WorldWideScience

Sample records for pricing schedule network

  1. Incorporating price-responsive customers in day-ahead scheduling of smart distribution networks

    International Nuclear Information System (INIS)

    Mazidi, Mohammadreza; Monsef, Hassan; Siano, Pierluigi

    2016-01-01

    Highlights: • Proposing a model for incorporating price-responsive customers in day-ahead scheduling of smart distribution networks; this model provides a win–win situation. • Introducing a risk management model based on a bi-level information-gap decision theory and recasting it into its equivalent single-level robust optimization problem using Karush–Kuhn–Tucker optimality conditions. • Utilizing mixed-integer linear programing formulation that is efficiently solved by commercial optimization software. - Abstract: Demand response and real-time pricing of electricity are key factors in a smart grid as they can increase economic efficiency and technical performances of power grids. This paper focuses on incorporating price-responsive customers in day-ahead scheduling of smart distribution networks under a dynamic pricing environment. A novel method is proposed and formulated as a tractable mixed integer linear programming optimization problem whose objective is to find hourly sale prices offered to customers, transactions (purchase/sale) with the wholesale market, commitment of distribution generation units, dispatch of battery energy storage systems and planning of interruptible loads in a way that the profit of the distribution network operator is maximized while customers’ benefit is guaranteed. To hedge distribution network operator against financial risk arising from uncertainty of wholesale market prices, a risk management model based on a bi-level information-gap decision theory is proposed. The proposed bi-level problem is solved by recasting it into its equivalent single-level robust optimization problem using Karush–Kuhn–Tucker optimality conditions. Performance of the proposed model is verified by applying it to a modified version of the IEEE 33-bus distribution test network. Numerical results demonstrate the effectiveness and efficiency of the proposed method.

  2. Future aircraft networks and schedules

    Science.gov (United States)

    Shu, Yan

    2011-07-01

    Because of the importance of air transportation scheduling, the emergence of small aircraft and the vision of future fuel-efficient aircraft, this thesis has focused on the study of aircraft scheduling and network design involving multiple types of aircraft and flight services. It develops models and solution algorithms for the schedule design problem and analyzes the computational results. First, based on the current development of small aircraft and on-demand flight services, this thesis expands a business model for integrating on-demand flight services with the traditional scheduled flight services. This thesis proposes a three-step approach to the design of aircraft schedules and networks from scratch under the model. In the first step, both a frequency assignment model for scheduled flights that incorporates a passenger path choice model and a frequency assignment model for on-demand flights that incorporates a passenger mode choice model are created. In the second step, a rough fleet assignment model that determines a set of flight legs, each of which is assigned an aircraft type and a rough departure time is constructed. In the third step, a timetable model that determines an exact departure time for each flight leg is developed. Based on the models proposed in the three steps, this thesis creates schedule design instances that involve almost all the major airports and markets in the United States. The instances of the frequency assignment model created in this thesis are large-scale non-convex mixed-integer programming problems, and this dissertation develops an overall network structure and proposes iterative algorithms for solving these instances. The instances of both the rough fleet assignment model and the timetable model created in this thesis are large-scale mixed-integer programming problems, and this dissertation develops subproblem schemes for solving these instances. Based on these solution algorithms, this dissertation also presents

  3. Wireless network pricing

    CERN Document Server

    Huang, Jianwei

    2013-01-01

    Today's wireless communications and networking practices are tightly coupled with economic considerations, to the extent that it is almost impossible to make a sound technology choice without understanding the corresponding economic implications. This book aims at providing a foundational introduction on how microeconomics, and pricing theory in particular, can help us to understand and build better wireless networks. The book can be used as lecture notes for a course in the field of network economics, or a reference book for wireless engineers and applied economists to understand how pricing

  4. Price schedules coordination for electricity pool markets

    Science.gov (United States)

    Legbedji, Alexis Motto

    2002-04-01

    We consider the optimal coordination of a class of mathematical programs with equilibrium constraints, which is formally interpreted as a resource-allocation problem. Many decomposition techniques were proposed to circumvent the difficulty of solving large systems with limited computer resources. The considerable improvement in computer architecture has allowed the solution of large-scale problems with increasing speed. Consequently, interest in decomposition techniques has waned. Nonetheless, there is an important class of applications for which decomposition techniques will still be relevant, among others, distributed systems---the Internet, perhaps, being the most conspicuous example---and competitive economic systems. Conceptually, a competitive economic system is a collection of agents that have similar or different objectives while sharing the same system resources. In theory, constructing a large-scale mathematical program and solving it centrally, using currently available computing power can optimize such systems of agents. In practice, however, because agents are self-interested and not willing to reveal some sensitive corporate data, one cannot solve these kinds of coordination problems by simply maximizing the sum of agent's objective functions with respect to their constraints. An iterative price decomposition or Lagrangian dual method is considered best suited because it can operate with limited information. A price-directed strategy, however, can only work successfully when coordinating or equilibrium prices exist, which is not generally the case when a weak duality is unavoidable. Showing when such prices exist and how to compute them is the main subject of this thesis. Among our results, we show that, if the Lagrangian function of a primal program is additively separable, price schedules coordination may be attained. The prices are Lagrange multipliers, and are also the decision variables of a dual program. In addition, we propose a new form of

  5. Optimal scheduling using priced timed automata

    DEFF Research Database (Denmark)

    Behrmann, Gerd; Larsen, Kim Guldstrand; Rasmussen, Jacob Illum

    2005-01-01

    This contribution reports on the considerable effort made recently towards extending and applying well-established timed automata technology to optimal scheduling and planning problems. The effort of the authors in this direction has to a large extent been carried out as part of the European...... projects VHS [20] and AMETIST [16] and are available in the recently released UPPAAL CORA [12], a variant of the real-time verification tool UPPAAL [18, 5] specialized for cost-optimal reachability for the extended model of so-called priced timed automata....

  6. Scheduling Maintenance Jobs in Networks

    OpenAIRE

    Abed, Fidaa; Chen, Lin; Disser, Yann; Groß, Martin; Megow, Nicole; Meißner, Julie; Richter, Alexander T.; Rischke, Roman

    2017-01-01

    We investigate the problem of scheduling the maintenance of edges in a network, motivated by the goal of minimizing outages in transportation or telecommunication networks. We focus on maintaining connectivity between two nodes over time; for the special case of path networks, this is related to the problem of minimizing the busy time of machines. We show that the problem can be solved in polynomial time in arbitrary networks if preemption is allowed. If preemption is restricted to integral t...

  7. Trading network predicts stock price.

    Science.gov (United States)

    Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi

    2014-01-16

    Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

  8. Nonlinear Price Schedules and Tied Products.

    OpenAIRE

    Ormiston, Michael B; Phillips, Owen R

    1988-01-01

    Illegal tying often occurs when a monopolist jointly sells a product with a complementary requirement, also sold competitively. Along with selling the complement at its competi tive price, this paper shows that profit can increase when a monopoli st lets consumers bundle any amount of the requirement with the basic product at a fixed price. Examples illustrate demand conditions that enhance the profitability of this nonlinear price strategy and show that profits can approximate those earned f...

  9. Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care.

    Science.gov (United States)

    Farbmacher, Helmut; Ihle, Peter; Schubert, Ingrid; Winter, Joachim; Wuppermann, Amelie

    2017-10-01

    Nonlinear price schedules generally have heterogeneous effects on health-care demand. We develop and apply a finite mixture bivariate probit model to analyze whether there are heterogeneous reactions to the introduction of a nonlinear price schedule in the German statutory health insurance system. In administrative insurance claims data from the largest German health insurance plan, we find that some individuals strongly react to the new price schedule while a second group of individuals does not react. Post-estimation analyses reveal that the group of the individuals who do not react to the reform includes the relatively sick. These results are in line with forward-looking behavior: Individuals who are already sick expect that they will hit the kink in the price schedule and thus are less sensitive to the co-payment. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  10. An EV Charging Scheduling Mechanism Based on Price Negotiation

    Directory of Open Access Journals (Sweden)

    Baocheng Wang

    2018-05-01

    Full Text Available Scheduling EV user’s charging behavior based on charging price and applying renewable energy resources are the effective methods to release the load pressure of power grids brought about by the large-scale popularity of electric vehicles (EVs. This paper presents a novel approach for EV charging scheduling based on price negotiation. Firstly, the EV charging system framework based on price negotiation and renewable energy resources is discussed. Secondly, the price negotiation model is presented, including the initial price models and the conditions of transactions. Finally, an EV charging scheduling mechanism based on price negotiation (CSM-PN, including the price adjustment strategies of both the operator and EV users is proposed to seek a final transaction during multi-round price negotiation. Simulation results show that this novel approach can effectively improve the charging station operator’s income, reduce the EV users’ costs, and balance the load of the power grid while improving the efficiency of the EV charging system.

  11. Resource-Optimal Scheduling Using Priced Timed Automata

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Rasmussen, Jacob Illum; Subramani, K.

    2004-01-01

    In this paper, we show how the simple structure of the linear programs encountered during symbolic minimum-cost reachability analysis of priced timed automata can be exploited in order to substantially improve the performance of the current algorithm. The idea is rooted in duality of linear......-80 percent performance gain. As a main application area, we show how to solve energy-optimal task graph scheduling problems using the framework of priced timed automata....

  12. On using priced timed automata to achieve optimal scheduling

    DEFF Research Database (Denmark)

    Rasmussen, Jacob Illum; Larsen, Kim Guldstrand; Subramani, K.

    2006-01-01

    This contribution reports on the considerable effort made recently towards extending and applying well-established timed automata technology to optimal scheduling and planning problems. The effort of the authors in this direction has to a large extent been carried out as part of the European proj...... of so-called priced timed automata....

  13. Spatial price dynamics: From complex network perspective

    Science.gov (United States)

    Li, Y. L.; Bi, J. T.; Sun, H. J.

    2008-10-01

    The spatial price problem means that if the supply price plus the transportation cost is less than the demand price, there exists a trade. Thus, after an amount of exchange, the demand price will decrease. This process is continuous until an equilibrium state is obtained. However, how the trade network structure affects this process has received little attention. In this paper, we give a evolving model to describe the levels of spatial price on different complex network structures. The simulation results show that the network with shorter path length is sensitive to the variation of prices.

  14. Maritime wideband communication networks video transmission scheduling

    CERN Document Server

    Yang, Tingting

    2014-01-01

    This Springer Brief covers emerging maritime wideband communication networks and how they facilitate applications such as maritime distress, urgency, safety and general communications. It provides valuable insight on the data transmission scheduling and protocol design for the maritime wideband network. This brief begins with an introduction to maritime wideband communication networks including the architecture, framework, operations and a comprehensive survey on current developments. The second part of the brief presents the resource allocation and scheduling for video packet transmission wit

  15. Evolutionary Scheduler for the Deep Space Network

    Science.gov (United States)

    Guillaume, Alexandre; Lee, Seungwon; Wang, Yeou-Fang; Zheng, Hua; Chau, Savio; Tung, Yu-Wen; Terrile, Richard J.; Hovden, Robert

    2010-01-01

    A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

  16. Scheduling Network Traffic for Grid Purposes

    DEFF Research Database (Denmark)

    Gamst, Mette

    This thesis concerns scheduling of network traffic in grid context. Grid computing consists of a number of geographically distributed computers, which work together for solving large problems. The computers are connected through a network. When scheduling job execution in grid computing, data...... transmission has so far not been taken into account. This causes stability problems, because data transmission takes time and thus causes delays to the execution plan. This thesis proposes the integration of job scheduling and network routing. The scientific contribution is based on methods from operations...... research and consists of six papers. The first four considers data transmission in grid context. The last two solves the data transmission problem, where the number of paths per data connection is bounded from above. The thesis shows that it is possible to solve the integrated job scheduling and network...

  17. Integrated network design and scheduling problems :

    Energy Technology Data Exchange (ETDEWEB)

    Nurre, Sarah G.; Carlson, Jeffrey J.

    2014-01-01

    We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.

  18. Integrated Job Scheduling and Network Routing

    DEFF Research Database (Denmark)

    Gamst, Mette; Pisinger, David

    2013-01-01

    We consider an integrated job scheduling and network routing problem which appears in Grid Computing and production planning. The problem is to schedule a number of jobs at a finite set of machines, such that the overall profit of the executed jobs is maximized. Each job demands a number of resou...... indicate that the algorithm can be used as an actual scheduling algorithm in the Grid or as a tool for analyzing Grid performance when adding extra machines or jobs. © 2012 Wiley Periodicals, Inc.......We consider an integrated job scheduling and network routing problem which appears in Grid Computing and production planning. The problem is to schedule a number of jobs at a finite set of machines, such that the overall profit of the executed jobs is maximized. Each job demands a number...... of resources which must be sent to the executing machine through a network with limited capacity. A job cannot start before all of its resources have arrived at the machine. The scheduling problem is formulated as a Mixed Integer Program (MIP) and proved to be NP-hard. An exact solution approach using Dantzig...

  19. The impact of alternative pricing methods for drugs in California Workers’ Compensation System: Fee-schedule pricing

    Science.gov (United States)

    Wilson, Leslie; Turkistani, Fatema A.; Huang, Wei; Tran, Dang M.; Lin, Tracy Kuo

    2018-01-01

    Introduction California’s Workers’ Compensation System (CAWCS) Department of Industrial Relations questioned the adequacy of the current Medi-Cal fee-schedule pricing and requested analysis of alternatives that maximize price availability and maintain budget neutrality. Objectives To compare CAWCS pharmacy-dispensed (PD) drug prices under alternative fee schedules, and identify combinations of alternative benchmarks that have prices available for the largest percentage of PD drugs and that best reach budget neutrality. Methods Claims transaction-level data (2011–2013) from CAWCS were used to estimate total annual PD pharmaceutical payments. Medi-Cal pricing data was from the Workman’s Compensation Insurance System (WCIS). Average Wholesale Prices (AWP), Wholesale Acquisition Costs (WAC), Direct Prices (DP), Federal Upper Limit (FUL) prices, and National Average Drug Acquisition Costs (NADAC) were from Medi-Span. We matched National Drug Codes (NDCs), pricing dates, and drug quantity for comparisons. We report pharmacy-dispensed (PD) claims frequency, reimbursement matching rate, and paid costs by CAWCS as the reference price against all alternative price benchmarks. Results Of 12,529,977 CAWCS claims for pharmaceutical products 11.6% (1,462,814) were for PD drugs. Prescription drug cost for CAWCS was over $152M; $63.9M, $47.9M, and $40.6M in 2011–2013. Ninety seven percent of these CAWCS PD claims had a Medi-Cal price. Alternative mechanisms provided a price for fewer claims; NADAC 94.23%, AWP 90.94%, FUL 73.11%, WAC 66.98%, and DP 14.33%. Among CAWCS drugs with no Medi-Cal price in PD claims, AWP, WAC, NADAC, DP, and FUL provided prices for 96.7%, 63.14%, 24.82%, 20.83%, and 15.08% of claims. Overall CAWCS paid 100.52% of Medi-Cal, 60% of AWP, 97% of WAC, 309.53% of FUL, 103.83% of DP, and 136.27% of NADAC. Conclusions CAWCS current Medi-Cal fee-schedule price list for PD drugs is more complete than all alternative fee-schedules. However, all

  20. The impact of alternative pricing methods for drugs in California Workers' Compensation System: Fee-schedule pricing.

    Science.gov (United States)

    Wilson, Leslie; Turkistani, Fatema A; Huang, Wei; Tran, Dang M; Lin, Tracy Kuo

    2018-01-01

    California's Workers' Compensation System (CAWCS) Department of Industrial Relations questioned the adequacy of the current Medi-Cal fee-schedule pricing and requested analysis of alternatives that maximize price availability and maintain budget neutrality. To compare CAWCS pharmacy-dispensed (PD) drug prices under alternative fee schedules, and identify combinations of alternative benchmarks that have prices available for the largest percentage of PD drugs and that best reach budget neutrality. Claims transaction-level data (2011-2013) from CAWCS were used to estimate total annual PD pharmaceutical payments. Medi-Cal pricing data was from the Workman's Compensation Insurance System (WCIS). Average Wholesale Prices (AWP), Wholesale Acquisition Costs (WAC), Direct Prices (DP), Federal Upper Limit (FUL) prices, and National Average Drug Acquisition Costs (NADAC) were from Medi-Span. We matched National Drug Codes (NDCs), pricing dates, and drug quantity for comparisons. We report pharmacy-dispensed (PD) claims frequency, reimbursement matching rate, and paid costs by CAWCS as the reference price against all alternative price benchmarks. Of 12,529,977 CAWCS claims for pharmaceutical products 11.6% (1,462,814) were for PD drugs. Prescription drug cost for CAWCS was over $152M; $63.9M, $47.9M, and $40.6M in 2011-2013. Ninety seven percent of these CAWCS PD claims had a Medi-Cal price. Alternative mechanisms provided a price for fewer claims; NADAC 94.23%, AWP 90.94%, FUL 73.11%, WAC 66.98%, and DP 14.33%. Among CAWCS drugs with no Medi-Cal price in PD claims, AWP, WAC, NADAC, DP, and FUL provided prices for 96.7%, 63.14%, 24.82%, 20.83%, and 15.08% of claims. Overall CAWCS paid 100.52% of Medi-Cal, 60% of AWP, 97% of WAC, 309.53% of FUL, 103.83% of DP, and 136.27% of NADAC. CAWCS current Medi-Cal fee-schedule price list for PD drugs is more complete than all alternative fee-schedules. However, all reimbursement approaches would require combinations of pricing benchmarks

  1. Planning and Scheduling for Environmental Sensor Networks

    Science.gov (United States)

    Frank, J. D.

