hongwei

2018-04-01

Under the effects of global change, water crisis ranks as the top global risk in the future decade, and water conflict in transboundary river basins as well as the geostrategic competition led by it is most concerned. This study presents an innovative integrated PPMGWO model of water resources optimization allocation in a transboundary river basin, which is integrated through the projection pursuit model (PPM) and Grey wolf optimization (GWO) method. This study uses the Songhua River basin and 25 control units as examples, adopting the PPMGWO model proposed in this study to allocate the water quantity. Using water consumption in all control units in the Songhua River basin in 2015 as reference to compare with optimization allocation results of firefly algorithm (FA) and Particle Swarm Optimization (PSO) algorithms as well as the PPMGWO model, results indicate that the average difference between corresponding allocation results and reference values are 0.195 bil m3, 0.151 bil m3, and 0.085 bil m3, respectively. Obviously, the average difference of the PPMGWO model is the lowest and its optimization allocation result is closer to reality, which further confirms the reasonability, feasibility, and accuracy of the PPMGWO model. And then the PPMGWO model is adopted to simulate allocation of available water quantity in Songhua River basin in 2018, 2020, and 2030. The simulation results show water quantity which could be allocated in all controls demonstrates an overall increasing trend with reasonable and equal exploitation and utilization of water resources in the Songhua River basin in future. In addition, this study has a certain reference value and application meaning to comprehensive management and water resources allocation in other transboundary river basins.

Reducing usage of the computational resources by event driven approach to model predictive control

Science.gov (United States)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

Path-Constrained Motion Planning for Robotics Based on Kinematic Constraints

NARCIS (Netherlands)

Dijk, van N.J.M.; Wouw, van de N.; Pancras, W.C.M.; Nijmeijer, H.

2007-01-01

Common robotic tracking tasks consist of motions along predefined paths. The design of time-optimal path-constrained trajectories for robotic applications is discussed in this paper. To increase industrial applicability, the proposed method accounts for robot kinematics together with actuator

Optimizing time and resource allocation trade-offs for investment into morphological and behavioral defense

DEFF Research Database (Denmark)

Steiner, Uli; Pfeiffer, Thomas

2007-01-01

pronounced at intermediate environmental conditions. Optimizing single traits generally leads to a more pronounced response of the defense traits, which implies that studying single traits leads to an overestimation of their response to predation. Behavioral defense and morphological defense compensate......Prey organisms are confronted with time and resource allocation trade-offs. Time allocation trade-offs partition time, for example, between foraging effort to acquire resources and behavioral defense. Resource allocation trade-offs partition the acquired resources between multiple traits...... for and augment each other depending on predator densities and the effectiveness of the defense mechanisms. In the presence of time constraints, the model shows peak investment into morphological and behavioral defense at intermediate resource levels....

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

Joint Chance-Constrained Dynamic Programming

Science.gov (United States)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J. Bob

2012-01-01

This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step.

Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

Directory of Open Access Journals (Sweden)

Heba-Allah I. ElAzab

2018-05-01

Full Text Available This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED. To reduce combined emission costs, integrating more renewable energy resources (RER and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired power plants. The Water Cycle Optimization Algorithm (WCOA is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO2 emission costs applying the Water Cycle Optimization Algorithm (WCOA. To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA as a meta-heuristic technique.

Optimizing the structure of financial resources of industrial enterprises in the market economy mechanism

Directory of Open Access Journals (Sweden)

A. Vlasov

2015-01-01

Full Text Available The article deals with the optimization of the structure of financial resources of industrial enterprises in the market economy mechanism. The slowdown of the Russian economy force companies to promote more accurate system financial planning its activities. In modern economic conditions the company's performance is largely dependent on the ability of management to more accurately predict financial flows, as well as more accurately predict the financial and human resources to ensure solvency of the enterprise, thus more competent to form the strategy of development of the organization.Goal / task. The aim of the article the search for the optimal structure of financial resources of industrial enterprises in the market economy mechanism and to develop proposals for the sustainable development of the enterprise. The task of this article is to investigate the structure of financial resources of the enterprise, in a deteriorating economic situation that must be considered in the sustainable development of industrial enterprises.Methodology. In conducting this study the main sources of the original data were the materials of the state statistics, the works of famous economists. The basis of the methodological developments based on comparative methods of analysis.Results. Given the concept of optimizing the structure of financial resources of the industrial enterprises. It shows the influence of external and internal factors affecting the stability of the industrial enterprises. Highlighted the impact of the economic situation on the role of these factors.Conclusions / significance. In the current economic conditions of the state and the new economic realities, it is necessary to focus to industrial enterprises to conduct an effective economic policy, thereby improving the financial stability of the enterprise.

Improved Sensitivity Relations in State Constrained Optimal Control

International Nuclear Information System (INIS)

Bettiol, Piernicola; Frankowska, Hélène; Vinter, Richard B.