    2005-12-01

    Environmental Sensor Networks are a new way of monitoring the environment. They comprise autonomous sensor nodes in the environment that record real-time data, which is retrieved, analyzed, integrated with other data sets (e.g. satellite images, GIS, process models) and ultimately lead to scientific discoveries. Sensor networks must operate within time and resource constraints. Sensors have limited onboard memory, energy, computational power, communications windows and communications bandwidth. The value of data will depend on when, where and how it was collected, how detailed the data is, how long it takes to integrate the data, and how important the data was to the original scientific question. Planning and scheduling of sensor networks is necessary for effective, safe operations in the face of these constraints. For example, power bus limitations may preclude sensors from simultaneously collecting data and communicating without damaging the sensor; planners and schedulers can ensure these operations are ordered so that they do not happen simultaneously. Planning and scheduling can also ensure best use of the sensor network to maximize the value of collected science data. For example, if data is best recorded using a particular camera angle but it is costly in time and energy to achieve this, planners and schedulers can search for times when time and energy are available to achieve the optimal camera angle. Planning and scheduling can handle uncertainty in the problem specification; planners can be re-run when new information is made available, or can generate plans that include contingencies. For example, if bad weather may prevent the collection of data, a contingent plan can check lighting conditions and turn off data collection to save resources if lighting is not ideal. Both mobile and immobile sensors can benefit from planning and scheduling. For example, data collection on otherwise passive sensors can be halted to preserve limited power and memory

  2. Scheduling with artificial neural networks

    OpenAIRE

    Gürgün, Burçkaan

    1993-01-01

    Ankara : Department of Industrial Engineering and The Institute of Engineering and Sciences of Bilkent Univ., 1993. Thesis (Master's) -- Bilkent University, 1993. Includes bibliographical references leaves 59-65. Artificial Neural Networks (ANNs) attempt to emulate the massively parallel and distributed processing of the human brain. They are being examined for a variety of problems that have been very difficult to solve. The objective of this thesis is to review the curren...

  3. 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...

  4. Overcoming barriers to scheduling embedded generation to support distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Wright, A.J.; Formby, J.R.

    2000-07-01

    Current scheduling of embedded generation for distribution in the UK is limited and patchy. Some DNOs actively schedule while others do none. The literature on the subject is mainly about accommodating volatile wind output, and optimising island systems, for both cost of supply and network stability. The forthcoming NETA will lower prices, expose unpredictable generation to imbalance markets and could introduce punitive constraint payments on DNOs, but at the same time create a dynamic market for both power and ancillary services from embedded generators. Most renewable generators either run as base load (e.g. waste ) or according to the vagaries of the weather (e.g. wind, hydro), so offer little scope for scheduling other than 'off'. CHP plant is normally heat- led for industrial processes or building needs, but supplementary firing or thermal storage often allow considerable scope for scheduling. Micro-CHP with thermal storage could provide short-term scheduling, but tends to be running anyway during the evening peak. Standby generation appears to be ideal for scheduling, but in practice operators may be unwilling to run parallel with the network, and noise and pollution problems may preclude frequent operation. Statistical analysis can be applied to calculate the reliability of several generators compared to one; with a large number of generators such as micro-CHP reliability of a proportion of load is close to unity. The type of communication for generation used will depend on requirements for bandwidth, cost, reliability and whether it is bundled with other services. With high levels of deeply embedded, small-scale generation using induction machines, voltage control and black start capability will become important concerns on 11 kV and LV networks. This will require increased generation monitoring and remote control of switchgear. Examples of cost benefits from scheduling are given, including deferred reinforcement, increased exports on non

  5. Scheduling Broadcasts in a Network of Timelines

    KAUST Repository

    Manzoor, Emaad A.

    2015-05-12

    Broadcasts and timelines are the primary mechanism of information exchange in online social platforms today. Services like Facebook, Twitter and Instagram have enabled ordinary people to reach large audiences spanning cultures and countries, while their massive popularity has created increasingly competitive marketplaces of attention. Timing broadcasts to capture the attention of such geographically diverse audiences has sparked interest from many startups and social marketing gurus. However, formal study is lacking on both the timing and frequency problems. In this thesis, we introduce, motivate and solve the broadcast scheduling problem of specifying the timing and frequency of publishing content to maximise the attention received. We validate and quantify three interacting behavioural phenomena to parametrise social platform users: information overload, bursty circadian rhythms and monotony aversion, which is defined here for the first time. Our analysis of the influence of monotony refutes the common assumption that posts on social network timelines are consumed piecemeal independently. Instead, we reveal that posts are consumed in chunks, which has important consequences for any future work considering human behaviour over social network timelines. Our quantification of monotony aversion is also novel, and has applications to problems in various domains such as recommender list diversification, user satiation and variety-seeking consumer behaviour. Having studied the underlying behavioural phenomena, we link schedules, timelines, attention and behaviour by formalising a timeline information exchange process. Our formulation gives rise to a natural objective function that quantifies the expected collective attention an arrangement of posts on a timeline will receive. We apply this formulation as a case-study on real-data from Twitter, where we estimate behavioural parameters, calculate the attention potential for different scheduling strategies and, using the

  6. Medicare Program; Revisions to Payment Policies Under the Physician Fee Schedule and Other Revisions to Part B for CY 2017; Medicare Advantage Bid Pricing Data Release; Medicare Advantage and Part D Medical Loss Ratio Data Release; Medicare Advantage Provider Network Requirements; Expansion of Medicare Diabetes Prevention Program Model; Medicare Shared Savings Program Requirements. Final rule.

    Science.gov (United States)

    2016-11-15

    This major final rule addresses changes to the physician fee schedule and other Medicare Part B payment policies, such as changes to the Value Modifier, to ensure that our payment systems are updated to reflect changes in medical practice and the relative value of services, as well as changes in the statute. This final rule also includes changes related to the Medicare Shared Savings Program, requirements for Medicare Advantage Provider Networks, and provides for the release of certain pricing data from Medicare Advantage bids and of data from medical loss ratio reports submitted by Medicare health and drug plans. In addition, this final rule expands the Medicare Diabetes Prevention Program model.

  7. Cleaning Schedule Operations in Heat Exchanger Networks

    Directory of Open Access Journals (Sweden)

    Huda Hairul

    2018-01-01

    Full Text Available Heat exchanger networks have been known to be the essential parts in the chemical industries. Unfortunately, since the performance of heat exchanger can be decreasing in transferring the heat from hot stream into cold stream due to fouling, then cleaning the heat exchanger is needed to restore its initial performance periodically. A process of heating crude oil in a refinery plant was used as a case study. As many as eleven heat exchangers were used to heat crude oil before it was heated by a furnace to the temperature required to the crude unit distillation column. The purpose of this study is to determine the cleaning schedule of heat exchanger on the heat exchanger networks due to the decrease of the overall heat transfer coefficient by various percentage of the design value. A close study on the process of heat exchanger cleaning schedule in heat exchanger networks using the method of decreasing overall heat transfer coefficient as target. The result showed that the higher the fouling value the more often the heat exchanger is cleaned because the overall heat transfer coefficient decreases quickly.

  8. A subjective scheduler for subjective dedicated networks

    Science.gov (United States)

    Suherman; Fakhrizal, Said Reza; Al-Akaidi, Marwan

    2017-09-01

    Multiple access technique is one of important techniques within medium access layer in TCP/IP protocol stack. Each network technology implements the selected access method. Priority can be implemented in those methods to differentiate services. Some internet networks are dedicated for specific purpose. Education browsing or tutorial video accesses are preferred in a library hotspot, while entertainment and sport contents could be subjects of limitation. Current solution may use IP address filter or access list. This paper proposes subjective properties of users or applications are used for priority determination in multiple access techniques. The NS-2 simulator is employed to evaluate the method. A video surveillance network using WiMAX is chosen as the object. Subjective priority is implemented on WiMAX scheduler based on traffic properties. Three different traffic sources from monitoring video: palace, park, and market are evaluated. The proposed subjective scheduler prioritizes palace monitoring video that results better quality, xx dB than the later monitoring spots.

  9. NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING

    Directory of Open Access Journals (Sweden)

    Sergey A. Sannikov

    2017-03-01

    Full Text Available Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”. Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network and variables affecting on the model (permanent disturbance. Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut. Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.

  10. Scheduling and Topology Design in Networks with Directional Antennas

    Science.gov (United States)

    2017-05-19

    Scheduling and Topology Design in Networks with Directional Antennas Thomas Stahlbuhk, Nathaniel M. Jones, Brooke Shrader Lincoln Laboratory...controllers must choose which pairs of nodes should communicate in order to establish a topology over which traffic can be sent. Additionally...interacting effects of topology design and transmission scheduling in wireless networks, in particular focusing on networks where nodes are divided into

  11. Forecasting of electricity prices with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

    2006-08-15

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

  12. Forecasting of electricity prices with neural networks

    International Nuclear Information System (INIS)

    Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

    2006-01-01

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

  13. Combined natural gas and electricity network pricing

    Energy Technology Data Exchange (ETDEWEB)

    Morais, M.S.; Marangon Lima, J.W. [Universidade Federal de Itajuba, Rua Dr. Daniel de Carvalho, no. 296, Passa Quatro, Minas Gerais, CEP 37460-000 (Brazil)

    2007-04-15

    The introduction of competition to electricity generation and commercialization has been the main focus of many restructuring experiences around the world. The open access to the transmission network and a fair regulated tariff have been the keystones for the development of the electricity market. Parallel to the electricity industry, the natural gas business has great interaction with the electricity market in terms of fuel consumption and energy conversion. Given that the transmission and distribution monopolistic activities are very similar to the natural gas transportation through pipelines, economic regulation related to the natural gas network should be coherent with the transmission counterpart. This paper shows the application of the main wheeling charge methods, such as MW/gas-mile, invested related asset cost (IRAC) and Aumman-Shapley allocation, to both transmission and gas network. Stead-state equations are developed to adequate the various pricing methods. Some examples clarify the results, in terms of investments for thermal generation plants and end consumers, when combined pricing methods are used for transmission and gas networks. The paper also shows that the synergies between gas and electricity industry should be adequately considered, otherwise wrong economic signals are sent to the market players. (author)

  14. Fair packet scheduling in Wireless Mesh Networks

    KAUST Repository

    Nawab, Faisal

    2014-02-01

    In this paper we study the interactions of TCP and IEEE 802.11 MAC in Wireless Mesh Networks (WMNs). We use a Markov chain to capture the behavior of TCP sessions, particularly the impact on network throughput due to the effect of queue utilization and packet relaying. A closed form solution is derived to numerically determine the throughput. Based on the developed model, we propose a distributed MAC protocol called Timestamp-ordered MAC (TMAC), aiming to alleviate the unfairness problem in WMNs. TMAC extends CSMA/CA by scheduling data packets based on their age. Prior to transmitting a data packet, a transmitter broadcasts a request control message appended with a timestamp to a selected list of neighbors. It can proceed with the transmission only if it receives a sufficient number of grant control messages from these neighbors. A grant message indicates that the associated data packet has the lowest timestamp of all the packets pending transmission at the local transmit queue. We demonstrate that a loose ordering of timestamps among neighboring nodes is sufficient for enforcing local fairness, subsequently leading to flow rate fairness in a multi-hop WMN. We show that TMAC can be implemented using the control frames in IEEE 802.11, and thus can be easily integrated in existing 802.11-based WMNs. Our simulation results show that TMAC achieves excellent resource allocation fairness while maintaining over 90% of maximum link capacity across a large number of topologies.

  15. A branch-and-price algorithm for the long-term home care scheduling problem

    DEFF Research Database (Denmark)

    Gamst, Mette; Jensen, Thomas Sejr

    2012-01-01

    In several countries, home care is provided for certain citizens living at home. The long-term home care scheduling problem is to generate work plans such that a high quality of service is maintained, the work hours of the employees are respected, and the overall cost is kept as low as possible. We...... propose a branchand-price algorithm for the long-term home care scheduling problem. The pricing problem generates a one-day plan for an employee, and the master problem merges the plans with respect to regularity constraints. The method is capable of generating plans with up to 44 visits during one week....

  16. Price and Service Discrimination in Queuing Systems: Incentive Compatibility of Gc\\mu Scheduling

    OpenAIRE

    Jan A. Van Mieghem

    2000-01-01

    This article studies the optimal prices and service quality grades that a queuing system---the "firm"---provides to heterogeneous, utility-maximizing customers who measure quality by their experienced delay distributions. Results are threefold: First, delay cost curves are introduced that allow for a flexible description of a customer's quality sensitivity. Second, a comprehensive executable approach is proposed that analytically specifies scheduling, delay distributions and prices for arbitr...

  17. Network scheduling at Belene NPP construction site

    International Nuclear Information System (INIS)

    Matveev, A.

    2010-01-01

    Four types of schedules differing in the level of their detail are singled out to enhance the efficiency of Belene NPP Project implementation planning and monitoring: Level 1 Schedule–Summary Integrated Overall Time Schedule (SIOTS) is an appendix to EPC Contract. The main purpose of SIOTS is the large scale presentation of the current information on the Project implementation. Level 2 Schedule–Integrated Overall Time Schedule (IOTS)is the contract schedule for the Contractor (ASE JSC) and their subcontractors.The principal purpose of IOTS is the work progress planning and monitoring, the analysis of the effect of activities implementation upon the progress of the Project as a whole. IOTS is the reporting schedule at the Employer –Contractor level. Level 3 Schedules, Detail Time Schedules(DTS) are developed by those who actually perform the work and are agreed upon with Atomstroyexport JSC.The main purpose of DTS is the detail planning of Atomstroyexport subcontractor's activities. DTSare the reporting schedules at the level of Contractor-Subcontractor. Level 4 Schedules are the High Detail Time Schedules (HDTS), which are the day-to-day plans of work implementation and are developed, as a rule, for a week's time period.Each lower level time schedule details the activities of the higher level time schedule

  18. An improved scheduling algorithm for linear networks

    KAUST Repository

    Bader, Ahmed; Alouini, Mohamed-Slim; Ayadi, Yassin

    2017-01-01

    In accordance with the present disclosure, embodiments of an exemplary scheduling controller module or device implement an improved scheduling process such that the targeted reduction in schedule length can be achieve while incurring minimal energy penalty by allowing for a large rate (or duration) selection alphabet.

  19. An improved scheduling algorithm for linear networks

    KAUST Repository

    Bader, Ahmed

    2017-02-09

    In accordance with the present disclosure, embodiments of an exemplary scheduling controller module or device implement an improved scheduling process such that the targeted reduction in schedule length can be achieve while incurring minimal energy penalty by allowing for a large rate (or duration) selection alphabet.

  20. Revenue Improvement Through Demand-Dependent Pricing of Network Services

    National Research Council Canada - National Science Library

    Sanders, David

    2000-01-01

    ... of the expectation of rewards based upon variable demands. This work shows that revenue improvement can occur in this network environment when a dynamic pricing policy is applied as opposed to optimal static pricing...

  1. Durable-Goods Monopolists, Network Effects and Penetration Pricing

    OpenAIRE

    Cyrus C.Y. Chu; Hung-Ken Chien

    2005-01-01

    We study the pricing problem of a durable-goods monopolist. With network effects, consumption externalities among heterogeneous groups of consumers generate a discontinuous demand function. Consequently, the lessor has to offer a low price in order to reach the mass market, whereas the seller has the option to build a customer base by setting a lower initial price and raise the price later in the mass market, which explains the practice of introductory pricing. Contrary to the existing litera...

  2. Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies

    NARCIS (Netherlands)

    Paterakis, N.G.; Erdinç, O.; Bakirtzis, A.G.; Catalao, J.P.S.

    2015-01-01

    In this paper, a detailed home energy management system structure is developed to determine the optimal dayahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets

  3. Traffic Scheduling in WDM Passive Optical Network with Delay Guarantee

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    WDM passive optical network becomes more favorable as the required bandwidth increases, but currently few media access control algorithms adapted to WDM access network. This paper presented a new scheduling algorithm for bandwidth sharing in WDM passive optical networks, which provides per-flow delay guarantee and supports variable-length packets scheduling. Through theoretical analysis and simulation, the end-to-end delay bound and throughput fairness of the algorithm was demonstrated.

  4. Sleep scheduling with expected common coverage in wireless sensor networks

    OpenAIRE

    Bulut, Eyuphan; Korpeoglu, Ibrahim

    2011-01-01

    Sleep scheduling, which is putting some sensor nodes into sleep mode without harming network functionality, is a common method to reduce energy consumption in dense wireless sensor networks. This paper proposes a distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. The scheme can activate and deactivate the three basic units of a sensor node (sensing, proces...

  5. Joint opportunistic scheduling and network coding for bidirectional relay channel

    KAUST Repository

    Shaqfeh, Mohammad

    2013-07-01

    In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users\\' transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited. © 2013 IEEE.

  6. Price Transparency in Primary Care: Can Patients Learn About Costs When Scheduling an Appointment?

    Science.gov (United States)

    Saloner, Brendan; Cope, Lisa Clemans; Hempstead, Katherine; Rhodes, Karin V; Polsky, Daniel; Kenney, Genevieve M

    2017-07-01

    Cost-sharing in health insurance plans creates incentives for patients to shop for lower prices, but it is unknown what price information patients can obtain when scheduling office visits. To determine whether new patients can obtain price information for a primary care visit and identify variation across insurance types, offices, and geographic areas. Simulated patient methodology in which trained interviewers posed as non-elderly adults seeking new patient primary care appointments. Caller insurance type (employer-sponsored insurance [ESI], Marketplace, or uninsured) and plan were experimentally manipulated. Callers who were offered a visit asked for price information. Unadjusted means and regression-adjusted differences by insurance, office types, and geography were calculated. Calls to a representative sample of primary care offices in ten states in 2014: Arkansas, Georgia, Iowa, Illinois, Massachusetts, Montana, New Jersey, Oregon, Pennsylvania, and Texas (N = 7865). Callers recorded whether they were able to obtain a price. If not, they recorded whether they were referred to other sources for price information. Overall, 61.8% of callers with ESI were able to obtain a price, versus 89.2% of uninsured and 47.3% of Marketplace callers (P information was also more readily available in small offices and in counties with high uninsured rates. Among callers not receiving a price, 72.1% of callers with ESI were referred to other sources (billing office or insurance company), versus 25.8% of uninsured and 50.9% of Marketplace callers (P information is often unavailable for privately insured patients seeking primary care visits at the time a visit is scheduled.

  7. Spectrum and service pricing for 802.22 networks

    DEFF Research Database (Denmark)

    Stefan, Andrei Lucian; Rota, Cyril; Pratas, Nuno

    2011-01-01

    of channels which have an impact on the demand, and thus on the spectrum pricing. The second part of the paper exemplifies how a Bertrand game model can solve the issue of service pricing for a Wireless Regional Area Network (WRAN) operator that is trying to deploy an 802.22 network in a region where...... a competitor already exists. In this work the service pricing was envisioned as a network preplanning step, one that would show the potential revenues for an operator by entering as inputs, among other, the competitor's coverage area and spectrum pricing. The case study has been conducted through CNPT1...

  8. Optimal hydro scheduling and offering strategies considering price uncertainty and risk management

    International Nuclear Information System (INIS)

    Catalão, J.P.S.; Pousinho, H.M.I.; Contreras, J.

    2012-01-01

    Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head on power production, start-up costs related to the units, multiple regions of operation, and constraints on discharge variation. As new contributions to the field, market uncertainty is introduced in the model via price scenarios and risk management is included using Conditional Value-at-Risk to limit profit volatility. Moreover, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic cascaded hydro system. -- Highlights: ► A mixed-integer nonlinear programming approach is considered for optimal hydro scheduling. ► Market uncertainty is introduced in the model via price scenarios. ► Risk management is included using conditional value-at-risk. ► Plant scheduling and pool offering by the hydro power producer are simultaneously considered. ► A realistic cascaded hydro system is solved.

  9. Optimal scheduling for electric heat booster under day-ahead electricity and heat pricing

    DEFF Research Database (Denmark)

    Cai, Hanmin; You, Shi; Bindner, Henrik W.

    2017-01-01

    Multi-energy system (MES) operation calls for active management of flexible resources across energy sectors to improve efficiency and meet challenging environmental targets. Electric heat booster, a solution for Domestic Hot Water (DHW) preparation under Low-Temperature-District-Heating (LTDH......) context, is identified as one of aforementioned flexible resources for electricity and heat sectors. This paper extends the concept of optimal load scheduling under day-ahead pricing from electricity sector only to both electricity and heat sectors. A case study constructing day-ahead energy prices...

  10. Generation and Demand Scheduling for a Grid-Connected Hybrid Microgrid Considering Price-based Incentives

    DEFF Research Database (Denmark)

    Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi

    2017-01-01

    Microgrids rely on energy management levels to optimally schedule their components. Conventionally, the research in this field has been focused on the optimal formulation of the generation or the demand side management separately without considering real case scenarios and validated only...... by simulation. This paper presents the power scheduling of a real site microgrid under a price-based demand response program defined in Shanghai, China managing generation and demand simultaneously. The proposed optimization problem aims to minimize operating cost by managing renewable energy sources as well...

  11. Real-Time Pricing-Based Scheduling Strategy in Smart Grids: A Hierarchical Game Approach

    Directory of Open Access Journals (Sweden)

    Jie Yang

    2014-01-01

    Full Text Available This paper proposes a scheduling strategy based on real-time pricing in smart grids. A hierarchical game is employed to analyze the decision-making process of generators and consumers. We prove the existence and uniqueness of Nash equilibrium and utilize a backward induction method to obtain the generation and consumption strategies. Then, we propose two dynamic algorithms for the generators and consumers to search for the equilibrium in a distributed fashion. Simulation results demonstrate that the proposed scheduling strategy can match supply with demand and shift load away from peak time.

  12. Collaborative Scheduling between OSPPs and Gasholders in Steel Mill under Time-of-Use Power Price

    Directory of Open Access Journals (Sweden)

    Juxian Hao

    2017-08-01

    Full Text Available Byproduct gases generated during steel production process are the main fuels for on-site power plants (OSPPs in steel enterprises. Recently, with the implementation of time-of-use (TOU power price in China, increasing attention has been paid to the collaborative scheduling between OSPPs and gasholders. However, the load shifting potential of OSPPs has seldom been discussed in previous studies. In this paper, a mixed integer linear programming (MILP-based scheduling model is built to evaluate the load shifting potential and the corresponding economic benefits. A case study is conducted on two steel enterprises with different configurations of OSPPs, and the optimal operation strategy is also discussed.

  13. Algorithms for Scheduling and Network Problems

    Science.gov (United States)

    1991-09-01

    time. We already know, by Lemma 2.2.1, that WOPT = O(log( mpU )), so if we could solve this integer program optimally we would be done. However, the...Folydirat, 15:177-191, 1982. [6] I.S. Belov and Ya. N. Stolin. An algorithm in a single path operations scheduling problem. In Mathematical Economics and

  14. FLOWSHOP SCHEDULING USING A NETWORK APPROACH ...

    African Journals Online (AJOL)

    eobe

    time when the last job completes on the last machine. The objective ... more jobs in a permutation flow shop scheduling problem ... processing time of a job on a machine is zero, it ..... hybrid flow shops with sequence dependent setup times ...

  15. U.S. Army Engineering and Support Center, Huntsville, Price Reasonableness Determinations for Federal Supply Schedule Orders for Supplies Need Improvement

    Science.gov (United States)

    2016-03-29

    Army Engineering and Support Center, Huntsville, Price Reasonableness Determinations for Federal Supply Schedule Orders for Supplies Need...0207.000) │ i Results in Brief U.S. Army Engineering and Support Center, Huntsville, Price Reasonableness Determinations for Federal Supply Schedule...officers made determinations of fair and reasonable pricing for General Services Administration Federal supply schedule orders awarded for purchases

  16. Consumer Behavior towards Scheduling and Pricing of Electric Cars Recharging: Theoretical and Experimental Analysis

    DEFF Research Database (Denmark)

    Fetene, Gebeyehu Manie

    electric cars. The last chapter deals with analysis of energy consumption rate and its determinants of electric cars under the hands of customers. A variety of techniques are used including analysis of field data, economics laboratory experiments and theoretical modeling with simulation. Chapter one...... and Pricing of Electric Vehicle Recharging’, proposes, and tests at laboratory, contracts about recharging BEVs combining the ultimatum game framework and the myopic loss aversion (MLA) behavioral hypothesis. The model represents the behavior of EV-owners trading-off between the amount of the discount on fee...... price as long-term contracts may curtail MLA behavior and help BEV owners to choose cost minimizing recharging time and, simultaneously, may help to reduce BEVs impact on the electricity grid system. The fourth chapter, ‘Using the Peer Effect in Scheduling and Pricing Electric Vehicles Recharging...

  17. Integrated model for pricing, delivery time setting, and scheduling in make-to-order environments

    Science.gov (United States)

    Garmdare, Hamid Sattari; Lotfi, M. M.; Honarvar, Mahboobeh

    2018-03-01

    Usually, in make-to-order environments which work only in response to the customer's orders, manufacturers for maximizing the profits should offer the best price and delivery time for an order considering the existing capacity and the customer's sensitivity to both the factors. In this paper, an integrated approach for pricing, delivery time setting and scheduling of new arrival orders are proposed based on the existing capacity and accepted orders in system. In the problem, the acquired market demands dependent on the price and delivery time of both the manufacturer and its competitors. A mixed-integer non-linear programming model is presented for the problem. After converting to a pure non-linear model, it is validated through a case study. The efficiency of proposed model is confirmed by comparing it to both the literature and the current practice. Finally, sensitivity analysis for the key parameters is carried out.

  18. Robust Optimization-Based Generation Self-Scheduling under Uncertain Price

    Directory of Open Access Journals (Sweden)

    Xiao Luo

    2011-01-01

    Full Text Available This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of box and ellipsoidal uncertainty, respectively. IEEE 30-bus system is used to test the new model. Some comparisons with other methods are done, and the sensitivity with respect to the uncertain set is analyzed. Comparing with the existed uncertain self-scheduling approaches, the new method has twofold characteristics. First, it does not need a prediction of distribution of random variables and just requires an estimated value and the uncertain set of power price. Second, the counterpart of RO corresponding to the self-scheduling is a simple quadratic or quadratic cone programming. This indicates that the reformulated problem can be solved by many ordinary optimization algorithms.

  19. Markets in real electric networks require reactive prices

    International Nuclear Information System (INIS)

    Hogan, W.W.

    1996-01-01

    Extending earlier seminal work, the author finds that locational spot price differences in an electric network provide the natural measure of the appropriate internodal transport charge. However, the problem of loop flow requires different economic intuition for interpreting the implications of spot pricing. The Direct Current model, which is the usual approximation for estimating spot prices, ignores reactive power effects; this approximation is best when thermal constraints create network congestion. However, when voltage constraints are problematic, the DC Load model is insufficient; a full AC Model is required to determine both real and reactive spot prices. 16 figs., 3 tabs., 22 refs

  20. Dynamic Pricing in Electronic Commerce Using Neural Network

    Science.gov (United States)

    Ghose, Tapu Kumar; Tran, Thomas T.

    In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.

  1. Distributed Estimation, Coding, and Scheduling in Wireless Visual Sensor Networks

    Science.gov (United States)

    Yu, Chao

    2013-01-01

    In this thesis, we consider estimation, coding, and sensor scheduling for energy efficient operation of wireless visual sensor networks (VSN), which consist of battery-powered wireless sensors with sensing (imaging), computation, and communication capabilities. The competing requirements for applications of these wireless sensor networks (WSN)…

  2. Toward an Autonomous Telescope Network: the TBT Scheduler

    Science.gov (United States)

    Racero, E.; Ibarra, A.; Ocaña, F.; de Lis, S. B.; Ponz, J. D.; Castillo, M.; Sánchez-Portal, M.

    2015-09-01

    Within the ESA SSA program, it is foreseen to deploy several robotic telescopes to provide surveillance and tracking services for hazardous objects. The TBT project will procure a validation platform for an autonomous optical observing system in a realistic scenario, consisting of two telescopes located in Spain and Australia, to collect representative test data for precursor SSA services. In this context, the planning and scheduling of the night consists of two software modules, the TBT Scheduler, that will allow the manual and autonomous planning of the night, and the control of the real-time response of the system, done by the RTS2 internal scheduler. The TBT Scheduler allocates tasks for both telescopes without human intervention. Every night it takes all the inputs needed and prepares the schedule following some predefined rules. The main purpose of the scheduler is the distribution of the time for follow-up of recently discovered targets and surveys. The TBT Scheduler considers the overall performance of the system, and combine follow-up with a priori survey strategies for both kind of objects. The strategy is defined according to the expected combined performance for both systems the upcoming night (weather, sky brightness, object accessibility and priority). Therefore, TBT Scheduler defines the global approach for the network and relies on the RTS2 internal scheduler for the final detailed distribution of tasks at each sensor.

  3. Location-Price Competition in Airline Networks

    Directory of Open Access Journals (Sweden)

    H. Gao

    2014-01-01

    Full Text Available This paper addresses location-then-price competition in airline market as a two-stage game of n players on the graph. Passenger’s demand distribution is described by multinomial logit model. Equilibrium in price game is computed through best response dynamics. We solve location game using backward induction, knowing that airlines will choose prices from equilibrium for the second-stage game. Some numerical results for airline market under consideration are presented.

  4. Automating Deep Space Network scheduling and conflict resolution

    Science.gov (United States)

    Johnston, Mark D.; Clement, Bradley

    2005-01-01

    The Deep Space Network (DSN) is a central part of NASA's infrastructure for communicating with active space missions, from earth orbit to beyond the solar system. We describe our recent work in modeling the complexities of user requirements, and then scheduling and resolving conflicts on that basis. We emphasize our innovative use of background 'intelligent' assistants' that carry out search asynchrnously while the user is focusing on various aspects of the schedule.

  5. Request-Driven Schedule Automation for the Deep Space Network

    Science.gov (United States)

    Johnston, Mark D.; Tran, Daniel; Arroyo, Belinda; Call, Jared; Mercado, Marisol

    2010-01-01

    The DSN Scheduling Engine (DSE) has been developed to increase the level of automated scheduling support available to users of NASA s Deep Space Network (DSN). We have adopted a request-driven approach to DSN scheduling, in contrast to the activity-oriented approach used up to now. Scheduling requests allow users to declaratively specify patterns and conditions on their DSN service allocations, including timing, resource requirements, gaps, overlaps, time linkages among services, repetition, priorities, and a wide range of additional factors and preferences. The DSE incorporates a model of the key constraints and preferences of the DSN scheduling domain, along with algorithms to expand scheduling requests into valid resource allocations, to resolve schedule conflicts, and to repair unsatisfied requests. We use time-bounded systematic search with constraint relaxation to return nearby solutions if exact ones cannot be found, where the relaxation options and order are under user control. To explore the usability aspects of our approach we have developed a graphical user interface incorporating some crucial features to make it easier to work with complex scheduling requests. Among these are: progressive revelation of relevant detail, immediate propagation and visual feedback from a user s decisions, and a meeting calendar metaphor for repeated patterns of requests. Even as a prototype, the DSE has been deployed and adopted as the initial step in building the operational DSN schedule, thus representing an important initial validation of our overall approach. The DSE is a core element of the DSN Service Scheduling Software (S(sup 3)), a web-based collaborative scheduling system now under development for deployment to all DSN users.

  6. Transmission Scheduling and Routing Algorithms for Delay Tolerant Networks

    Science.gov (United States)

    Dudukovich, Rachel; Raible, Daniel E.

    2016-01-01

    The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

  7. Flowshop Scheduling Using a Network Approach | Oladeinde ...

    African Journals Online (AJOL)

    In this paper, a network based formulation of a permutation flow shop problem is presented. Two nuances of flow shop problems with different levels of complexity are solved using different approaches to the linear programming formulation. Key flow shop parameters inclosing makespan of the flow shop problems were ...

  8. Optimal scheduling for distribution network with redox flow battery storage

    International Nuclear Information System (INIS)

    Hosseina, Majid; Bathaee, Seyed Mohammad Taghi

    2016-01-01

    Highlights: • A novel method for optimal scheduling of storages in radial network is presented. • Peak shaving and load leveling are the main objectives. • Vanadium redox flow battery is considered as the energy storage unit. • Real data is used for simulation. - Abstract: There are many advantages to utilize storages in electric power system. Peak shaving, load leveling, load frequency control, integration of renewable, energy trading and spinning reserve are the most important of them. Batteries, especially redox flow batteries, are one of the appropriate storages for utilization in distribution network. This paper presents a novel, heuristic and practical method for optimal scheduling in distribution network with flow battery storage. This heuristic method is more suitable for scheduling and operation of distribution networks which require installation of storages. Peak shaving and load leveling is considered as the main objective in this paper. Several indices are presented in this paper for determine the place of storages and also scheduling for optimal use of energy in them. Simulations of this paper are based on real information of distribution network substation that located in Semnan, Iran.

  9. Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Kazuhiko Hiramoto

    2018-01-01

    Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

  10. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  11. Mixed Criticality Scheduling for Industrial Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xi Jin

    2016-08-01

    Full Text Available Wireless sensor networks (WSNs have been widely used in industrial systems. Their real-time performance and reliability are fundamental to industrial production. Many works have studied the two aspects, but only focus on single criticality WSNs. Mixed criticality requirements exist in many advanced applications in which different data flows have different levels of importance (or criticality. In this paper, first, we propose a scheduling algorithm, which guarantees the real-time performance and reliability requirements of data flows with different levels of criticality. The algorithm supports centralized optimization and adaptive adjustment. It is able to improve both the scheduling performance and flexibility. Then, we provide the schedulability test through rigorous theoretical analysis. We conduct extensive simulations, and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones.

  12. A Hierarchical Dispatch Structure for Distribution Network Pricing

    OpenAIRE

    Yuan, Zhao; Hesamzadeh, Mohammad Reza

    2015-01-01

    This paper presents a hierarchical dispatch structure for efficient distribution network pricing. The dispatch coordination problem in the context of hierarchical network operators are addressed. We formulate decentralized generation dispatch into a bilevel optimization problem in which main network operator and the connected distribution network operator optimize their costs in two levels. By using Karush-Kuhn-Tucker conditions and Fortuny-Amat McCarl linearization, the bilevel optimization ...

  13. Multimodal processes scheduling in mesh-like network environment

    Directory of Open Access Journals (Sweden)

    Bocewicz Grzegorz

    2015-06-01

    Full Text Available Multimodal processes planning and scheduling play a pivotal role in many different domains including city networks, multimodal transportation systems, computer and telecommunication networks and so on. Multimodal process can be seen as a process partially processed by locally executed cyclic processes. In that context the concept of a Mesh-like Multimodal Transportation Network (MMTN in which several isomorphic subnetworks interact each other via distinguished subsets of common shared intermodal transport interchange facilities (such as a railway station, bus station or bus/tram stop as to provide a variety of demand-responsive passenger transportation services is examined. Consider a mesh-like layout of a passengers transport network equipped with different lines including buses, trams, metro, trains etc. where passenger flows are treated as multimodal processes. The goal is to provide a declarative model enabling to state a constraint satisfaction problem aimed at multimodal transportation processes scheduling encompassing passenger flow itineraries. Then, the main objective is to provide conditions guaranteeing solvability of particular transport lines scheduling, i.e. guaranteeing the right match-up of local cyclic acting bus, tram, metro and train schedules to a given passengers flow itineraries.

  14. GCF: Green Conflict Free TDMA Scheduling for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Pawar, Pranav M.; Nielsen, Rasmus Hjorth; Prasad, Neeli R.

    2012-01-01

    The last few years have seen the promising growth in the application of wireless sensor networks (WSNs). The contribution of this paper is on a cluster-based time division multiple access (TDMA) scheduling algorithm to improve the performance of WSN applications in terms of energy efficiency, delay...

  15. Interval algebra: an effective means of scheduling surveillance radar networks

    CSIR Research Space (South Africa)

    Focke, RW

    2015-05-01

    Full Text Available Interval Algebra provides an effective means to schedule surveillance radar networks, as it is a temporal ordering constraint language. Thus it provides a solution to a part of resource management, which is included in the revised Data Fusion...

  16. Interval algebra - an effective means of scheduling surveillance radar networks

    CSIR Research Space (South Africa)

    Focke, RW

    2015-05-01

    Full Text Available Interval Algebra provides an effective means to schedule surveillance radar networks, as it is a temporal ordering constraint language. Thus it provides a solution to a part of resource management, which is included in the revised Data Fusion...

  17. Controller Area Network (CAN) schedulability analysis : refuted, revisited and revised

    NARCIS (Netherlands)

    Davis, R.I.; Burns, A.; Bril, R.J.; Lukkien, J.J.

    2007-01-01

    Controller Area Network (CAN) is used extensively in automotive applications, with in excess of 400 million CAN enabled microcontrollers manufactured each year. In 1994 schedulability analysis was developed for CAN, showing how worst-case response times of CAN messages could be calculated and hence

  18. A Data Scheduling and Management Infrastructure for the TEAM Network

    Science.gov (United States)

    Andelman, S.; Baru, C.; Chandra, S.; Fegraus, E.; Lin, K.; Unwin, R.