2015-01-01

Sensitivity relations in optimal control provide an interpretation of the costate trajectory and the Hamiltonian, evaluated along an optimal trajectory, in terms of gradients of the value function. While sensitivity relations are a straightforward consequence of standard transversality conditions for state constraint free optimal control problems formulated in terms of control-dependent differential equations with smooth data, their verification for problems with either pathwise state constraints, nonsmooth data, or for problems where the dynamic constraint takes the form of a differential inclusion, requires careful analysis. In this paper we establish validity of both ‘full’ and ‘partial’ sensitivity relations for an adjoint state of the maximum principle, for optimal control problems with pathwise state constraints, where the underlying control system is described by a differential inclusion. The partial sensitivity relation interprets the costate in terms of partial Clarke subgradients of the value function with respect to the state variable, while the full sensitivity relation interprets the couple, comprising the costate and Hamiltonian, as the Clarke subgradient of the value function with respect to both time and state variables. These relations are distinct because, for nonsmooth data, the partial Clarke subdifferential does not coincide with the projection of the (full) Clarke subdifferential on the relevant coordinate space. We show for the first time (even for problems without state constraints) that a costate trajectory can be chosen to satisfy the partial and full sensitivity relations simultaneously. The partial sensitivity relation in this paper is new for state constraint problems, while the full sensitivity relation improves on earlier results in the literature (for optimal control problems formulated in terms of Lipschitz continuous multifunctions), because a less restrictive inward pointing hypothesis is invoked in the proof, and because

Diagnosis and outcome of birth asphyxia in resource constrained health care set up

International Nuclear Information System (INIS)

Zaman, S.; Shah, S.A.; Mehmood, S.; Shahzad, S.; Munir, M.; Mushtaq, A.

2017-01-01

Objective: To determine morbidity and mortality of neonates with low APGAR score in a resource constrained health care set up. Study Design: Prospective descriptive study. Place and Duration of Study: The study was carried out in combined military hospital Attock, from Jan 2013 to Jan 2015. Material and Methods: All term neonates with 37 completed weeks of gestation and APGAR score less than 7 were included in the study. APGAR score was calculated by an attending pediatrician, gynecologist or trained female nurse at 0 and 5 minutes. In Neonatal Intensive Care Unit [NICU] the babies were daily examined by pediatrician. Outcome was documented in term of morbidity i.e. fits and mortality i.e. death of babies. Results: Total number of neonates included in the study were 85 of which 55 (65%) were males and 30 (35%) were females. Of the total neonates 65 (76%) were discharged in satisfactory conditions and 20 (24%) expired during stay in the hospital. The mean APGAR score of newborns was 4.98 +- 0.98 at 5 minutes. During stay in hospital 46 (54%) were diagnosed to have hypoxic ischemic encephalopathy 2 (HIE2), those diagnosed with HIE3 were 5 (6%) and the rest 14 (16%) with HIE1. Conclusion: Low APGAR score is an important cause of admission to NICU. Low APGAR score was found associated with increased risk of fits in neonates and one of the most important cause of mortality in our set up. (author)

Optimal resource allocation and load scheduling for a multi-commodity smart energy system

NARCIS (Netherlands)

Blaauwbroek, N.; Nguyen, H.P.; Shi, H.; Kamphuis, I.G.; Kling, W.L.; Konsman, M.J.

2015-01-01

The increasing introduction of district heating systems together with hybrid energy appliances as heat pumps and micro-combined heat and power installations, results in new opportunities for optimizing the available resources in multi-commodity smart energy systems, including electricity, heat and

A Generalized Decision Framework Using Multi-objective Optimization for Water Resources Planning

Science.gov (United States)

Basdekas, L.; Stewart, N.; Triana, E.

2013-12-01

Colorado Springs Utilities (CSU) is currently engaged in an Integrated Water Resource Plan (IWRP) to address the complex planning scenarios, across multiple time scales, currently faced by CSU. The modeling framework developed for the IWRP uses a flexible data-centered Decision Support System (DSS) with a MODSIM-based modeling system to represent the operation of the current CSU raw water system coupled with a state-of-the-art multi-objective optimization algorithm. Three basic components are required for the framework, which can be implemented for planning horizons ranging from seasonal to interdecadal. First, a water resources system model is required that is capable of reasonable system simulation to resolve performance metrics at the appropriate temporal and spatial scales of interest. The system model should be an existing simulation model, or one developed during the planning process with stakeholders, so that 'buy-in' has already been achieved. Second, a hydrologic scenario tool(s) capable of generating a range of plausible inflows for the planning period of interest is required. This may include paleo informed or climate change informed sequences. Third, a multi-objective optimization model that can be wrapped around the system simulation model is required. The new generation of multi-objective optimization models do not require parameterization which greatly reduces problem complexity. Bridging the gap between research and practice will be evident as we use a case study from CSU's planning process to demonstrate this framework with specific competing water management objectives. Careful formulation of objective functions, choice of decision variables, and system constraints will be discussed. Rather than treating results as theoretically Pareto optimal in a planning process, we use the powerful multi-objective optimization models as tools to more efficiently and effectively move out of the inferior decision space. The use of this framework will help CSU

Optimization with PDE constraints ESF networking program 'OPTPDE'

CERN Document Server

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).

Performance evaluation of multi-stratum resources optimization with network functions virtualization for cloud-based radio over optical fiber networks.