    2009-04-01

    currently partnering with the San Diego Super Computer Center to build the data management infrastructure. Data collected from the three core protocols as well as others are currently made available through the TEAM Network portal, which provides the content management framework, the data scheduling and management framework, an administrative framework to implement and manage TEAM sites, collaborative tools and a number of tools and applications utilizing Google Map and Google Earth products. A critical element of the TEAM Network data management infrastructure is to make the data publicly available in as close to real-time as possible (the TEAM Network Data Use Policy: http://www.teamnetwork.org/en/data/policy). This requires two essential tasks to be accomplished, 1) A data collection schedule has to be planned, proposed and approved for a given TEAM site. This is a challenging process since TEAM sites are geographically distributed across the tropics and hence have different seasons where they schedule field sampling for the different TEAM protocols. Capturing this information and ensuring that TEAM sites follow the outlined legal contract is key to the data collection process and 2) A stream-lined and efficient information management system to ensure data collected from the field meet the minimum data standards (i.e. are of the highest scientific quality) and are securely transferred, archived, processed and be rapidly made publicaly available, as a finished consumable product via the TEAM Network portal. The TEAM Network is achieving these goals by implementing an end-to-end framework consisting of the Sampling Scheduler application and the Data Management Framework. Sampling Scheduler The Sampling Scheduler is a project management, calendar based portal application that will allow scientists at a TEAM site to schedule field sampling for each of the TEAM protocols implemented at that site. The sampling scheduler addresses the specific requirements established in the

  19. Scheduling of a hydro producer considering head-dependency, price scenarios and risk-aversion

    International Nuclear Information System (INIS)

    Pousinho, H.M.I.; Mendes, V.M.F.; Catalão, J.P.S.

    2012-01-01

    Highlights: ► A MIQP approach is proposed for the short-term hydro scheduling problem. ► Head-dependency, discontinuous operating regions and discharge ramping constraints are considered. ► As new contribution to earlier studies, market uncertainty is introduced in the model via price scenarios. ► Also, risk aversion is incorporated by limiting the volatility of the expected profit through CVaR. ► A case study based on one of the main Portuguese cascaded hydro systems is provided. - Abstract: In this paper, a mixed-integer quadratic programming approach is proposed for the short-term hydro scheduling problem, considering head-dependency, discontinuous operating regions and discharge ramping constraints. As new contributions to earlier studies, market uncertainty is introduced in the model via price scenarios, and risk aversion is also incorporated by limiting the volatility of the expected profit through the conditional value-at-risk. Our approach has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems, requiring a negligible computational time.

  20. Scheduled Collision Avoidance in wireless sensor network using Zigbee

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2014-01-01

    Transmission reliability and energy consumptions are two critical concerns associated with wireless sensor network (WSN) design for a long time and continuous operation. With the increase in reliability of the transmission, the energy consumption increases by affecting the efficiency of the network....... This paper proposes the Schedule based Collision Avoidance (SCA) algorithm for finding the tradeoff between reliability and energy efficiency by fusion of CSMA/CA and TDMA techniques in Zigbee/ IEEE802.15.4. It uses the multi-path data propagation for collision avoidance and effective utilization...... of the channel providing efficient energy consumption. It analyses different scheduling schemes to provide an appropriate solution for reducing collisions and improving network lifetime....

  1. An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax Networks

    OpenAIRE

    R Murali Prasad; P. Satish Kumar

    2010-01-01

    Admission control schemes and scheduling algorithms are designed to offer QoS services in 802.16/802.16e networks and a number of studies have investigated these issues. But the channel condition and priority of traffic classes are very rarely considered in the existing scheduling algorithms. Although a number of energy saving mechanisms have been proposed for the IEEE 802.16e, to minimize the power consumption of IEEE 802.16e mobile stations with multiple real-time connections has not yet be...

  2. Estimating the price elasticity of expenditure for prescription drugs in the presence of non-linear price schedules: an illustration from Quebec, Canada.

    Science.gov (United States)

    Contoyannis, Paul; Hurley, Jeremiah; Grootendorst, Paul; Jeon, Sung-Hee; Tamblyn, Robyn

    2005-09-01

    The price elasticity of demand for prescription drugs is a crucial parameter of interest in designing pharmaceutical benefit plans. Estimating the elasticity using micro-data, however, is challenging because insurance coverage that includes deductibles, co-insurance provisions and maximum expenditure limits create a non-linear price schedule, making price endogenous (a function of drug consumption). In this paper we exploit an exogenous change in cost-sharing within the Quebec (Canada) public Pharmacare program to estimate the price elasticity of expenditure for drugs using IV methods. This approach corrects for the endogeneity of price and incorporates the concept of a 'rational' consumer who factors into consumption decisions the price they expect to face at the margin given their expected needs. The IV method is adapted from an approach developed in the public finance literature used to estimate income responses to changes in tax schedules. The instrument is based on the price an individual would face under the new cost-sharing policy if their consumption remained at the pre-policy level. Our preferred specification leads to expenditure elasticities that are in the low range of previous estimates (between -0.12 and -0.16). Naïve OLS estimates are between 1 and 4 times these magnitudes. (c) 2005 John Wiley & Sons, Ltd.

  3. Electricity price forecasting using Enhanced Probability Neural Network

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposes a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED), an Enhanced Probability Neural Network (EPNN) is proposed in the solving process. In this paper, the Locational Marginal Price (LMP), system load and temperature of PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday, and weekend. With the OED to smooth parameters in the EPNN, the forecasting error can be improved during the training process to promote the accuracy and reliability where even the ''spikes'' can be tracked closely. Simulation results show the effectiveness of the proposed EPNN to provide quality information in a price volatile environment. (author)

  4. 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

  5. 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.

  6. 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.

  7. Optimal residential smart appliances scheduling considering distribution network constraints

    Directory of Open Access Journals (Sweden)

    Yu-Ree Kim

    2016-01-01

    Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.

  8. Dynamic pricing of network goods with boundedly rational consumers.

    Science.gov (United States)

    Radner, Roy; Radunskaya, Ami; Sundararajan, Arun

    2014-01-07

    We present a model of dynamic monopoly pricing for a good that displays network effects. In contrast with the standard notion of a rational-expectations equilibrium, we model consumers as boundedly rational and unable either to pay immediate attention to each price change or to make accurate forecasts of the adoption of the network good. Our analysis shows that the seller's optimal price trajectory has the following structure: The price is low when the user base is below a target level, is high when the user base is above the target, and is set to keep the user base stationary once the target level has been attained. We show that this pricing policy is robust to a number of extensions, which include the product's user base evolving over time and consumers basing their choices on a mixture of a myopic and a "stubborn" expectation of adoption. Our results differ significantly from those that would be predicted by a model based on rational-expectations equilibrium and are more consistent with the pricing of network goods observed in practice.

  9. Coordinated scheduling for the downlink of cloud radio-access networks

    KAUST Repository

    Douik, Ahmed S.; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2015-01-01

    This paper addresses the coordinated scheduling problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy and the synchronization of the transmit

  10. Opportunistic Nonorthogonal Packet Scheduling in Fixed Broadband Wireless Access Networks

    Directory of Open Access Journals (Sweden)

    Ahmed Mohamed H

    2006-01-01

    Full Text Available In order to mitigate high cochannel interference resulting from dense channel reuse, the interference management issues are often considered as essential part of scheduling schemes in fixed broadband wireless access (FBWA networks. To that end, a series of literature has been published recently, in which a group of base stations forms an interferer group (downlink transmissions from each base station become dominant interference for the users in other in-group base stations, and the scheduling scheme deployed in the group allows only one base station to transmit at a time. As a result of time orthogonality in transmissions, the dominant cochannel interferers are prevented, and hence the packet error rate can be improved. However, prohibiting concurrent transmissions in these orthogonal schemes introduces throughput penalty as well as higher end-to-end packet delay which might not be desirable for real-time services. In this paper, we utilize opportunistic nonorthogonality among the in-group transmissions whenever possible and propose a novel transmission scheduling scheme for FBWA networks. The proposed scheme, in contrast to the proactive interference avoidance techniques, strives for the improvements in delay and throughput efficiency. To facilitate opportunistic nonorthogonal transmissions in the interferer group, estimation of signal-to-interference-plus-noise ratio (SINR is required at the scheduler. We have observed from simulations that the proposed scheme outperforms the reference orthogonal scheme in terms of spectral efficiency, mean packet delay, and packet dropping rate.

  11. Auction pricing of network access for North American railways

    DEFF Research Database (Denmark)

    Harrod, Steven

    2013-01-01

    The question of pricing train paths for "open access" railway networks in North America is discussed. An auction process is suggested as necessary to maintain transparency in the contracting process. Multiple random samples of auction pricing for a single track railway line demonstrate...... that the infrastructure entity will receive approximately 15.6% less than the true value of the contracted train paths. This loss of revenue threatens the objective of reducing government subsidy for the railway network. (C) 2012 Elsevier Ltd. All rights reserved....

  12. A Novel Message Scheduling Framework for Delay Tolerant Networks Routing

    KAUST Repository

    Elwhishi, Ahmed

    2013-05-01

    Multicopy routing strategies have been considered the most applicable approaches to achieve message delivery in Delay Tolerant Networks (DTNs). Epidemic routing and two-hop forwarding routing are two well-reported approaches for delay tolerant networks routing which allow multiple message replicas to be launched in order to increase message delivery ratio and/or reduce message delivery delay. This advantage, nonetheless, is at the expense of additional buffer space and bandwidth overhead. Thus, to achieve efficient utilization of network resources, it is important to come up with an effective message scheduling strategy to determine which messages should be forwarded and which should be dropped in case of buffer is full. This paper investigates a new message scheduling framework for epidemic and two-hop forwarding routing in DTNs, such that the forwarding/dropping decision can be made at a node during each contact for either optimal message delivery ratio or message delivery delay. Extensive simulation results show that the proposed message scheduling framework can achieve better performance than its counterparts.

  13. ECS: Efficient Communication Scheduling for Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Lu Hong

    2011-03-01

    Full Text Available TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs, because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols.

  14. Dynamic Intelligent Feedback Scheduling in Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Hui-ying Chen

    2013-01-01

    Full Text Available For the networked control system with limited bandwidth and flexible workload, a dynamic intelligent feedback scheduling strategy is proposed. Firstly, a monitor is used to acquire the current available network bandwidth. Then, the new available bandwidth in the next interval is predicted by using LS_SVM approach. At the same time, the dynamic performance indices of all control loops are obtained with a two-dimensional fuzzy logic modulator. Finally, the predicted network bandwidth is dynamically allocated by the bandwidth manager and the priority allocator in terms of the loops' dynamic performance indices. Simulation results show that the sampling periods and priorities of control loops are adjusted timely according to the network workload condition and the dynamic performance of control loops, which make the system running in the optimal state all the time.

  15. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  16. A Bilevel Scheduling Approach for Modeling Energy Transaction of Virtual Power Plants in Distribution Networks

    Directory of Open Access Journals (Sweden)

    F. Nazari

    2017-03-01

    Full Text Available By increasing the use of distributed generation (DG in the distribution network operation, an entity called virtual power plant (VPP has been introduced to control, dispatch and aggregate the generation of DGs, enabling them to participate either in the electricity market or the distribution network operation. The participation of VPPs in the electricity market has made challenges to fairly allocate payments and benefits between VPPs and distribution network operator (DNO. This paper presents a bilevel scheduling approach to model the energy transaction between VPPs and DNO.  The upper level corresponds to the decision making of VPPs which bid their long- term contract prices so that their own profits are maximized and the lower level represents the DNO decision making to supply electricity demand of the network by minimizing its overall cost. The proposed bilevel scheduling approach is transformed to a single level optimizing problem using its Karush-Kuhn-Tucker (KKT optimality conditions. Several scenarios are applied to scrutinize the effectiveness and usefulness of the proposed model. 

  17. Pricing and Capacity Planning Problems in Energy Transmission Networks

    DEFF Research Database (Denmark)

    Villumsen, Jonas Christoffer

    strategy. In the Nordic electricity system a market with zonal prices is adopted. We consider the problem of designing zones in an optimal way explicitly considering uncertainty. Finally, we formulate the integrated problem of pipeline capacity expansion planning and transmission pricing in natural gas...... necessitates a radical change in the way we plan and operate energy systems. Another paradigm change which began in the 1990’s for electricity systems is that of deregulation. This has led to a variety of different market structures implemented across the world. In this thesis we discuss capacity planning...... and transmission pricing problems in energy transmission networks. Although the modelling framework applies to energy networks in general, most of the applications discussed concern the transmission of electricity. A number of the problems presented involves transmission switching, which allows the operator...

  18. A neural network approach to job-shop scheduling.

    Science.gov (United States)

    Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E

    1991-01-01

    A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.

  19. Second-best Pricing for Imperfect Substitutes in Urban Networks

    NARCIS (Netherlands)

    Rouwendal, J.; Verhoef, Erik

    2003-01-01

    This paper considers second-best pricing as it arises through incomplete coverage of full networks. The main principles are first reviewed by considering the classic two-route problem and some extensions that have been studied more recently. In most of these studies the competing routes are assumed

  20. Second Best Pricing for Imperfect Substitutes in Urban Networks

    NARCIS (Netherlands)

    Rouwendal, J.; Verhoef, Erik

    2004-01-01

    This paper considers second-best pricing as it arises through incomplete coverage of full networks. The main principles are first reviewed by considering the classic two-route problem and some extensions that have been studied more recently. In most of these studies the competing routes are assumed

  1. Network Asymmetries and Access Pricing in Cellular Telecommunications

    NARCIS (Netherlands)

    V. Kocsis

    2005-01-01

    textabstractNetwork shares and retail prices are not symmetric in the telecommunications market with multiple bottlenecks which give rise to new questions of access fee regulation. In this paper we consider a model with two types of asymmetry arising from different entry timing, i.e. a larger

  2. A probabilistic computational framework for bridge network optimal maintenance scheduling

    International Nuclear Information System (INIS)

    Bocchini, Paolo; Frangopol, Dan M.

    2011-01-01

    This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.

  3. Modeling of price and profit in coupled-ring networks

    Science.gov (United States)

    Tangmongkollert, Kittiwat; Suwanna, Sujin

    2016-06-01

    We study the behaviors of magnetization, price, and profit profiles in ring networks in the presence of the external magnetic field. The Ising model is used to determine the state of each node, which is mapped to the buy-or-sell state in a financial market, where +1 is identified as the buying state, and -1 as the selling state. Price and profit mechanisms are modeled based on the assumption that price should increase if demand is larger than supply, and it should decrease otherwise. We find that the magnetization can be induced between two rings via coupling links, where the induced magnetization strength depends on the number of the coupling links. Consequently, the price behaves linearly with time, where its rate of change depends on the magnetization. The profit grows like a quadratic polynomial with coefficients dependent on the magnetization. If two rings have opposite direction of net spins, the price flows in the direction of the majority spins, and the network with the minority spins gets a loss in profit.

  4. Neural Networks as Semiparametric Option Pricing Tool

    Czech Academy of Sciences Publication Activity Database

    Baruník, Jozef; Baruníková, M.

    2011-01-01

    Roč. 18, č. 28 (2011), s. 66-83 ISSN 1212-074X R&D Projects: GA ČR GD402/09/H045; GA ČR GA402/09/0965 Grant - others:GA ČR(CZ) GA402/09/0732 Institutional research plan: CEZ:AV0Z10750506 Keywords : option valuation * neural network * S&P 500 index options Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/barunik-0367688.pdf

  5. Entity’s Irregular Demand Scheduling of the Wholesale Electricity Market based on the Forecast of Hourly Price Ratios

    Directory of Open Access Journals (Sweden)

    O. V. Russkov

    2015-01-01

    Full Text Available The article considers a hot issue to forecast electric power demand amounts and prices for the entities of wholesale electricity market (WEM, which are in capacity of a large user with production technology requirements prevailing over hourly energy planning ones. An electric power demand of such entities is on irregular schedule. The article analyses mathematical models, currently applied to forecast demand amounts and prices. It describes limits of time-series models and fundamental ones in case of hourly forecasting an irregular demand schedule of the electricity market entity. The features of electricity trading at WEM are carefully analysed. Factors that influence on irregularity of demand schedule of the metallurgical plant are shown. The article proposes method for the qualitative forecast of market price ratios as a tool to reduce a dependence on the accuracy of forecasting an irregular schedule of demand. It describes the differences between the offered method and the similar ones considered in research studies and scholarly works. The correlation between price ratios and relaxation in the requirements for the forecast accuracy of the electric power consumption is analysed. The efficiency function of forecast method is derived. The article puts an increased focus on description of the mathematical model based on the method of qualitative forecast. It shows main model parameters and restrictions the electricity market imposes on them. The model prototype is described as a programme module. Methods to assess an effectiveness of the proposed forecast model are examined. The positive test results of the model using JSC «Volzhsky Pipe Plant» data are given. A conclusion is drawn concerning the possibility to decrease dependence on the forecast accuracy of irregular schedule of entity’s demand at WEM. The effective trading tool has been found for the entities of irregular demand schedule at WEM. The tool application allows minimizing cost

  6. Pricing Strategies for Viral Marketing on Social Networks

    KAUST Repository

    Arthur, David

    2009-01-01

    We study the use of viral marketing strategies on social networks that seek to maximize revenue from the sale of a single product. We propose a model in which the decision of a buyer to buy the product is influenced by friends that own the product and the price at which the product is offered. The influence model we analyze is quite general, naturally extending both the Linear Threshold model and the Independent Cascade model, while also incorporating price information. We consider sales proceeding in a cascading manner through the network, i.e. a buyer is offered the product via recommendations from its neighbors who own the product. In this setting, the seller influences events by offering a cashback to recommenders and by setting prices (via coupons or discounts) for each buyer in the social network. This choice of prices for the buyers is termed as the seller\\'s strategy. Finding a seller strategy which maximizes the expected revenue in this setting turns out to be NP-hard. However, we propose a seller strategy that generates revenue guaranteed to be within a constant factor of the optimal strategy in a wide variety of models. The strategy is based on an influence-and-exploit idea, and it consists of finding the right trade-off at each time step between: generating revenue from the current user versus offering the product for free and using the influence generated from this sale later in the process. © 2009 Springer-Verlag Berlin Heidelberg.