Science.gov (United States)

Yang, Hui; He, Yongqi; Zhang, Jie; Ji, Yuefeng; Bai, Wei; Lee, Young

2016-04-18

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate the services in optical networks. In view of this, this study extends to consider the multiple dimensional resources optimization of radio, optical and BBU processing in 5G age. We propose a novel multi-stratum resources optimization (MSRO) architecture with network functions virtualization for cloud-based radio over optical fiber networks (C-RoFN) using software defined control. A global evaluation scheme (GES) for MSRO in C-RoFN is introduced based on the proposed architecture. The MSRO can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical and BBU resources effectively to maximize radio coverage. The efficiency and feasibility of the proposed architecture are experimentally demonstrated on OpenFlow-based enhanced SDN testbed. The performance of GES under heavy traffic load scenario is also quantitatively evaluated based on MSRO architecture in terms of resource occupation rate and path provisioning latency, compared with other provisioning scheme.

Modeling and optimal resources allocation of a novel tri-distributed generation system based on sustainable energy resources

International Nuclear Information System (INIS)

Soheyli, Saman; Mehrjoo, Mehri; Shafiei Mayam, Mohamad Hossein

2017-01-01

the ability to operate independently. In order to obtain the optimized solutions for each system component, a genetic optimization algorithm is applied to solve a resources allocation problem. The possibility of implementation in distant regions, considerable reduction in fuel consumption and pollution are the advantages of the proposed system. The numerical results demonstrate that the fossil fuels consumption and the pollution, respectively, are reduced up to 154 and 207 times more than common separated production (SP) systems. Moreover, power grid and other auxiliary systems requirements are reduced to less than 1%.

Optimizing Endoscope Reprocessing Resources Via Process Flow Queuing Analysis.

Science.gov (United States)

Seelen, Mark T; Friend, Tynan H; Levine, Wilton C

2018-05-04

The Massachusetts General Hospital (MGH) is merging its older endoscope processing facilities into a single new facility that will enable high-level disinfection of endoscopes for both the ORs and Endoscopy Suite, leveraging economies of scale for improved patient care and optimal use of resources. Finalized resource planning was necessary for the merging of facilities to optimize staffing and make final equipment selections to support the nearly 33,000 annual endoscopy cases. To accomplish this, we employed operations management methodologies, analyzing the physical process flow of scopes throughout the existing Endoscopy Suite and ORs and mapping the future state capacity of the new reprocessing facility. Further, our analysis required the incorporation of historical case and reprocessing volumes in a multi-server queuing model to identify any potential wait times as a result of the new reprocessing cycle. We also performed sensitivity analysis to understand the impact of future case volume growth. We found that our future-state reprocessing facility, given planned capital expenditures for automated endoscope reprocessors (AERs) and pre-processing sinks, could easily accommodate current scope volume well within the necessary pre-cleaning-to-sink reprocessing time limit recommended by manufacturers. Further, in its current planned state, our model suggested that the future endoscope reprocessing suite at MGH could support an increase in volume of at least 90% over the next several years. Our work suggests that with simple mathematical analysis of historic case data, significant changes to a complex perioperative environment can be made with ease while keeping patient safety as the top priority.

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.

An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks

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Guangyuan Wu

2018-01-01

Full Text Available In Body Area Networks (BANs, how to achieve energy management to extend the lifetime of the body area networks system is one of the most critical problems. In this paper, we design a body area network system powered by renewable energy, in which the sensors carried by patient with energy harvesting module can transmit data to a personal device. We do not require any a priori knowledge of the stochastic nature of energy harvesting and energy consumption. We formulate a user utility optimization problem. We use Lyapunov Optimization techniques to decompose the problem into three sub-problems, i.e., battery management, collecting rate control and transmission power allocation. We propose an online resource allocation algorithm to achieve two major goals: (1 balancing sensors’ energy harvesting and energy consumption while stabilizing the BANs system; and (2 maximizing the user utility. Performance analysis addresses required battery capacity, bounded data queue length and optimality of the proposed algorithm. Simulation results verify the optimization of algorithm.

Multiobjective optimization of urban water resources: Moving toward more practical solutions

Science.gov (United States)

Mortazavi, Mohammad; Kuczera, George; Cui, Lijie

2012-03-01

The issue of drought security is of paramount importance for cities located in regions subject to severe prolonged droughts. The prospect of "running out of water" for an extended period would threaten the very existence of the city. Managing drought security for an urban water supply is a complex task involving trade-offs between conflicting objectives. In this paper a multiobjective optimization approach for urban water resource planning and operation is developed to overcome practically significant shortcomings identified in previous work. A case study based on the headworks system for Sydney (Australia) demonstrates the approach and highlights the potentially serious shortcomings of Pareto optimal solutions conditioned on short climate records, incomplete decision spaces, and constraints to which system response is sensitive. Where high levels of drought security are required, optimal solutions conditioned on short climate records are flawed. Our approach addresses drought security explicitly by identifying approximate optimal solutions in which the system does not "run dry" in severe droughts with expected return periods up to a nominated (typically large) value. In addition, it is shown that failure to optimize the full mix of interacting operational and infrastructure decisions and to explore the trade-offs associated with sensitive constraints can lead to significantly more costly solutions.