  7. Scheduling Data Access in Smart Grid Networks Utilizing Context Information

    DEFF Research Database (Denmark)

    Findrik, Mislav; Grønbæk, Jesper; Olsen, Rasmus Løvenstein

    2014-01-01

    Current electrical grid is facing increased penetration of intermittent energy resources, in particular wind and solar energy. Fast variability of the power supply due to renewable energy resources can be balanced out using different energy storage systems or shifting the loads. Efficiently...... managing this fast flexibility requires two-way data exchange between a controller and sensors/meters via communication networks. In this paper we investigated scheduling of data collection utilizing meta-data from sensors that are describing dynamics of information. We show the applicability...

  8. Computation and evaluation of scheduled waiting time for railway networks

    DEFF Research Database (Denmark)

    Landex, Alex

    2010-01-01

    Timetables are affected by scheduled waiting time (SWT) that prolongs the travel times for trains and thereby passengers. SWT occurs when a train hinders another train to run with the wanted speed. The SWT affects both the trains and the passengers in the trains. The passengers may be further...... affected due to longer transfer times to other trains. SWT can be estimated analytically for a given timetable or by simulation of timetables and/or plans of operation. The simulation of SWT has the benefit that it is possible to examine the entire network. This makes it possible to improve the future...

  9. Multiresolution Network Temporal and Spatial Scheduling Model of Scenic Spot

    Directory of Open Access Journals (Sweden)

    Peng Ge

    2013-01-01

    Full Text Available Tourism is one of pillar industries of the world economy. Low-carbon tourism will be the mainstream direction of the scenic spots' development, and the ω path of low-carbon tourism development is to develop economy and protect environment simultaneously. However, as the tourists' quantity is increasing, the loads of scenic spots are out of control. And the instantaneous overload in some spots caused the image phenomenon of full capacity of the whole scenic spot. Therefore, realizing the real-time schedule becomes the primary purpose of scenic spot’s management. This paper divides the tourism distribution system into several logically related subsystems and constructs a temporal and spatial multiresolution network scheduling model according to the regularity of scenic spots’ overload phenomenon in time and space. It also defines dynamic distribution probability and equivalent dynamic demand to realize the real-time prediction. We define gravitational function between fields and takes it as the utility of schedule, after resolving the transportation model of each resolution, it achieves hierarchical balance between demand and capacity of the system. The last part of the paper analyzes the time complexity of constructing a multiresolution distribution system.

  10. Collaborative Distributed Scheduling Approaches for Wireless Sensor Network

    Science.gov (United States)

    Niu, Jianjun; Deng, Zhidong

    2009-01-01

    Energy constraints restrict the lifetime of wireless sensor networks (WSNs) with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs) based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes' energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs. PMID:22408491

  11. New preemptive scheduling for OBS networks considering cascaded wavelength conversion

    Science.gov (United States)

    Gao, Xingbo; Bassiouni, Mostafa A.; Li, Guifang

    2009-05-01

    In this paper we introduce a new preemptive scheduling technique for next generation optical burst-switched networks considering the impact of cascaded wavelength conversions. It has been shown that when optical bursts are transmitted all optically from source to destination, each wavelength conversion performed along the lightpath may cause certain signal-to-noise deterioration. If the distortion of the signal quality becomes significant enough, the receiver would not be able to recover the original data. Accordingly, subject to this practical impediment, we improve a recently proposed fair channel scheduling algorithm to deal with the fairness problem and aim at burst loss reduction simultaneously in optical burst switching. In our scheme, the dynamic priority associated with each burst is based on a constraint threshold and the number of already conducted wavelength conversions among other factors for this burst. When contention occurs, a new arriving superior burst may preempt another scheduled one according to their priorities. Extensive simulation results have shown that the proposed scheme further improves fairness and achieves burst loss reduction as well.

  12. Collaborative Distributed Scheduling Approaches for Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Zhidong Deng

    2009-10-01

    Full Text Available Energy constraints restrict the lifetime of wireless sensor networks (WSNs with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes’ energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs.

  13. Application for Single Price Auction Model (SPA) in AC Network

    Science.gov (United States)

    Wachi, Tsunehisa; Fukutome, Suguru; Chen, Luonan; Makino, Yoshinori; Koshimizu, Gentarou

    This paper aims to develop a single price auction model with AC transmission network, based on the principle of maximizing social surplus of electricity market. Specifically, we first formulate the auction market as a nonlinear optimization problem, which has almost the same form as the conventional optimal power flow problem, and then propose an algorithm to derive both market clearing price and trade volume of each player even for the case of market-splitting. As indicated in the paper, the proposed approach can be used not only for the price evaluation of auction or bidding market but also for analysis of bidding strategy, congestion effect and other constraints or factors. Several numerical examples are used to demonstrate effectiveness of our method.

  14. Game-theoretic pricing for video streaming in mobile networks.

    Science.gov (United States)

    Lin, W Sabrina; Liu, K J Ray

    2012-05-01

    Mobile phones are among the most popular consumer devices, and the recent developments of 3G networks and smart phones enable users to watch video programs by subscribing data plans from service providers. Due to the ubiquity of mobile phones and phone-to-phone communication technologies, data-plan subscribers can redistribute the video content to nonsubscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized redistribution behavior to protect or maximize his/her own profit. In this paper, we analyze the optimal price setting for the service provider by investigating the equilibrium between the subscribers and the secondary buyers in the content-redistribution network. We model the behavior between the subscribers and the secondary buyers as a noncooperative game and find the optimal price and quantity for both groups of users. Based on the behavior of users in the redistribution network, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.

  15. FOREST HARVEST SCHEDULING PLAN INTEGRATED TO THE ROAD NETWORK

    Directory of Open Access Journals (Sweden)

    Pedro Henrique Belavenutti Martins da Silva

    2016-03-01

    Full Text Available In industrial forest plantations, the spatial distribution of management units for harvest scheduling influences the timber production cost and the non-renewable resources consumption, due to issues related to transport logistic. In this context, this research aimed to formulate Integer Linear Programming (ILP by means of the application of Floyd-Warshall network optimization algorithm to generate timber production routes, minimizing the production costs resulting from harvest activities and forest road maintenance. Then, scenarios were simulated considering different minimal harvest ages for Pinus spp. and Eucalyptus spp. stands. The planning horizon was five years with annual periodicity. The study area was 23,330 hectares of forests, located in Paraná state (southern Brazil. We compared the simulated scenarios according to the following parameter indicators: harvest income, building road network and the production unit cost. The decreasing of the minimal harvest age reduces the mean production of management units scheduled to be harvested, in other hand, it requires fewer roads to be built, and consequently increases the production unit cost. The solutions obtained by using ILP models presented an optimality gap lower than 0.1%.

  16. Optimal Pricing Strategy for Wireless Social Community Networks

    OpenAIRE

    Mazloumian, Amin; Manshaei, Mohammad Hossein; Felegyhazi, Mark; Hubaux, Jean-Pierre

    2008-01-01

    The increasing number of mobile applications fuels the demand for affordable and ubiquitous wireless access. The traditional wireless network technologies such as EV-DO or WiMAX provide this service but require a huge upfront investment in infrastructure and spectrum. On the contrary, as they do not have to face such an investment, social community operators rely on subscribers who constitute a community of users. The pricing strategy of the provided wireless access is an open problem for thi...

  17. Stochastic project networks temporal analysis, scheduling and cost minimization

    CERN Document Server

    Neumann, Klaus

    1990-01-01

    Project planning, scheduling, and control are regularly used in business and the service sector of an economy to accomplish outcomes with limited resources under critical time constraints. To aid in solving these problems, network-based planning methods have been developed that now exist in a wide variety of forms, cf. Elmaghraby (1977) and Moder et al. (1983). The so-called "classical" project networks, which are used in the network techniques CPM and PERT and which represent acyclic weighted directed graphs, are able to describe only projects whose evolution in time is uniquely specified in advance. Here every event of the project is realized exactly once during a single project execution and it is not possible to return to activities previously carried out (that is, no feedback is permitted). Many practical projects, however, do not meet those conditions. Consider, for example, a production process where some parts produced by a machine may be poorly manufactured. If an inspection shows that a part does no...

  18. 48 CFR 552.216-70 - Economic Price Adjustment-FSS Multiple Award Schedule Contracts.

    Science.gov (United States)

    2010-10-01

    ... ___* percent of the original contract unit price. The Government reserves the right to raise this ceiling where... price increase. (e) The Government reserves the right to exercise one of the following options: (1... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Economic Price Adjustment...

  19. Dynamic Vehicle Scheduling for Working Service Network with Dual Demands

    Directory of Open Access Journals (Sweden)

    Bing Li

    2017-01-01

    Full Text Available This study aims to develop some models to aid in making decisions on the combined fleet size and vehicle assignment in working service network where the demands include two types (minimum demands and maximum demands, and vehicles themselves can act like a facility to provide services when they are stationary at one location. This type of problem is named as the dynamic working vehicle scheduling with dual demands (DWVS-DD and formulated as a mixed integer programming (MIP. Instead of a large integer program, the problem is decomposed into small local problems that are guided by preset control parameters. The approach for preset control parameters is given. By introducing them into the MIP formulation, the model is reformulated as a piecewise form. Further, a piecewise method by updating preset control parameters is proposed for solving the reformulated model. Numerical experiments show that the proposed method produces better solution within reasonable computing time.

  20. Stock price change rate prediction by utilizing social network activities.

    Science.gov (United States)

    Deng, Shangkun; Mitsubuchi, Takashi; Sakurai, Akito

    2014-01-01

    Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

  1. Stock Price Change Rate Prediction by Utilizing Social Network Activities

    Directory of Open Access Journals (Sweden)

    Shangkun Deng

    2014-01-01

    Full Text Available Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL and genetic algorithm (GA. MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

  2. Forecasting electricity market pricing using artificial neural networks

    International Nuclear Information System (INIS)

    Pao, Hsiao-Tien

    2007-01-01

    Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long

  3. Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

    OpenAIRE

    Chih-Yu Wen; Ying-Chih Chen

    2009-01-01

    This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show t...

  4. Adaptive Priority-Based Downlink Scheduling for WiMAX Networks

    OpenAIRE

    Wu, Shih-Jung; Huang, Shih-Yi; Huang, Kuo-Feng

    2012-01-01

    Supporting quality of service (QoS) guarantees for diverse multimedia services are the primary concerns for WiMAX (IEEE 802.16) networks. A scheduling scheme that satisfies QoS requirements has become more important for wireless communications. We propose a downlink scheduling scheme called adaptive priority-based downlink scheduling (APDS) for providing QoS guarantees in IEEE 802.16 networks. APDS comprises two major components: priority assignment and resource allocation. Different service-...

  5. Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles

    Directory of Open Access Journals (Sweden)

    Nadeem Javaid

    2018-06-01

    Full Text Available In this paper, we propose a demand side management (DSM scheme in the residential area for electricity cost and peak to average ratio (PAR alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE and teacher learning-based optimization (TLBO. Furthermore, we propose a hybrid technique (HT having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP and critical peak pricing (CPP are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE in terms of electricity cost and PAR reduction while minimizing the average waiting time.

  6. Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management

    International Nuclear Information System (INIS)

    Aghajani, G.R.; Shayanfar, H.A.; Shayeghi, H.

    2015-01-01

    Highlights: • Using DRPs to cover the uncertainties resulted from power generation by WT and PV. • Proposing the use of price-offer packages and amount of DR for implement DRPs. • Considering a multi-objective scheduling model and use of MOPSO algorithm. - Abstract: In this paper, a multi-objective energy management system is proposed in order to optimize micro-grid (MG) performance in a short-term in the presence of Renewable Energy Sources (RESs) for wind and solar energy generation with a randomized natural behavior. Considering the existence of different types of customers including residential, commercial, and industrial consumers can participate in demand response programs. As with declare their interruptible/curtailable demand rate or select from among different proposed prices so as to assist the central micro-grid control in terms of optimizing micro-grid operation and covering energy generation uncertainty from the renewable sources. In this paper, to implement Demand Response (DR) schedules, incentive-based payment in the form of offered packages of price and DR quantity collected by Demand Response Providers (DRPs) is used. In the typical micro-grid, different technologies including Wind Turbine (WT), PhotoVoltaic (PV) cell, Micro-Turbine (MT), Full Cell (FC), battery hybrid power source and responsive loads are used. The simulation results are considered in six different cases in order to optimize operation cost and emission with/without DR. Considering the complexity and non-linearity of the proposed problem, Multi-Objective Particle Swarm Optimization (MOPSO) is utilized. Also, fuzzy-based mechanism and non-linear sorting system are applied to determine the best compromise considering the set of solutions from Pareto-front space. The numerical results represented the effect of the proposed Demand Side Management (DSM) scheduling model on reducing the effect of uncertainty obtained from generation power and predicted by WT and PV in a MG.

  7. Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chih-Yu Wen

    2009-05-01

    Full Text Available This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

  8. Dynamic hierarchical sleep scheduling for wireless ad-hoc sensor networks.

    Science.gov (United States)

    Wen, Chih-Yu; Chen, Ying-Chih

    2009-01-01

    This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

  9. A Scheduling Algorithm for Minimizing the Packet Error Probability in Clusterized TDMA Networks

    Directory of Open Access Journals (Sweden)

    Arash T. Toyserkani

    2009-01-01

    Full Text Available We consider clustered wireless networks, where transceivers in a cluster use a time-slotted mechanism (TDMA to access a wireless channel that is shared among several clusters. An approximate expression for the packet-loss probability is derived for networks with one or more mutually interfering clusters in Rayleigh fading environments, and the approximation is shown to be good for relevant scenarios. We then present a scheduling algorithm, based on Lagrangian duality, that exploits the derived packet-loss model in an attempt to minimize the average packet-loss probability in the network. Computer simulations of the proposed scheduling algorithm show that a significant increase in network throughput can be achieved compared to uncoordinated scheduling. Empirical trials also indicate that the proposed optimization algorithm almost always converges to an optimal schedule with a reasonable number of iterations. Thus, the proposed algorithm can also be used for bench-marking suboptimal scheduling algorithms.

  10. Heat networks in France in 2014. Heat networks: which price for the consumer?

    International Nuclear Information System (INIS)

    Reynaud, Didier; Gong, Zheng; Moreau, Sylvain; Bottin, Anne; Reperant, Patricia

    2016-04-01

    A first document publishes and comments various statistics regarding heat networks in France in 2014: distribution in terms of quantity of supplied heat, main urban units, distribution in terms of urban unit size and in terms of community type (land, isolated town, outskirts, centre town). It also indicates the types, percentages and evolution of energies consumed in these heat networks, the shares of fossil and renewable energies, and the distribution of networks in terms of energy type. Some regional data are briefly commented (energy shares in each region, number of primary housing connected to an urban heating network). This publication also indicates methodological aspects and the definitions of the main components and characteristics of a heat network. Notably based on some of these data, the second document comments the price of heat supplied in these heat networks in 2014 as these prices are not regulated and depend on agreements between clients and providers. These prices are analysed in terms of linear thermal density, of heat quantity supplied by delivery point, of sector (industry, housing, office building), of rate of renewable energy, and of operator status (public service delegation or private ownership)

  11. Gain and exposure scheduling to compensate for photorefractive neural-network weight decay

    Science.gov (United States)

    Goldstein, Adam A.; Petrisor, Gregory C.; Jenkins, B. Keith

    1995-03-01

    A gain and exposure schedule that theoretically eliminates the effect of photorefractive weight decay for the general class of outer-product neural-network learning algorithms (e.g., backpropagation, Widrow-Hoff, perceptron) is presented. This schedule compensates for photorefractive diffraction-efficiency decay by iteratively increasing the spatial-light-modulator transfer function gain and decreasing the weight-update exposure time. Simulation results for the scheduling procedure, as applied to backpropagation learning for the exclusive-OR problem, show improved learning performance compared with results for networks trained without scheduling.

  12. Deterministic Echo State Networks Based Stock Price Forecasting

    Directory of Open Access Journals (Sweden)

    Jingpei Dan

    2014-01-01

    Full Text Available Echo state networks (ESNs, as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500 demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

  13. DIMACS Workshop on Interconnection Networks and Mapping, and Scheduling Parallel Computations

    CERN Document Server

    Rosenberg, Arnold L; Sotteau, Dominique; NSF Science and Technology Center in Discrete Mathematics and Theoretical Computer Science; Interconnection networks and mapping and scheduling parallel computations

    1995-01-01

    The interconnection network is one of the most basic components of a massively parallel computer system. Such systems consist of hundreds or thousands of processors interconnected to work cooperatively on computations. One of the central problems in parallel computing is the task of mapping a collection of processes onto the processors and routing network of a parallel machine. Once this mapping is done, it is critical to schedule computations within and communication among processor from universities and laboratories, as well as practitioners involved in the design, implementation, and application of massively parallel systems. Focusing on interconnection networks of parallel architectures of today and of the near future , the book includes topics such as network topologies,network properties, message routing, network embeddings, network emulation, mappings, and efficient scheduling. inputs for a process are available where and when the process is scheduled to be computed. This book contains the refereed pro...

  14. Joint Network Coding and Opportunistic Scheduling for the Bidirectional Relay Channel

    KAUST Repository

    Shaqfeh, Mohammad

    2013-05-27

    In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users’ transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited.

  15. Joint Network Coding and Opportunistic Scheduling for the Bidirectional Relay Channel

    KAUST Repository

    Shaqfeh, Mohammad; Alnuweiri, Hussein; Alouini, Mohamed-Slim; Zafar, Ammar

    2013-01-01

    In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users’ transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited.

  16. Space network scheduling benchmark: A proof-of-concept process for technology transfer

    Science.gov (United States)

    Moe, Karen; Happell, Nadine; Hayden, B. J.; Barclay, Cathy

    1993-01-01

    This paper describes a detailed proof-of-concept activity to evaluate flexible scheduling technology as implemented in the Request Oriented Scheduling Engine (ROSE) and applied to Space Network (SN) scheduling. The criteria developed for an operational evaluation of a reusable scheduling system is addressed including a methodology to prove that the proposed system performs at least as well as the current system in function and performance. The improvement of the new technology must be demonstrated and evaluated against the cost of making changes. Finally, there is a need to show significant improvement in SN operational procedures. Successful completion of a proof-of-concept would eventually lead to an operational concept and implementation transition plan, which is outside the scope of this paper. However, a high-fidelity benchmark using actual SN scheduling requests has been designed to test the ROSE scheduling tool. The benchmark evaluation methodology, scheduling data, and preliminary results are described.

  17. Applying the behavioral economics principle of unit price to DRO schedule thinning.

    Science.gov (United States)

    Roane, Henry S; Falcomata, Terry S; Fisher, Wayne W

    2007-01-01

    Within the context of behavioral economics, the ratio of response requirements to reinforcer magnitude is called unit price. In this investigation, we yoked increases in reinforcer magnitude with increases in intervals of differential reinforcement of other behavior (DRO) to thin DRO intervals to a terminal value.

  18. Applying the Behavioral Economics Principle of Unit Price to DRO Schedule Thinning

    Science.gov (United States)

    Roane, Henry S.; Falcomata, Terry S.; Fisher, Wayne W.

    2007-01-01

    Within the context of behavioral economics, the ratio of response requirements to reinforcer magnitude is called "unit price." In this investigation, we yoked increases in reinforcer magnitude with increases in intervals of differential reinforcement of other behavior (DRO) to thin DRO intervals to a terminal value. (Contains 1 figure.)

  19. A three-stage heuristic for harvest scheduling with access road network development

    Science.gov (United States)

    Mark M. Clark; Russell D. Meller; Timothy P. McDonald

    2000-01-01

    In this article we present a new model for the scheduling of forest harvesting with spatial and temporal constraints. Our approach is unique in that we incorporate access road network development into the harvest scheduling selection process. Due to the difficulty of solving the problem optimally, we develop a heuristic that consists of a solution construction stage...

  20. An efficient schedule based data aggregation using node mobility for wireless sensor network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Pawar, Pranav M.; Prasad, Neeli R.

    2014-01-01

    In the Wireless Sensor Networks, (WSNs) a key challenge is to schedule the activities of the mobile node for improvement in throughput, energy consumption and delay. This paper proposes efficient schedule based data aggregation algorithm using node mobility (SDNM). It considers the cluster...

  1. Low-Complexity Scheduling and Power Adaptation for Coordinated Cloud-Radio Access Networks

    KAUST Repository

    Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2017-01-01

    In practical wireless systems, the successful implementation of resource allocation techniques strongly depends on the algorithmic complexity. Consider a cloud-radio access network (CRAN), where the central cloud is responsible for scheduling

  2. Simulation and Optimization Methodologies for Military Transportation Network Routing and Scheduling and for Military Medical Services

    National Research Council Canada - National Science Library

    Rodin, Ervin Y

    2005-01-01

    The purpose of this present research was to develop a generic model and methodology for analyzing and optimizing large-scale air transportation networks including both their routing and their scheduling...

  3. Real-time-service-based Distributed Scheduling Scheme for IEEE 802.16j Networks

    OpenAIRE

    Kuo-Feng Huang; Shih-Jung Wu

    2013-01-01

    Supporting Quality of Service (QoS) guarantees for diverse multimedia services is the primary concern for IEEE802.16j networks. A scheduling scheme that satisfies the QoS requirements has become more important for wireless communications. We proposed an adaptive nontransparent-based distributed scheduling scheme (ANDS) for IEEE 802.16j networks. ANDS comprises three major components: Priority Assignment, Resource Allocation, Preserved Bandwidth Adjustment. Different service-type connections p...

  4. Narrow Networks On The Health Insurance Marketplaces: Prevalence, Pricing, And The Cost Of Network Breadth.

    Science.gov (United States)

    Dafny, Leemore S; Hendel, Igal; Marone, Victoria; Ody, Christopher

    2017-09-01

    Anecdotal reports and systematic research highlight the prevalence of narrow-network plans on the Affordable Care Act's health insurance Marketplaces. At the same time, Marketplace premiums in the period 2014-16 were much lower than projected by the Congressional Budget Office in 2009. Using detailed data on the breadth of both hospital and physician networks, we studied the prevalence of narrow networks and quantified the association between network breadth and premiums. Controlling for many potentially confounding factors, we found that a plan with narrow physician and hospital networks was 16 percent cheaper than a plan with broad networks for both, and that narrowing the breadth of just one type of network was associated with a 6-9 percent decrease in premiums. Narrow-network plans also have a sizable impact on federal outlays, as they depress the premium of the second-lowest-price silver plan, to which subsidy amounts are linked. Holding all else constant, we estimate that federal subsidies would have been 10.8 percent higher in 2014 had Marketplaces required all plans to offer broad provider networks. Narrow networks are a promising source of potential savings for other segments of the commercial insurance market. Project HOPE—The People-to-People Health Foundation, Inc.

  5. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

    International Nuclear Information System (INIS)

    Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.

    2004-01-01

    This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)

  6. Fuzzy Networked Control Systems Design Considering Scheduling Restrictions

    Directory of Open Access Journals (Sweden)

    H. Benítez-Pérez

    2012-01-01

    known a priory but from a dynamic real-time behavior. To do so, the use of priority dynamic Priority exchange scheduling is performed. The objective of this paper is to show a way to tackle multiple time delays that are bounded and the dynamic response from real-time scheduling approximation. The related control law is designed considering fuzzy logic approximation for nonlinear time delays coupling, where the main advantage is the integration of this behavior through extended state space representation keeping certain linear and bounded behavior and leading to a stable situation during events presentation by guaranteeing stability through Lyapunov.

  7. The Carbon Trading Price and Trading Volume Forecast in Shanghai City by BP Neural Network

    OpenAIRE

    Liu Zhiyuan; Sun Zongdi

    2017-01-01

    In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network...

  8. Learning Search Control Knowledge for Deep Space Network Scheduling

    Science.gov (United States)

    Gratch, Jonathan; Chien, Steve; DeJong, Gerald

    1993-01-01

    While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.

  9. 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...

  10. A Novel Message Scheduling Framework for Delay Tolerant Networks Routing

    KAUST Repository

    Elwhishi, Ahmed; Ho, Pin-Han; Naik, K.; Shihada, Basem

    2013-01-01

    new message scheduling framework for epidemic and two-hop forwarding routing in DTNs, such that the forwarding/dropping decision can be made at a node during each contact for either optimal message delivery ratio or message delivery delay. Extensive

  11. Access pricing for transmission networks: Hypotheses and empirical evidence

    Energy Technology Data Exchange (ETDEWEB)

    Martoccia, Maria [Decision Technology Centre, London (United Kingdom)

    1999-08-01

    The sectors characterised by the use of transmission or transport networks as inputs of production (electricity, gas, telecommunications) have long been considered as natural monopolies. Thanks to the technological innovations which have modified the economics of production (as in electricity generation) or that have driven the development of high value added services (as in telecommunications), the boundaries of the old natural monopolies have been eroded by the presence of operators potentially able to compete in national and international markets. The objective is to delineate, by analysing the more significant theoretical contributions and some of the restructuring experiences of the sector in question, the possible regulatory solutions which, in the perspective of a `European market` for electricity, makes the management and the expansion of the transmission networks adequate for the `open access` of national electricity sectors. The analysis of some mature experiences, such as in Chile, Argentina, the UK and Norway, in the second section, will offer a useful support to this evaluation. The regulatory solution here adopted will be analysed, in particular, with reference to the two main problems outlined above: on the one hand, the problem of providing through prices the necessary information about the opportunities of using the transmission assets; and on the other hand, the problem of defining an efficient incentive mechanism for the behaviour of the monopolist (the owner of the transmission assets). Finally, by considering the limits found in the solutions explored in these models, we will try, in the third section, to delineate the evolution that the regulation of the analysed sectors could follow, in an attempt to make the optimal solution defined in the first section consistent with the imperfections of the real scenarios. (EHS)

  12. On the number of different dynamics in Boolean networks with deterministic update schedules.

    Science.gov (United States)

    Aracena, J; Demongeot, J; Fanchon, E; Montalva, M

    2013-04-01

    Deterministic Boolean networks are a type of discrete dynamical systems widely used in the modeling of genetic networks. The dynamics of such systems is characterized by the local activation functions and the update schedule, i.e., the order in which the nodes are updated. In this paper, we address the problem of knowing the different dynamics of a Boolean network when the update schedule is changed. We begin by proving that the problem of the existence of a pair of update schedules with different dynamics is NP-complete. However, we show that certain structural properties of the interaction diagraph are sufficient for guaranteeing distinct dynamics of a network. In [1] the authors define equivalence classes which have the property that all the update schedules of a given class yield the same dynamics. In order to determine the dynamics associated to a network, we develop an algorithm to efficiently enumerate the above equivalence classes by selecting a representative update schedule for each class with a minimum number of blocks. Finally, we run this algorithm on the well known Arabidopsis thaliana network to determine the full spectrum of its different dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. QoS Differentiated and Fair Packet Scheduling in Broadband Wireless Access Networks

    Directory of Open Access Journals (Sweden)

    Zhang Yan

    2009-01-01

    Full Text Available This paper studies the packet scheduling problem in Broadband Wireless Access (BWA networks. The key difficulties of the BWA scheduling problem lie in the high variability of wireless channel capacity and the unknown model of packet arrival process. It is difficult for traditional heuristic scheduling algorithms to handle the situation and guarantee satisfying performance in BWA networks. In this paper, we introduce learning-based approach for a better solution. Specifically, we formulate the packet scheduling problem as an average cost Semi-Markov Decision Process (SMDP. Then, we solve the SMDP by using reinforcement learning. A feature-based linear approximation and the Temporal-Difference learning technique are employed to produce a near optimal solution of the corresponding SMDP problem. The proposed algorithm, called Reinforcement Learning Scheduling (RLS, has in-built capability of self-training. It is able to adaptively and timely regulate its scheduling policy according to the instantaneous network conditions. Simulation results indicate that RLS outperforms two classical scheduling algorithms and simultaneously considers: (i effective QoS differentiation, (ii high bandwidth utilization, and (iii both short-term and long-term fairness.

  14. Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

    KAUST Repository

    Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2016-01-01

    results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

  15. Sleep Scheduling in Critical Event Monitoring with Wireless Sensor Networks

    NARCIS (Netherlands)

    Guo, Peng; Jiang, Tao; Zhang, Qian; Zhang, Kui

    In this paper, we focus on the applications of wireless sensor networks (WSNs) for critical event monitoring, where normally there are only small number of packets need to be transmitted, while when urgent event occurs, the alarm should be broadcast to the entire network as soon as possible. During

  16. Optimal task scheduling policy in energy harvesting wireless sensor networks

    NARCIS (Netherlands)

    Rao, Vijay S.; Prasad, R. Venkatesha; Niemegeers, Ignas G M M

    2015-01-01

    Ambient energy harvesting for Wireless Sensor Networks (WSNs) is being pitched as a promising solution for long-lasting deployments in various WSN applications. However, the sensor nodes most often do not have enough energy to handle application, network and house-keeping tasks because amount of

  17. Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

    Science.gov (United States)

    Yang, S; Wang, D

    2000-01-01

    This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

  18. Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

    Science.gov (United States)

    Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

    2016-04-01

    The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

  19. Visibility graph network analysis of natural gas price: The case of North American market

    Science.gov (United States)

    Sun, Mei; Wang, Yaqi; Gao, Cuixia

    2016-11-01

    Fluctuations in prices of natural gas significantly affect global economy. Therefore, the research on the characteristics of natural gas price fluctuations, turning points and its influencing cycle on the subsequent price series is of great significance. Global natural gas trade concentrates on three regional markets: the North American market, the European market and the Asia-Pacific market, with North America having the most developed natural gas financial market. In addition, perfect legal supervision and coordinated regulations make the North American market more open and more competitive. This paper focuses on the North American natural gas market specifically. The Henry Hub natural gas spot price time series is converted to a visibility graph network which provides a new direction for macro analysis of time series, and several indicators are investigated: degree and degree distribution, the average shortest path length and community structure. The internal mechanisms underlying price fluctuations are explored through the indicators. The results show that the natural gas prices visibility graph network (NGP-VGN) is of small-world and scale-free properties simultaneously. After random rearrangement of original price time series, the degree distribution of network becomes exponential distribution, different from the original ones. This means that, the original price time series is of long-range negative correlation fractal characteristic. In addition, nodes with large degree correspond to significant geopolitical or economic events. Communities correspond to time cycles in visibility graph network. The cycles of time series and the impact scope of hubs can be found by community structure partition.

  20. An enhanced radial basis function network for short-term electricity price forecasting

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)

  1. Genetic algorithm and neural network hybrid approach for job-shop scheduling

    OpenAIRE

    Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

    1998-01-01

    Copyright @ 1998 ACTA Press This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and ...

  2. DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

    OpenAIRE

    Qiao Wei; Li Ying; Wu Zhong-Hai

    2017-01-01

    Deep neural networks (DNNs) have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling ...

  3. Efficient Resource Scheduling by Exploiting Relay Cache for Cellular Networks

    Directory of Open Access Journals (Sweden)

    Chun He

    2015-01-01

    Full Text Available In relay-enhanced cellular systems, throughput of User Equipment (UE is constrained by the bottleneck of the two-hop link, backhaul link (or the first hop link, and access link (the second hop link. To maximize the throughput, resource allocation should be coordinated between these two hops. A common resource scheduling algorithm, Adaptive Distributed Proportional Fair, only ensures that the throughput of the first hop is greater than or equal to that of the second hop. But it cannot guarantee a good balance of the throughput and fairness between the two hops. In this paper, we propose a Two-Hop Balanced Distributed Scheduling (TBS algorithm by exploiting relay cache for non-real-time data traffic. The evolved Node Basestation (eNB adaptively adjusts the number of Resource Blocks (RBs allocated to the backhaul link and direct links based on the cache information of relays. Each relay allocates RBs for relay UEs based on the size of the relay UE’s Transport Block. We also design a relay UE’s ACK feedback mechanism to update the data at relay cache. Simulation results show that the proposed TBS can effectively improve resource utilization and achieve a good trade-off between system throughput and fairness by balancing the throughput of backhaul and access link.

  4. DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

    Directory of Open Access Journals (Sweden)

    Qiao Wei

    2017-01-01

    Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.

  5. Distributed Hybrid Scheduling in Multi-Cloud Networks using Conflict Graphs

    KAUST Repository

    Douik, Ahmed

    2017-09-07

    Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and, therefore, allows for joint signal processing within the cloud transmission. Across the multiple clouds, however, only scheduling-level coordination is permitted, as low levels of backhaul communication are feasible. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), which are maintained at a fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled to a single cloud at most, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs’ frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The considered scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, which can be solved using efficient techniques. The paper then proposes solving the problem using both optimal and heuristic algorithms that can be implemented in a distributed fashion across the network. The proposed distributed algorithms rely on the well-chosen structure of the constructed conflict graph utilized to solve the maximum-weight independent set problem. Simulation results suggest that the proposed optimal and heuristic hybrid scheduling strategies provide appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

  6. Dynamic supply chain network design with capacity planning and multi-period pricing

    DEFF Research Database (Denmark)

    Fattahi, Mohammad; Mahootchi, Masoud; Govindan, Kannan

    2015-01-01

    This paper addresses a new problem in designing and planning a multi-echelon and multi-product supply chain network over a multi-period horizon in which customer zones have price-sensitive demands. Based on price-demand relationships, a generic method is presented to obtain price levels...... for products and then, a mixed-integer linear programming model is developed. Due to the problem intractability, a simulated annealing algorithm that uses some developed linear relaxation-based heuristics for capacity planning and pricing is presented. Numerical results demonstrate the significance...

  7. Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks

    Directory of Open Access Journals (Sweden)

    Huan Chen

    2017-04-01

    Full Text Available Heating oil is an extremely important heating fuel to consumers in northeastern United States. This paper studies the fluctuations law and dynamic behavior of heating oil spot and futures prices by setting up their complex network models based on the data of America in recent 30 years. Firstly, modes are defined by the method of coarse graining, the spot price fluctuation network of heating oil (HSPFN and its futures price fluctuation network (HFPFN in different periods are established to analyze the transformation characteristics between the modes. Secondly, several indicators are investigated: average path length, node strength and strength distribution, betweeness, etc. In addition, a function is established to measure and analyze the network similarity. The results show the cumulative time of new nodes appearing in either spot or futures price network is not random but exhibits a growth trend of straight line. Meanwhile, the power law distributions of spot and futures price fluctuations in different periods present regularity and complexity. Moreover, these prices are strongly correlated in stable fluctuation period but weak in the phase of sharp fluctuation. Finally, the time distribution characteristics of important modes in the networks and the evolution results of the topological properties mentioned above are obtained.

  8. Rate adaptation in ad hoc networks based on pricing

    CSIR Research Space (South Africa)

    Awuor, F

    2011-09-01

    Full Text Available that incorporates penalty (pricing) obtruded to users’ choices of transmission parameters to curb the self-interest behaviour. Therefore users determine their data rates and transmit power based on the perceived coupled interference at the intended receiver...

  9. The Spectrum Sharing in Cognitive Radio Networks Based on Competitive Price Game

    Directory of Open Access Journals (Sweden)

    Y. B. Li

    2012-09-01

    Full Text Available The competitive price game model is used to analyze the spectrum sharing problem in the cognitive radio networks, and the spectrum sharing problem with the constraints of available spectrum resource from primary users is further discussed in this paper. The Rockafeller multiplier method is applied to deal with the constraints of available licensed spectrum resource, and the improved profits function is achieved, which can be used to measure the impact of shared spectrum price strategies on the system profit. However, in the competitive spectrum sharing problem of practical cognitive radio network, primary users have to determine price of the shared spectrum without the acknowledgement of the other primary user’s price strategies. Thus a fast gradient iterative calculation method of equilibrium price is proposed, only with acknowledgement of the price strategies of shared spectrum during last cycle. Through the adaptive iteration at the direction with largest gradient of improved profit function, the equilibrium price strategies can be achieved rapidly. It can also avoid the predefinition of adjustment factor according to the parameters of communication system in conventional linear iteration method. Simulation results show that the proposed competitive price spectrum sharing model can be applied in the cognitive radio networks with constraints of available licensed spectrum, and it has better convergence performance.