Distributed optimal coordination for distributed energy resources in power systems

DEFF Research Database (Denmark)

Wu, Di; Yang, Tao; Stoorvogel, A.

2017-01-01

Driven by smart grid technologies, distributed energy resources (DERs) have been rapidly developing in recent years for improving reliability and efficiency of distribution systems. Emerging DERs require effective and efficient coordination in order to reap their potential benefits. In this paper......, we consider an optimal DER coordination problem over multiple time periods subject to constraints at both system and device levels. Fully distributed algorithms are proposed to dynamically and automatically coordinate distributed generators with multiple/single storages. With the proposed algorithms...

A Multiuser Manufacturing Resource Service Composition Method Based on the Bees Algorithm

Directory of Open Access Journals (Sweden)

Yongquan Xie

2015-01-01

Full Text Available In order to realize an optimal resource service allocation in current open and service-oriented manufacturing model, multiuser resource service composition (RSC is modeled as a combinational and constrained multiobjective problem. The model takes into account both subjective and objective quality of service (QoS properties as representatives to evaluate a solution. The QoS properties aggregation and evaluation techniques are based on existing researches. The basic Bees Algorithm is tailored for finding a near optimal solution to the model, since the basic version is only proposed to find a desired solution in continuous domain and thus not suitable for solving the problem modeled in our study. Particular rules are designed for handling the constraints and finding Pareto optimality. In addition, the established model introduces a trusted service set to each user so that the algorithm could start by searching in the neighbor of more reliable service chains (known as seeds than those randomly generated. The advantages of these techniques are validated by experiments in terms of success rate, searching speed, ability of avoiding ingenuity, and so forth. The results demonstrate the effectiveness of the proposed method in handling multiuser RSC problems.

Theoretical Aspects of Optimizing the Allocation of Public Financial Resources at Local Level

OpenAIRE

Eugen DOGARIU

2010-01-01

The allocation of financial resources at local, but also at central level, is an issue especially since in times of crisis, finding the optimum way to spend public funds concerns all authorities. This paper aims to identify the ways in which, by leaving from the division of powers based on the allocation of resources and tools available, the local authorities can identify an optimal level of public expenditure so as to achieve a maximum level of using them. Also, the paper seeks to identify t...

Constrained Burn Optimization for the International Space Station

Science.gov (United States)

Brown, Aaron J.; Jones, Brandon A.

2017-01-01

In long-term trajectory planning for the International Space Station (ISS), translational burns are currently targeted sequentially to meet the immediate trajectory constraints, rather than simultaneously to meet all constraints, do not employ gradient-based search techniques, and are not optimized for a minimum total deltav (v) solution. An analytic formulation of the constraint gradients is developed and used in an optimization solver to overcome these obstacles. Two trajectory examples are explored, highlighting the advantage of the proposed method over the current approach, as well as the potential v and propellant savings in the event of propellant shortages.

Coordinated Optimization of Distributed Energy Resources and Smart Loads in Distribution Systems: Preprint

Energy Technology Data Exchange (ETDEWEB)

Yang, Rui; Zhang, Yingchen

2016-08-01

Distributed energy resources (DERs) and smart loads have the potential to provide flexibility to the distribution system operation. A coordinated optimization approach is proposed in this paper to actively manage DERs and smart loads in distribution systems to achieve the optimal operation status. A three-phase unbalanced Optimal Power Flow (OPF) problem is developed to determine the output from DERs and smart loads with respect to the system operator's control objective. This paper focuses on coordinating PV systems and smart loads to improve the overall voltage profile in distribution systems. Simulations have been carried out in a 12-bus distribution feeder and results illustrate the superior control performance of the proposed approach.

Toward Cognitively Constrained Models of Language Processing: A Review

Directory of Open Access Journals (Sweden)

Margreet Vogelzang

2017-09-01

Full Text Available Language processing is not an isolated capacity, but is embedded in other aspects of our cognition. However, it is still largely unexplored to what extent and how language processing interacts with general cognitive resources. This question can be investigated with cognitively constrained computational models, which simulate the cognitive processes involved in language processing. The theoretical claims implemented in cognitive models interact with general architectural constraints such as memory limitations. This way, it generates new predictions that can be tested in experiments, thus generating new data that can give rise to new theoretical insights. This theory-model-experiment cycle is a promising method for investigating aspects of language processing that are difficult to investigate with more traditional experimental techniques. This review specifically examines the language processing models of Lewis and Vasishth (2005, Reitter et al. (2011, and Van Rij et al. (2010, all implemented in the cognitive architecture Adaptive Control of Thought—Rational (Anderson et al., 2004. These models are all limited by the assumptions about cognitive capacities provided by the cognitive architecture, but use different linguistic approaches. Because of this, their comparison provides insight into the extent to which assumptions about general cognitive resources influence concretely implemented models of linguistic competence. For example, the sheer speed and accuracy of human language processing is a current challenge in the field of cognitive modeling, as it does not seem to adhere to the same memory and processing capacities that have been found in other cognitive processes. Architecture-based cognitive models of language processing may be able to make explicit which language-specific resources are needed to acquire and process natural language. The review sheds light on cognitively constrained models of language processing from two angles: we

A linear bi-level multi-objective program for optimal allocation of water resources.