  10. Method and apparatus for scheduling broadcasts in social networks

    KAUST Repository

    Manzoor, Emaad Ahmed

    2016-08-25

    A method, apparatus, and computer readable medium are provided for maximizing consumption of broadcasts by a producer. An example method includes receiving selection of a total number of time slots to use for scheduling broadcasts, and receiving information regarding the producer\\'s followers. The example method further 5 includes identifying, by a processor and based on the received information, discount factors associated with the producer\\'s followers, and calculating, by the processor and based on the received information, a predicted number of competitor broadcasts during each time slot of the total number of time slots. Finally, the example method includes determining, by the processor and based on the discount factors and the predicted 10 number of competitor broadcasts during each time slot, a number of broadcasts for the producer to transmit in each time slot of the total number of time slots.

  11. A Gain-Scheduling PI Control Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Stefania Tronci

    2017-01-01

    Full Text Available This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR, considering both single-input single-output (SISO and multi-input multi-output (MIMO control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

  12. OCRWM transportation network to support budget/schedule estimates

    International Nuclear Information System (INIS)

    Sullivan, G.L.; Wankerl, M.W.

    1991-01-01

    The Office of Civilian Radioactive Waste Management (OCRWM) has the objective of developing and placing into operation a system capable of transporting spent nuclear fuel and high-level waste from the various waste sources to waste receiving facilities beginning in 1998. The operational tranportation system (TS) to perform this function will consist of five subsystems. To determine what developmental efforts will be required for these subsystems, an effort was initiated in early 1990 to identify the required activities and define the schedule for each. This effort was expanded in late 1990 to include the Economic and Systems Analysis (E ampersand SA), and the Institutional work required to ensure that the TS is cost effective, fully integrated, and publicly accepted

  13. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  14. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

    International Nuclear Information System (INIS)

    Yu, Lean; Wang, Shouyang; Lai, Kin Keung

    2008-01-01

    In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

  15. Scheduling of network access for feedback-based embedded systems

    Science.gov (United States)

    Liberatore, Vincenzo

    2002-07-01

    nd communication capabilities. Examples range from smart dust embedded in building materials to networks of appliances in the home. Embedded devices will be deployed in unprecedented numbers, will enable pervasive distributed computing, and will radically change the way people interact with the surrounding environment [EGH00a]. The paper targets embedded systems and their real-time (RT) communication requirements. RT requirements arise from the

  16. Multi-User Preemptive Scheduling For Critical Low Latency Communications in 5G Networks

    DEFF Research Database (Denmark)

    Abdul-Mawgood Ali Ali Esswie, Ali; Pedersen, Klaus

    2018-01-01

    5G new radio is envisioned to support three major service classes: enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine type communications. Emerging URLLC services require up to one millisecond of communication latency with 99.999% success...... probability. Though, there is a fundamental trade-off between system spectral efficiency (SE) and achievable latency. This calls for novel scheduling protocols which cross-optimize system performance on user-centric; instead of network-centric basis. In this paper, we develop a joint multi-user preemptive...... scheduling strategy to simultaneously cross-optimize system SE and URLLC latency. At each scheduling opportunity, available URLLC traffic is always given higher priority. When sporadic URLLC traffic appears during a transmission time interval (TTI), proposed scheduler seeks for fitting the URLLC-eMBB traffic...

  17. Optimization of workflow scheduling in Utility Management System with hierarchical neural network

    Directory of Open Access Journals (Sweden)

    Srdjan Vukmirovic

    2011-08-01

    Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

  18. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    Science.gov (United States)

    Lee, Charles H.; Cheung, Kar-Ming

    2012-01-01

    In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

  19. Short-term electricity prices forecasting in a competitive market: A neural network approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

    2007-01-01

    This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

  20. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-04-01

    Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

  1. Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate

    International Nuclear Information System (INIS)

    Esmaeily, Ali; Ahmadi, Abdollah; Raeisi, Fatima; Ahmadi, Mohammad Reza; Esmaeel Nezhad, Ali; Janghorbani, Mohammadreza

    2017-01-01

    A new optimization framework based on MILP model is introduced in the paper for the problem of stochastic self-scheduling of hydrothermal units known as HTSS Problem implemented in a joint energy and reserve electricity market with day-ahead mechanism. The proposed MILP framework includes some practical constraints such as the cost due to valve-loading effect, the limit due to DRR and also multi-POZs, which have been less investigated in electricity market models. For the sake of more accuracy, for hydro generating units’ model, multi performance curves are also used. The problem proposed in this paper is formulated using a model on the basis of a stochastic optimization technique while the objective function is maximizing the expected profit utilizing MILP technique. The suggested stochastic self-scheduling model employs the price forecast error in order to take into account the uncertainty due to price. Besides, LMCS is combined with roulette wheel mechanism so that the scenarios corresponding to the non-spinning reserve price and spinning reserve price as well as the energy price at each hour of the scheduling are generated. Finally, the IEEE 118-bus power system is used to indicate the performance and the efficiency of the suggested technique. - Highlights: • Characterizing the uncertainties of price and FOR of units. • Replacing the fixed ramping rate constraints with the dynamic ones. • Proposing linearized model for the valve-point effects of thermal units. • Taking into consideration the multi-POZs relating to the thermal units. • Taking into consideration the multi-performance curves of hydroelectric units.

  2. Multi-Channel Scheduling for Fast Convergecast in Wireless Sensor Networks

    NARCIS (Netherlands)

    Durmaz, O.; Ghosh, A.; Krishnamachari, B.; Chintalapudi, K.

    We explore the following fundamental question - how fast can information be collected from a wireless sensor network? We consider a number of design parameters such as, power control, time and frequency scheduling, and routing. There are essentially two factors that hinder efficient data collection

  3. Time-optimum packet scheduling for many-to-one routing in wireless sensor networks

    Science.gov (United States)

    Song, W.-Z.; Yuan, F.; LaHusen, R.; Shirazi, B.

    2007-01-01

    This paper studies the wireless sensor networks (WSN) application scenario with periodical traffic from all sensors to a sink. We present a time-optimum and energy-efficient packet scheduling algorithm and its distributed implementation. We first give a general many-to-one packet scheduling algorithm for wireless networks, and then prove that it is time-optimum and costs [image omitted], N(u0)-1) time slots, assuming each node reports one unit of data in each round. Here [image omitted] is the total number of sensors, while [image omitted] denotes the number of sensors in a sink's largest branch subtree. With a few adjustments, we then show that our algorithm also achieves time-optimum scheduling in heterogeneous scenarios, where each sensor reports a heterogeneous amount of data in each round. Then we give a distributed implementation to let each node calculate its duty-cycle locally and maximize efficiency globally. In this packet-scheduling algorithm, each node goes to sleep whenever it is not transceiving, so that the energy waste of idle listening is also mitigated. Finally, simulations are conducted to evaluate network performance using the Qualnet simulator. Among other contributions, our study also identifies the maximum reporting frequency that a deployed sensor network can handle.

  4. Optimizing Intermodal Train Schedules with a Design Balanced Network Design Model

    DEFF Research Database (Denmark)

    Pedersen, Michael Berliner; Crainic, Teodor Gabriel

    We present a modeling approach for optimizing intermodal trains schedules based on an infrastructure divided into time-dependent train paths. The formulation can be generalized to a capacitated multi commodity network design model with additional design balance constraints. We present a Tabu Search...

  5. Distributed Hybrid Scheduling in Multi-Cloud Networks using Conflict Graphs

    KAUST Repository

    Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2017-01-01

    Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access

  6. Decentralized vs. centralized scheduling in wireless sensor networks for data fusion

    NARCIS (Netherlands)

    Mitici, M.A.; Goseling, Jasper; de Graaf, Maurits; Boucherie, Richardus J.

    2014-01-01

    We consider the problem of data estimation in a sensor wireless network where sensors transmit their observations according to decentralized and centralized transmission schedules. A data collector is interested in achieving a data estimation using several sensor observations such that the variance

  7. Decentralized vs. centralized scheduling in wireless sensor networks for data fusion

    NARCIS (Netherlands)

    Mitici, Mihaela; Goseling, Jasper; de Graaf, Maurits; Boucherie, Richardus J.

    2013-01-01

    We consider the problem of data estimation in a sensor wireless network where sensors transmit their observations according to decentralized and centralized transmission schedules. A data collector is interested in achieving a data estimation using several sensor observations such that the variance

  8. Coordinated scheduling for the downlink of cloud radio-access networks

    KAUST Repository

    Douik, Ahmed S.

    2015-09-11

    This paper addresses the coordinated scheduling problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy and the synchronization of the transmit frames across the connected base-stations (BS). The transmitted frame of every BS consists of several time/frequency blocks, called power-zones (PZ), maintained at fixed transmit power. The paper considers the problem of scheduling users to PZs and BSs in a coordinated fashion across the network, by maximizing a network-wide utility under the practical constraint that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the scheduling graph in which each vertex represents an association of users, PZs and BSs. The problem is formulated as a maximum weight clique, in which the weight of each vertex is the benefit of the association represented by that vertex. The paper further presents heuristic algorithms with low computational complexity. Simulation results show the performance of the proposed algorithms and suggest that the heuristics perform near optimal in low shadowing environments. © 2015 IEEE.

  9. Congestion Control and Traffic Scheduling for Collaborative Crowdsourcing in SDN Enabled Mobile Wireless Networks

    Directory of Open Access Journals (Sweden)

    Dawei Shen

    2018-01-01

    Full Text Available Currently, a number of crowdsourcing-based mobile applications have been implemented in mobile networks and Internet of Things (IoT, targeted at real-time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN. This paper focuses on the traffic scheduling and load balancing problem in software-defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput.

  10. Constant Price of Anarchy in Network Creation Games via Public Service Advertising

    Science.gov (United States)

    Demaine, Erik D.; Zadimoghaddam, Morteza

    Network creation games have been studied in many different settings recently. These games are motivated by social networks in which selfish agents want to construct a connection graph among themselves. Each node wants to minimize its average or maximum distance to the others, without paying much to construct the network. Many generalizations have been considered, including non-uniform interests between nodes, general graphs of allowable edges, bounded budget agents, etc. In all of these settings, there is no known constant bound on the price of anarchy. In fact, in many cases, the price of anarchy can be very large, namely, a constant power of the number of agents. This means that we have no control on the behavior of network when agents act selfishly. On the other hand, the price of stability in all these models is constant, which means that there is chance that agents act selfishly and we end up with a reasonable social cost.

  11. Sleep/wake scheduling scheme for minimizing end-to-end delay in multi-hop wireless sensor networks

    OpenAIRE

    Madani Sajjad; Nazir Babar; Hasbullah Halabi

    2011-01-01

    Abstract We present a sleep/wake schedule protocol for minimizing end-to-end delay for event driven multi-hop wireless sensor networks. In contrast to generic sleep/wake scheduling schemes, our proposed algorithm performs scheduling that is dependent on traffic loads. Nodes adapt their sleep/wake schedule based on traffic loads in response to three important factors, (a) the distance of the node from the sink node, (b) the importance of the node's location from connectivity's perspective, and...

  12. Design of Hierarchical Ring Networks Using Branch-and-Price

    DEFF Research Database (Denmark)

    Thomadsen, Tommy; Stidsen, Thomas K.

    2004-01-01

    -ring is designed connecting the metro-rings, minimizing fixed link establishment costs of the federal-ring. A branch-and-price algorithm is presented for the design of the bottom layer and it is suggested that existing methods are used for the design of the federal-ring. Computational results are given...

  13. Price

    International Nuclear Information System (INIS)

    Anon.

    1991-01-01

    The price terms in wheeling contracts very substantially, reflecting the differing conditions affecting the parties contracting for the service. These terms differ in the manner in which rates are calculated, the formulas used, and the philosophy underlying the accord. For example, and EEI study found that firm wheeling rates ranged from 20 cents to $1.612 per kilowatt per month. Nonfirm rates ranged from .15 mills to 5.25 mills per kilowatt-hour. The focus in this chapter is on cost-based rates, reflecting the fact that the vast majority of existing contracts are based on rate designs reflecting embedded costs. This situation may change in the future, but, for now, this fact can't be ignored

  14. ELIMINATION OF THE DISADVANTAGES OF SCHEDULING-NETWORK PLANNING BY APPLYING THE MATRIX OF KEY PROJECT EVENTS

    OpenAIRE

    Morozenko Andrey Aleksandrovich; Krasovskiy Dmitriy Viktorovich

    2017-01-01

    The article discusses the current disadvantages of the scheduling-network planning in the management of the terms of investment-construction project. Problems associated with the construction of the schedule and the definitions of the duration of the construction project are being studied. The problems of project management for the management apparatus are shown, which consists in the absence of mechanisms for prompt response to deviations in the parameters of the scheduling-network diagram. ...

  15. Multi-layer service function chaining scheduling based on auxiliary graph in IP over optical network

    Science.gov (United States)

    Li, Yixuan; Li, Hui; Liu, Yuze; Ji, Yuefeng

    2017-10-01

    Software Defined Optical Network (SDON) can be considered as extension of Software Defined Network (SDN) in optical networks. SDON offers a unified control plane and makes optical network an intelligent transport network with dynamic flexibility and service adaptability. For this reason, a comprehensive optical transmission service, able to achieve service differentiation all the way down to the optical transport layer, can be provided to service function chaining (SFC). IP over optical network, as a promising networking architecture to interconnect data centers, is the most widely used scenarios of SFC. In this paper, we offer a flexible and dynamic resource allocation method for diverse SFC service requests in the IP over optical network. To do so, we firstly propose the concept of optical service function (OSF) and a multi-layer SFC model. OSF represents the comprehensive optical transmission service (e.g., multicast, low latency, quality of service, etc.), which can be achieved in multi-layer SFC model. OSF can also be considered as a special SF. Secondly, we design a resource allocation algorithm, which we call OSF-oriented optical service scheduling algorithm. It is able to address multi-layer SFC optical service scheduling and provide comprehensive optical transmission service, while meeting multiple optical transmission requirements (e.g., bandwidth, latency, availability). Moreover, the algorithm exploits the concept of Auxiliary Graph. Finally, we compare our algorithm with the Baseline algorithm in simulation. And simulation results show that our algorithm achieves superior performance than Baseline algorithm in low traffic load condition.

  16. NUMA-Aware Thread Scheduling for Big Data Transfers over Terabits Network Infrastructure

    Directory of Open Access Journals (Sweden)

    Taeuk Kim

    2018-01-01

    Full Text Available The evergrowing trend of big data has led scientists to share and transfer the simulation and analytical data across the geodistributed research and computing facilities. However, the existing data transfer frameworks used for data sharing lack the capability to adopt the attributes of the underlying parallel file systems (PFS. LADS (Layout-Aware Data Scheduling is an end-to-end data transfer tool optimized for terabit network using a layout-aware data scheduling via PFS. However, it does not consider the NUMA (Nonuniform Memory Access architecture. In this paper, we propose a NUMA-aware thread and resource scheduling for optimized data transfer in terabit network. First, we propose distributed RMA buffers to reduce memory controller contention in CPU sockets and then schedule the threads based on CPU socket and NUMA nodes inside CPU socket to reduce memory access latency. We design and implement the proposed resource and thread scheduling in the existing LADS framework. Experimental results showed from 21.7% to 44% improvement with memory-level optimizations in the LADS framework as compared to the baseline without any optimization.

  17. Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey

    OpenAIRE

    Luong, Nguyen Cong; Wang, Ping; Niyato, Dusit; Liang, Ying-Chang; Hou, Fen; Han, Zhu

    2017-01-01

    This paper presents a comprehensive literature review on applications of economic and pricing theory for resource management in the evolving fifth generation (5G) wireless networks. The 5G wireless networks are envisioned to overcome existing limitations of cellular networks in terms of data rate, capacity, latency, energy efficiency, spectrum efficiency, coverage, reliability, and cost per information transfer. To achieve the goals, the 5G systems will adopt emerging technologies such as mas...

  18. Distributed and Cooperative Link Scheduling for Large-Scale Multihop Wireless Networks

    Directory of Open Access Journals (Sweden)

    Swami Ananthram

    2007-01-01

    Full Text Available A distributed and cooperative link-scheduling (DCLS algorithm is introduced for large-scale multihop wireless networks. With this algorithm, each and every active link in the network cooperatively calibrates its environment and converges to a desired link schedule for data transmissions within a time frame of multiple slots. This schedule is such that the entire network is partitioned into a set of interleaved subnetworks, where each subnetwork consists of concurrent cochannel links that are properly separated from each other. The desired spacing in each subnetwork can be controlled by a tuning parameter and the number of time slots specified for each frame. Following the DCLS algorithm, a distributed and cooperative power control (DCPC algorithm can be applied to each subnetwork to ensure a desired data rate for each link with minimum network transmission power. As shown consistently by simulations, the DCLS algorithm along with a DCPC algorithm yields significant power savings. The power savings also imply an increased feasible region of averaged link data rates for the entire network.

  19. Power Saving Scheduling Scheme for Internet of Things over LTE/LTE-Advanced Networks

    Directory of Open Access Journals (Sweden)

    Yen-Wei Kuo

    2015-01-01

    Full Text Available The devices of Internet of Things (IoT will grow rapidly in the near future, and the power consumption and radio spectrum management will become the most critical issues in the IoT networks. Long Term Evolution (LTE technology will become a promising technology used in IoT networks due to its flat architecture, all-IP network, and greater spectrum efficiency. The 3rd Generation Partnership Project (3GPP specified the Discontinuous Reception (DRX to reduce device’s power consumption. However, the DRX may pose unexpected communication delay due to missing Physical Downlink Control Channel (PDCCH information in sleep mode. Recent studies mainly focus on optimizing DRX parameters to manage the tradeoff between the energy consumption and communication latency. In this paper, we proposed a fuzzy-based power saving scheduling scheme for IoT over the LTE/LTE-Advanced networks to deal with the issues of the radio resource management and power consumption from the scheduling and resource allocation perspective. The proposed scheme considers not only individual IoT device’s real-time requirement but also the overall network performance. The simulation results show that our proposed scheme can meet the requirements of the DRX cycle and scheduling latency and can save about half of energy consumption for IoT devices compared to conventional approaches.

  20. Distributed and Cooperative Link Scheduling for Large-Scale Multihop Wireless Networks

    Directory of Open Access Journals (Sweden)

    Ananthram Swami

    2007-12-01

    Full Text Available A distributed and cooperative link-scheduling (DCLS algorithm is introduced for large-scale multihop wireless networks. With this algorithm, each and every active link in the network cooperatively calibrates its environment and converges to a desired link schedule for data transmissions within a time frame of multiple slots. This schedule is such that the entire network is partitioned into a set of interleaved subnetworks, where each subnetwork consists of concurrent cochannel links that are properly separated from each other. The desired spacing in each subnetwork can be controlled by a tuning parameter and the number of time slots specified for each frame. Following the DCLS algorithm, a distributed and cooperative power control (DCPC algorithm can be applied to each subnetwork to ensure a desired data rate for each link with minimum network transmission power. As shown consistently by simulations, the DCLS algorithm along with a DCPC algorithm yields significant power savings. The power savings also imply an increased feasible region of averaged link data rates for the entire network.