Directory of Open Access Journals (Sweden)

Ijaz Ahmad

Full Text Available This paper presents a simple bi-level multi-objective linear program (BLMOLP with a hierarchical structure consisting of reservoir managers and several water use sectors under a multi-objective framework for the optimal allocation of limited water resources. Being the upper level decision makers (i.e., leader in the hierarchy, the reservoir managers control the water allocation system and tend to create a balance among the competing water users thereby maximizing the total benefits to the society. On the other hand, the competing water use sectors, being the lower level decision makers (i.e., followers in the hierarchy, aim only to maximize individual sectoral benefits. This multi-objective bi-level optimization problem can be solved using the simultaneous compromise constraint (SICCON technique which creates a compromise between upper and lower level decision makers (DMs, and transforms the multi-objective function into a single decision-making problem. The bi-level model developed in this study has been applied to the Swat River basin in Pakistan for the optimal allocation of water resources among competing water demand sectors and different scenarios have been developed. The application of the model in this study shows that the SICCON is a simple, applicable and feasible approach to solve the BLMOLP problem. Finally, the comparisons of the model results show that the optimization model is practical and efficient when it is applied to different conditions with priorities assigned to various water users.

The Alberta dilemma: optimal sharing of a water resource by an agricultural and an oil sector

NARCIS (Netherlands)

Gaudet, G.; Moreaux, M.; Withagen, C.A.A.M.

2006-01-01

We fully characterize the optimal time paths of production and water usage by an agricultural and an oil sector that share a limited water resource. We show that for any given water stock, if the oil stock is sufficiently large, it will become optimal to have a phase during which the agricultural

Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

Directory of Open Access Journals (Sweden)

Xiao-feng Xu

2017-01-01

Full Text Available Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

Science.gov (United States)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Conditions for the Solvability of the Linear Programming Formulation for Constrained Discounted Markov Decision Processes

Energy Technology Data Exchange (ETDEWEB)

Dufour, F., E-mail: dufour@math.u-bordeaux1.fr [Institut de Mathématiques de Bordeaux, INRIA Bordeaux Sud Ouest, Team: CQFD, and IMB (France); Prieto-Rumeau, T., E-mail: tprieto@ccia.uned.es [UNED, Department of Statistics and Operations Research (Spain)

2016-08-15

We consider a discrete-time constrained discounted Markov decision process (MDP) with Borel state and action spaces, compact action sets, and lower semi-continuous cost functions. We introduce a set of hypotheses related to a positive weight function which allow us to consider cost functions that might not be bounded below by a constant, and which imply the solvability of the linear programming formulation of the constrained MDP. In particular, we establish the existence of a constrained optimal stationary policy. Our results are illustrated with an application to a fishery management problem.

Optimizing Power–Frequency Droop Characteristics of Distributed Energy Resources

Energy Technology Data Exchange (ETDEWEB)

Guggilam, Swaroop S.; Zhao, Changhong; Dall Anese, Emiliano; Chen, Yu Christine; Dhople, Sairaj V.

2018-05-01

This paper outlines a procedure to design power-frequency droop slopes for distributed energy resources (DERs) installed in distribution networks to optimally participate in primary frequency response. In particular, the droop slopes are engineered such that DERs respond in proportion to their power ratings and they are not unfairly penalized in power provisioning based on their location in the distribution network. The main contribution of our approach is that a guaranteed level of frequency regulation can be guaranteed at the feeder head, while ensuring that the outputs of individual DERs conform to some well-defined notion of fairness. The approach we adopt leverages an optimization-based perspective and suitable linearizations of the power-flow equations to embed notions of fairness and information regarding the physics of the power flows within the distribution network into the droop slopes. Time-domain simulations from a differential algebraic equation model of the 39-bus New England test-case system augmented with three instances of the IEEE 37-node distribution-network with frequency-sensitive DERs are provided to validate our approach.

Energy Resource Planning. Optimal utilization of energy resources

International Nuclear Information System (INIS)

Miclescu, T.; Domschke, W.; Bazacliu, G.; Dumbrava, V.

1996-01-01

For a thermal power plants system, the primary energy resources cost constitutes a significant percentage of the total system operational cost. Therefore a small percentage saving in primary energy resource allocation cost for a long term, often turns out to be a significant monetary value. In recent years, with a rapidly changing fuel supply situation, including the impact of energy policies changing, this area has become extremely sensitive. Natural gas availability has been restricted in many areas, coal production and transportation cost have risen while productivity has decreased, oil imports have increased and refinery capacity failed to meet demand. The paper presents a mathematical model and a practical procedure to solve the primary energy resource allocation. The objectives is to minimise the total energy cost over the planning period subject to constraints with regards to primary energy resource, transportation and energy consumption. Various aspects of the proposed approach are discussed, and its application to a power system is illustrated.(author) 2 figs., 1 tab., 3 refs

Optimal and Fair Resource Allocation for Multiuser Wireless Multimedia Transmissions

Directory of Open Access Journals (Sweden)

Zhangyu Guan

2009-01-01

Full Text Available This paper presents an optimal and fair strategy for multiuser multimedia radio resource allocation (RRA based on coopetition, which suggests a judicious mixture of competition and cooperation. We formulate the co-opetition strategy as sum utility maximization at constraints from both Physical (PHY and Application (APP layers. We show that the maximization can be solved efficiently employing the well-defined Layering as Optimization Decomposition (LOD method. Moreover, the coopetition strategy is applied to power allocation among multiple video users, and evaluated through comparing with existing- competition based strategy. Numerical results indicate that, the co-opetition strategy adapts the best to the changes of network conditions, participating users, and so forth. It is also shown that the coopetition can lead to an improved number of satisfied users, and in the meanwhile provide more flexible tradeoff between system efficiency and fairness among users.

Optimization in radiological protection

International Nuclear Information System (INIS)

Acosta Perez, Clarice de Freitas

1996-01-01

The optimization concept in radiation protection is, in its essence, practical. In each aspect that we deal with the man, it is necessary to take frequent decisions such as: what is the protection level to be pursued, since the protection levels under consideration provide doses lower than the appropriate annual limits. The optimization gives a basic framework of the minding that is appropriate to conduct to a balance kind of the resources available for the protection and protection level obtained against a multitude of factors and constrains in a manner to obtain the best result. In this work, was performed the optimization, from the radiation protection point of view, of a facility project who enclose two shielded hot cells where will be handled UO 2 small plate with 50% of U-235 burn-up, irradiated in the research swimming pool reactor, IEA-R1. To obtain this goal were specified the relevant factors and criteria, were applied the main techniques used in a decision-making in radiological protection, presently adopted and was performed a sensibility study of the factors and criteria used in this work. In order to obtain a greater agility in applying the techniques for decision-making was developed a micro computer program. (author)

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

Science.gov (United States)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

A brief introduction to continuous evolutionary optimization

CERN Document Server

Kramer, Oliver

2014-01-01

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel ...

The bounds of feasible space on constrained nonconvex quadratic programming

Science.gov (United States)

Zhu, Jinghao

2008-03-01

This paper presents a method to estimate the bounds of the radius of the feasible space for a class of constrained nonconvex quadratic programmingsE Results show that one may compute a bound of the radius of the feasible space by a linear programming which is known to be a P-problem [N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica 4 (1984) 373-395]. It is proposed that one applies this method for using the canonical dual transformation [D.Y. Gao, Canonical duality theory and solutions to constrained nonconvex quadratic programming, J. Global Optimization 29 (2004) 377-399] for solving a standard quadratic programming problem.

The equivalence of multi-criteria methods for radiotherapy plan optimization

International Nuclear Information System (INIS)

Breedveld, Sebastiaan; Storchi, Pascal R M; Heijmen, Ben J M

2009-01-01

Several methods can be used to achieve multi-criteria optimization of radiation therapy treatment planning, which strive for Pareto-optimality. The property of the solution being Pareto optimal is desired, because it guarantees that no criteria can be improved without deteriorating another criteria. The most widely used methods are the weighted-sum method, in which the different treatment objectives are weighted, and constrained optimization methods, in which treatment goals are set and the algorithm has to find the best plan fulfilling these goals. The constrained method used in this paper, the 2pεc (2-phase ε-constraint) method is based on the ε-constraint method, which generates Pareto-optimal solutions. Both approaches are uniquely related to each other. In this paper, we will show that it is possible to switch from the constrained method to the weighted-sum method by using the Lagrange multipliers from the constrained optimization problem, and vice versa by setting the appropriate constraints. In general, the theory presented in this paper can be useful in cases where a new situation is slightly different from the original situation, e.g. in online treatment planning, with deformations of the volumes of interest, or in automated treatment planning, where changes to the automated plan have to be made. An example of the latter is given where the planner is not satisfied with the result from the constrained method and wishes to decrease the dose in a structure. By using the Lagrange multipliers, a weighted-sum optimization problem is constructed, which generates a Pareto-optimal solution in the neighbourhood of the original plan, but fulfills the new treatment objectives.

Dynamic Convex Duality in Constrained Utility Maximization

OpenAIRE

Li, Yusong; Zheng, Harry

2016-01-01

In this paper, we study a constrained utility maximization problem following the convex duality approach. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of FBSDEs plus additional conditions. Such formulation then allows us to explicitly characterize the primal optimal control as a function of the adjoint process coming from the dual FBSDEs in a dynamic fashion and vice versa. Moreover, we also...

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Oussama Ait Sahed

2015-01-01

Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

International Nuclear Information System (INIS)

Salazar, Daniel; Rocco, Claudio M.; Galvan, Blas J.

2006-01-01

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature

Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

Energy Technology Data Exchange (ETDEWEB)

Salazar, Daniel [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain) and Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: danielsalazaraponte@gmail.com; Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve; Galvan, Blas J. [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain)]. E-mail: bgalvan@step.es

2006-09-15

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.

Constrained convex minimization via model-based excessive gap

OpenAIRE

Tran Dinh, Quoc; Cevher, Volkan

2014-01-01

We introduce a model-based excessive gap technique to analyze first-order primal- dual methods for constrained convex minimization. As a result, we construct new primal-dual methods with optimal convergence rates on the objective residual and the primal feasibility gap of their iterates separately. Through a dual smoothing and prox-function selection strategy, our framework subsumes the augmented Lagrangian, and alternating methods as special cases, where our rates apply.

Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

Science.gov (United States)

Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar

2013-11-01

Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal

Optimization algorithms and applications

CERN Document Server

Arora, Rajesh Kumar

2015-01-01

Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc

Minimum Time Trajectory Optimization of CNC Machining with Tracking Error Constraints

Directory of Open Access Journals (Sweden)

Qiang Zhang

2014-01-01

Full Text Available An off-line optimization approach of high precision minimum time feedrate for CNC machining is proposed. Besides the ordinary considered velocity, acceleration, and jerk constraints, dynamic performance constraint of each servo drive is also considered in this optimization problem to improve the tracking precision along the optimized feedrate trajectory. Tracking error is applied to indicate the servo dynamic performance of each axis. By using variable substitution, the tracking error constrained minimum time trajectory planning problem is formulated as a nonlinear path constrained optimal control problem. Bang-bang constraints structure of the optimal trajectory is proved in this paper; then a novel constraint handling method is proposed to realize a convex optimization based solution of the nonlinear constrained optimal control problem. A simple ellipse feedrate planning test is presented to demonstrate the effectiveness of the approach. Then the practicability and robustness of the trajectory generated by the proposed approach are demonstrated by a butterfly contour machining example.

Connecting Colorado's Renewable Resources to the Markets in a Cabon-Constrained Electricity Sector

Energy Technology Data Exchange (ETDEWEB)

None

2009-12-31

The benchmark goal that drives the report is to achieve a 20 percent reduction in carbon dioxide (CO{sub 2}) emissions in Colorado's electricity sector below 2005 levels by 2020. We refer to this as the '20 x 20 goal.' In discussing how to meet this goal, the report concentrates particularly on the role of utility-scale renewable energy and high-voltage transmission. An underlying recognition is that any proposed actions must not interfere with electric system reliability and should minimize financial impacts on customers and utilities. The report also describes the goals of Colorado's New Energy Economy5 - identified here, in summary, as the integration of energy, environment, and economic policies that leads to an increased quality of life in Colorado. We recognize that a wide array of options are under constant consideration by professionals in the electric industry, and the regulatory community. Many options are under discussion on this topic, and the costs and benefits of the options are inherently difficult to quantify. Accordingly, this report should not be viewed as a blueprint with specific recommendations for the timing, siting, and sizing of generating plants and high-voltage transmission lines. We convened the project with the goal of supplying information inputs for consideration by the state's electric utilities, legislators, regulators, and others as we work creatively to shape our electricity sector in a carbon-constrained world. The report addresses various issues that were raised in the Connecting Colorado's Renewable Resources to the Markets report, also known as the SB07-91 Report. That report was produced by the Senate Bill 2007-91 Renewable Resource Generation Development Areas Task Force and presented to the Colorado General Assembly in 2007. The SB07-91 Report provided the Governor, the General Assembly, and the people of Colorado with an assessment of the capability of Colorado's utility-scale renewable

Robust media processing on programmable power-constrained systems

Science.gov (United States)

McVeigh, Jeff

2005-03-01

To achieve consumer-level quality, media systems must process continuous streams of audio and video data while maintaining exacting tolerances on sampling rate, jitter, synchronization, and latency. While it is relatively straightforward to design fixed-function hardware implementations to satisfy worst-case conditions, there is a growing trend to utilize programmable multi-tasking solutions for media applications. The flexibility of these systems enables support for multiple current and future media formats, which can reduce design costs and time-to-market. This paper provides practical engineering solutions to achieve robust media processing on such systems, with specific attention given to power-constrained platforms. The techniques covered in this article utilize the fundamental concepts of algorithm and software optimization, software/hardware partitioning, stream buffering, hierarchical prioritization, and system resource and power management. A novel enhancement to dynamically adjust processor voltage and frequency based on buffer fullness to reduce system power consumption is examined in detail. The application of these techniques is provided in a case study of a portable video player implementation based on a general-purpose processor running a non real-time operating system that achieves robust playback of synchronized H.264 video and MP3 audio from local storage and streaming over 802.11.

A one-layer recurrent neural network for constrained nonsmooth optimization.

Science.gov (United States)

Liu, Qingshan; Wang, Jun

2011-10-01

This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

Science.gov (United States)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

Multiobjective Optimization Model for Wind Power Allocation

Directory of Open Access Journals (Sweden)

Juan Alemany

2017-01-01

Full Text Available There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented ε-constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.

Comparative Study on the Optimization Path of Industrial Value Chain in China’s Resource-Based Cities

Directory of Open Access Journals (Sweden)

Menglin Xing

2018-04-01

Full Text Available China’s resource-based cities have currently entered a period of comprehensive transformation. The differences in the economic and technical environment and significant policy orientation make it unique to some extent. This study applied value chain theory to analyze the industrial value chain of China’s resource-based cities, and three important types of optimization paths that have been applied differently by different cities were proposed. Grey relational analysis was used to compare the comprehensive value creation capacity of the three paths and its relationship with the comparative advantage of local industry. We found that a circular economy system has significant capacity to optimize economic and social value and favorable prospects for environmental value. However, this may have obvious instability in early periods of transition. This disadvantage can be remedied by cultivating related industries that have significantly comprehensive advantages in the early period. In the long term, the other two paths need to be combined with cultivating emerging industries. Finally, we found that the value creation capacity of China’s resource-based cities has roots in optimization of the industrial value chain rather than enhancement of the industrial comparative advantage. The reason for this is that value creation capacity has not yet been transformed into a source of industrial comparative advantage.

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Science.gov (United States)

Ehsan, Shoaib; Clark, Adrian F; Naveed ur Rehman; McDonald-Maier, Klaus D

2015-07-10

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

Directory of Open Access Journals (Sweden)

Shoaib Ehsan

2015-07-01

Full Text Available The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF, allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video. Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44% in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

Science.gov (United States)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

A chance-constrained stochastic approach to intermodal container routing problems.

Science.gov (United States)

Zhao, Yi; Liu, Ronghui; Zhang, Xi; Whiteing, Anthony

2018-01-01

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost.

A new approach to nonlinear constrained Tikhonov regularization

KAUST Repository

Ito, Kazufumi

2011-09-16

We present a novel approach to nonlinear constrained Tikhonov regularization from the viewpoint of optimization theory. A second-order sufficient optimality condition is suggested as a nonlinearity condition to handle the nonlinearity of the forward operator. The approach is exploited to derive convergence rate results for a priori as well as a posteriori choice rules, e.g., discrepancy principle and balancing principle, for selecting the regularization parameter. The idea is further illustrated on a general class of parameter identification problems, for which (new) source and nonlinearity conditions are derived and the structural property of the nonlinearity term is revealed. A number of examples including identifying distributed parameters in elliptic differential equations are presented. © 2011 IOP Publishing Ltd.

A one-layer recurrent neural network for constrained nonconvex optimization.

Science.gov (United States)

Li, Guocheng; Yan, Zheng; Wang, Jun

2015-01-01

In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.

Optimization of horizontal microcode within and beyond basic blocks: an application of processor scheduling with resources

Energy Technology Data Exchange (ETDEWEB)

Fisher, J.A.

1979-10-01

Microprogram optimization is the rearrangement of microcode written vertically, with one operation issued per step, into legal horizontal microinstructions, in which several operations are issued each instruction cycle. The rearrangement is done in a way that approximately minimizes the running time of the code. This problem is identified with the problem of processor scheduling with resource constraints. Thus, the problem of optimizing basic blocks of microcode can be seen to be np-complete; however, approximate methods for basic blocks which have good records in other, similar scheduling environments can be used. In priority list scheduling the tasks are ordered according to some evaluation function, and then schedules are found by repeated scans of the list. Several evaluation functions are shown to perform very well on large samples of various classes of random data-precedence graphs with characteristics similar to those derived from microprograms. A method of spotting resource bottlenecks in the derived data-precedence graph enables one to obtain a resource-considerate evaluation function, in which tasks which contribute directly to or precede bottlenecks have their priorities raised. The complexity of the calculations necessary to compute the lower bound was greatly reduced. A method is suggested for optimizing beyond basic blocks. Groups of basic blocks are treated as if they were one block; the information necessary to control the motion of tasks between blocks is encoded as data-precedence constraints on the conditional tasks. Long paths of code can thus be optimized, with no back branches, by the same methods used for basic blocks. 9 figures, 6 tables.

Chance-Constrained Guidance With Non-Convex Constraints

Science.gov (United States)

Ono, Masahiro

2011-01-01

Missions to small bodies, such as comets or asteroids, require autonomous guidance for descent to these small bodies. Such guidance is made challenging by uncertainty in the position and velocity of the spacecraft, as well as the uncertainty in the gravitational field around the small body. In addition, the requirement to avoid collision with the asteroid represents a non-convex constraint that means finding the optimal guidance trajectory, in general, is intractable. In this innovation, a new approach is proposed for chance-constrained optimal guidance with non-convex constraints. Chance-constrained guidance takes into account uncertainty so that the probability of collision is below a specified threshold. In this approach, a new bounding method has been developed to obtain a set of decomposed chance constraints that is a sufficient condition of the original chance constraint. The decomposition of the chance constraint enables its efficient evaluation, as well as the application of the branch and bound method. Branch and bound enables non-convex problems to be solved efficiently to global optimality. Considering the problem of finite-horizon robust optimal control of dynamic systems under Gaussian-distributed stochastic uncertainty, with state and control constraints, a discrete-time, continuous-state linear dynamics model is assumed. Gaussian-distributed stochastic uncertainty is a more natural model for exogenous disturbances such as wind gusts and turbulence than the previously studied set-bounded models. However, with stochastic uncertainty, it is often impossible to guarantee that state constraints are satisfied, because there is typically a non-zero probability of having a disturbance that is large enough to push the state out of the feasible region. An effective framework to address robustness with stochastic uncertainty is optimization with chance constraints. These require that the probability of violating the state constraints (i.e., the probability of

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Directory of Open Access Journals (Sweden)

Zhengyang Song