  1. Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Gezaq Abror

    2018-01-01

    Full Text Available Wireless Sensor Network (WSN can be applied for Air Pollution Level Monitoring System that have been determined by the Environmental Impact Management Agency which is  PM10, SO2, O3, NO2 and CO. In WSN, node system is constrained to a limited power supply, so that the node system has a lifetime. To doing lifetime maximization, power management scheme is required and sensor nodes should use energy efficiently. This paper proposes dynamic sleep scheduling using Time Category-Fuzzy Logic (Time-Fuzzy Scheduling as a reference for calculating time interval for sleep and activated node system to support power management scheme. This research contributed in power management design to be applied to the WSN system to reduce energy expenditure. From the test result in real hardware node system, it can be seen that Time-Fuzzy Scheduling is better in terms of using the battery and it is better in terms of energy consumption too because it is more efficient 51.85% when it is compared with Fuzzy Scheduling, it is more efficient 68.81% when it is compared with Standard Scheduling and it is more efficient 85.03% when compared with No Scheduling.

  2. Low-Feedback Opportunistic Scheduling Schemes for Wireless Networks with Heterogenous Users

    KAUST Repository

    Rashid, Faraan

    2012-07-01

    Efficient implementation of resource sharing strategies in a multi-user wireless environment can improve the performance of a network significantly. In this thesis we study various scheduling strategies for wireless networks and handle the problem of opportunistically scheduling transmissions using channel aware schemes. First we propose a scheme that can handle users with asymmetric channel conditions and is opportunistic in the sense that it exploits the multi-user diversity of the network. The scheme requires the users to have a priori knowledge of their channel distributions. The associated overhead is limited meaning it offers reduced feedback load, that does not scale with the increasing number of users. The main technique used to shrink the feedback load is the contention based distributed implementation of a splitting algorithm that does not require explicit feedback to the scheduler from every user. The users find the best among themselves, in a distributed manner, while requiring just a ternary broadcast feedback from the scheduler at the end of each mini-slot. In addition, it can also handle fairness constraints in time and throughput to various degrees. Next we propose another opportunistic scheduler that offers most of the benefits of the previously proposed scheme but is more practical because it can also handle heterogenous users whose channel distributions are unknown. This new scheme actually reduces the complexity and is also more robust for changing traffic patterns. Finally we extend both these schemes to the scenario where there are fixed thresholds, this enables us to handle opportunistic scheduling in practical systems that can only transmit over finite number of discrete rates with the additional benefit that full feedback session, even from the selected user, is never required.

  3. Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

    Science.gov (United States)

    Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

    2017-09-01

    Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

  4. A review on application of neural networks and fuzzy logic to solve hydrothermal scheduling problem

    International Nuclear Information System (INIS)

    Haroon, S.; Malik, T.N.; Zafar, S.

    2014-01-01

    Electrical power system is highly complicated having hydro and thermal mix with large number of machines. To reduce power production cost, hydro and thermal resources are mixed. Hydrothermal scheduling is the optimal coordination of hydro and thermal plants to meet the system load demand at minimum possible operational cost while satisfying the system constraints. Hydrothermal scheduling is dynamic, large scale, non-linear and non-convex optimization problem. The classical techniques have failed in solving such problem. Artificial Intelligence Tools based techniques are used now a day to solve this complex optimization problem because of their no requirements on the nature of the problem. The aim of this research paper is to provide a comprehensive survey of literature related to both Artificial Neural Network (ANN) and Fuzzy Logic (FL) as effective optimization algorithms for the hydrothermal scheduling problem. The outcomes along with the merits and demerits of individual techniques are also discussed. (author)

  5. Consumer preferences relative to the price and network capability of small urban vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Burns, L.D.

    1979-09-01

    Preferences of consumers for small urban vehicle concepts differing only with respect to their hypothetical purchase prices and network capabilities (i.e., whether they are capable of operating on expressways, major arterials, or local streets) are analyzed using statistical techniques based on psychological scaling theories. Results from these analyses indicate that a vast majority of consumers are not readily willing to give up the accessibility provided by conventional automobiles. More specifically, over the range of hypothetical prices considered here, network capability dominates as a determinant of preferences for vehicle concepts. Also, the ability to operate vehicles on expressways is of utmost importance to consumers.

  6. A New Software for Management, Scheduling, and Optimization for the Light Hydrocarbon Pipeline Network System of Daqing Oilfield

    Directory of Open Access Journals (Sweden)

    Yongtu Liang

    2014-01-01

    Full Text Available This paper presents the new software which specifically developed based on Visual Studio 2010 for Daqing Oilfield China includes the most complex light hydrocarbon pipeline network system in Asia, has become a powerful auxiliary tool to manage field data, makes scheduling plans for batching operation, and optimizes pumping plans. Firstly, DMM for recording and managing field data is summarized. Then, the batch scheduling simulation module called SSM for the difficult batch-scheduling issues of the multiple-source pipeline network system is introduced. Finally, SOM, that is Scheduling Optimization Module, is indicated for solving the problem of the pumps being started up/shut-down frequently.

  7. Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Qihua Wang

    2017-11-01

    Full Text Available Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.

  8. Stock prices forecasting based on wavelet neural networks with PSO

    OpenAIRE

    Wang Kai-Cheng; Yang Chi-I; Chang Kuei-Fang

    2017-01-01

    This research examines the forecasting performance of wavelet neural network (WNN) model using published stock data obtained from Financial Times Stock Exchange (FTSE) Taiwan Stock Exchange (TWSE) 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO) to choose the appropriate initial network values for different companies. The findings come with two advantages. First...

  9. Coordinated Scheduling and Power Control in Cloud-Radio Access Networks

    KAUST Repository

    Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2015-01-01

    This paper addresses the joint coordinated scheduling and power control problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy, power control, and synchronization of the transmit frames across the single-antenna base-stations (BS). The transmit frame consists of several time/frequency blocks, called power-zones (PZ). The paper considers the problem of scheduling users to PZs and determining their power levels (PL), by maximizing the weighted sum-rate under the practical constraints that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the joint scheduling and power control graph formed by several clusters, where each is formed by a set of vertices, representing the possible association of users, BSs, and PLs for one specific PZ. The problem is, then, formulated as a maximumweight clique problem, in which the weight of each vertex is the sum of the benefits of the individual associations belonging to that vertex. Simulation results suggest that the proposed crosslayer scheme provides appreciable performance improvement as compared to schemes from recent literature.

  10. Coordinated Scheduling and Power Control in Cloud-Radio Access Networks

    KAUST Repository

    Douik, Ahmed

    2015-12-01

    This paper addresses the joint coordinated scheduling and power control problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy, power control, and synchronization of the transmit frames across the single-antenna base-stations (BS). The transmit frame consists of several time/frequency blocks, called power-zones (PZ). The paper considers the problem of scheduling users to PZs and determining their power levels (PL), by maximizing the weighted sum-rate under the practical constraints that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the joint scheduling and power control graph formed by several clusters, where each is formed by a set of vertices, representing the possible association of users, BSs, and PLs for one specific PZ. The problem is, then, formulated as a maximumweight clique problem, in which the weight of each vertex is the sum of the benefits of the individual associations belonging to that vertex. Simulation results suggest that the proposed crosslayer scheme provides appreciable performance improvement as compared to schemes from recent literature.

  11. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    Energy Technology Data Exchange (ETDEWEB)

    Mandal, Paras; Senjyu, Tomonobu [Department of Electrical and Electronics, University of the Ryukyus, 1 Senbaru, Nagakami Nishihara, Okinawa 903-0213 (Japan); Funabashi, Toshihisa [Meidensha Corporation, Tokyo 103-8515 (Japan)

    2006-09-15

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy. (author)

  12. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    International Nuclear Information System (INIS)

    Mandal, Paras; Senjyu, Tomonobu; Funabashi, Toshihisa

    2006-01-01

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6 h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy

  13. Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

    Science.gov (United States)

    Wan, Ying; Cao, Jinde; Wen, Guanghui

    In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

  14. Modeling the transient security constraints of natural gas network in day-ahead power system scheduling

    DEFF Research Database (Denmark)

    Yang, Jingwei; Zhang, Ning; Kang, Chongqing

    2017-01-01

    The rapid deployment of gas-fired generating units makes the power system more vulnerable to failures in the natural gas system. To reduce the risk of gas system failure and to guarantee the security of power system operation, it is necessary to take the security constraints of natural gas...... accurately, they are hard to be embedded into the power system scheduling model, which consists of algebraic equations and inequations. This paper addresses this dilemma by proposing an algebraic transient model of natural gas network which is similar to the branch-node model of power network. Based...... pipelines into account in the day-ahead power generation scheduling model. However, the minute- and hour-level dynamic characteristics of gas systems prevents an accurate decision-making simply with the steady-state gas flow model. Although the partial differential equations depict the dynamics of gas flow...

  15. The scheduling of tracking times for interplanetary spacecraft on the Deep Space Network

    Science.gov (United States)

    Webb, W. A.

    1978-01-01

    The Deep Space Network (DSN) is a network of tracking stations, located throughout the globe, used to track spacecraft for NASA's interplanetary missions. This paper describes a computer program, DSNTRAK, which provides an optimum daily tracking schedule for the DSN given the view periods at each station for a mission set of n spacecraft, where n is between 2 and 6. The objective function is specified in terms of relative total daily tracking time requirements between the n spacecraft. Linear programming is used to maximize the total daily tracking time and determine an optimal daily tracking schedule consistent with DSN station capabilities. DSNTRAK is used as part of a procedure to provide DSN load forecasting information for proposed future NASA mission sets.

  16. Decentralized vs. centralized scheduling in wireless sensor networks for data fusion

    OpenAIRE

    Mitici, M.A.; Goseling, Jasper; de Graaf, Maurits; Boucherie, Richardus J.

    2014-01-01

    We consider the problem of data estimation in a sensor wireless network where sensors transmit their observations according to decentralized and centralized transmission schedules. A data collector is interested in achieving a data estimation using several sensor observations such that the variance of the estimation is below a targeted threshold. We analyze the waiting time for a collector to receive sufficient sensor observations. We show that, for sufficiently large sensor sets, the decentr...

  17. Energy-saving scheme based on downstream packet scheduling in ethernet passive optical networks

    Science.gov (United States)

    Zhang, Lincong; Liu, Yejun; Guo, Lei; Gong, Xiaoxue

    2013-03-01

    With increasing network sizes, the energy consumption of Passive Optical Networks (PONs) has grown significantly. Therefore, it is important to design effective energy-saving schemes in PONs. Generally, energy-saving schemes have focused on sleeping the low-loaded Optical Network Units (ONUs), which tends to bring large packet delays. Further, the traditional ONU sleep modes are not capable of sleeping the transmitter and receiver independently, though they are not required to transmit or receive packets. Clearly, this approach contributes to wasted energy. Thus, in this paper, we propose an Energy-Saving scheme that is based on downstream Packet Scheduling (ESPS) in Ethernet PON (EPON). First, we design both an algorithm and a rule for downstream packet scheduling at the inter- and intra-ONU levels, respectively, to reduce the downstream packet delay. After that, we propose a hybrid sleep mode that contains not only ONU deep sleep mode but also independent sleep modes for the transmitter and the receiver. This ensures that the energy consumed by the ONUs is minimal. To realize the hybrid sleep mode, a modified GATE control message is designed that involves 10 time points for sleep processes. In ESPS, the 10 time points are calculated according to the allocated bandwidths in both the upstream and the downstream. The simulation results show that ESPS outperforms traditional Upstream Centric Scheduling (UCS) scheme in terms of energy consumption and the average delay for both real-time and non-real-time packets downstream. The simulation results also show that the average energy consumption of each ONU in larger-sized networks is less than that in smaller-sized networks; hence, our ESPS is better suited for larger-sized networks.

  18. Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

    International Nuclear Information System (INIS)

    Anbazhagan, S.; Kumarappan, N.

    2014-01-01

    Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

  19. Stock prices forecasting based on wavelet neural networks with PSO

    Directory of Open Access Journals (Sweden)

    Wang Kai-Cheng

    2017-01-01

    Full Text Available This research examines the forecasting performance of wavelet neural network (WNN model using published stock data obtained from Financial Times Stock Exchange (FTSE Taiwan Stock Exchange (TWSE 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX, hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO to choose the appropriate initial network values for different companies. The findings come with two advantages. First, the network initial values are automatically selected instead of being a constant. Second, threshold and training data percentage become constant values, because PSO assists with self-adjustment. We can achieve a success rate over 73% without the necessity to manually adjust parameter or create another math model.

  20. The Price of Selfish Stackelberg Leadership in a Network Game

    OpenAIRE

    Goldberg, P. W.; Polpinit, P.

    2007-01-01

    We study a class of games in which a finite number of agents each controls a quantity of flow to be routed through a network, and are able to split their own flow between multiple paths through the network. Recent work on this model has contrasted the social cost of Nash equilibria with the best possible social cost. Here we show that additional costs are incurred in situations where a selfish ``leader'' agent allocates his flow, and then commits to that choice so that other agents are compel...

  1. Stochastic User Equilibrium Assignment in Schedule-Based Transit Networks with Capacity Constraints

    Directory of Open Access Journals (Sweden)

    Wangtu Xu

    2012-01-01

    Full Text Available This paper proposes a stochastic user equilibrium (SUE assignment model for a schedule-based transit network with capacity constraint. We consider a situation in which passengers do not have the full knowledge about the condition of the network and select paths that minimize a generalized cost function encompassing five components: (1 ride time, which is composed of in-vehicle and waiting times, (2 overload delay, (3 fare, (4 transfer constraints, and (5 departure time difference. We split passenger demands among connections which are the space-time paths between OD pairs of the network. All transit vehicles have a fixed capacity and operate according to some preset timetables. When the capacity constraint of the transit line segment is reached, we show that the Lagrange multipliers of the mathematical programming problem are equivalent to the equilibrium passenger overload delay in the congested transit network. The proposed model can simultaneously predict how passengers choose their transit vehicles to minimize their travel costs and estimate the associated costs in a schedule-based congested transit network. A numerical example is used to illustrate the performance of the proposed model.

  2. Analysis of the transmission characteristics of China's carbon market transaction price volatility from the perspective of a complex network.

    Science.gov (United States)

    Jia, Jingjing; Li, Huajiao; Zhou, Jinsheng; Jiang, Meihui; Dong, Di

    2018-03-01

    Research on the price fluctuation transmission of the carbon trading pilot market is of great significance for the establishment of China's unified carbon market and its development in the future. In this paper, the carbon market transaction prices of Beijing, Shanghai, Tianjin, Shenzhen, and Guangdong were selected from December 29, 2013 to March 26, 2016, as sample data. Based on the view of the complex network theory, we construct a price fluctuation transmission network model of five pilot carbon markets in China, with the purposes of analyzing the topological features of this network, including point intensity, weighted clustering coefficient, betweenness centrality, and community structure, and elucidating the characteristics and transmission mechanism of price fluctuation in China's five pilot cities. The results of point intensity and weighted clustering coefficient show that the carbon prices in the five markets remained unchanged and transmitted smoothly in general, and price fragmentation is serious; however, at some point, the price fluctuates with mass phenomena. The result of betweenness centrality reflects that a small number of price fluctuations can control the whole market carbon price transmission and price fluctuation evolves in an alternate manner. The study provides direction for the scientific management of the carbon price. Policy makers should take a positive role in promoting market activity, preventing the risks that may arise from mass trade and scientifically forecasting the volatility of trading prices, which will provide experience for the establishment of a unified carbon market in China.

  3. Pricing Strategies for Viral Marketing on Social Networks

    KAUST Repository

    Arthur, David; Motwani, Rajeev; Sharma, Aneesh; Xu, Ying

    2009-01-01

    We study the use of viral marketing strategies on social networks that seek to maximize revenue from the sale of a single product. We propose a model in which the decision of a buyer to buy the product is influenced by friends that own the product

  4. Pricing-based revenue management for flexible products on a network

    NARCIS (Netherlands)

    Sierag, DIrk

    2017-01-01

    This paper proposes and analyses a pricing-based revenue management model that allows flexible products on a network, with a non-trivial extension to group reservations. Under stochastic demand the problem can be solved using dynamic programming, though it suffers from the curse of dimensionality.

  5. Price-based optimal control of power flow in electrical energy transmission networks

    NARCIS (Netherlands)

    Jokic, A.; Lazar, M.; Bosch, van den P.P.J.; Bemporad, A.; Bicchi, A.; Buttazzo, G.

    2007-01-01

    This article presents a novel control scheme for achieving optimal power balancing and congestion control in electrical energy transmission networks via nodal prices. We develop an explicit controller that guarantees economically optimal steady-state operation while respecting all line flow

  6. Improved Lower Bounds on the Price of Stability of Undirected Network Design Games

    Science.gov (United States)

    Bilò, Vittorio; Caragiannis, Ioannis; Fanelli, Angelo; Monaco, Gianpiero

    Bounding the price of stability of undirected network design games with fair cost allocation is a challenging open problem in the Algorithmic Game Theory research agenda. Even though the generalization of such games in directed networks is well understood in terms of the price of stability (it is exactly H n , the n-th harmonic number, for games with n players), far less is known for network design games in undirected networks. The upper bound carries over to this case as well while the best known lower bound is 42/23 ≈ 1.826. For more restricted but interesting variants of such games such as broadcast and multicast games, sublogarithmic upper bounds are known while the best known lower bound is 12/7 ≈ 1.714. In the current paper, we improve the lower bounds as follows. We break the psychological barrier of 2 by showing that the price of stability of undirected network design games is at least 348/155 ≈ 2.245. Our proof uses a recursive construction of a network design game with a simple gadget as the main building block. For broadcast and multicast games, we present new lower bounds of 20/11 ≈ 1.818 and 1.862, respectively.

  7. Simulation of heat exchanger network (HEN) and planning the optimum cleaning schedule

    International Nuclear Information System (INIS)

    Sanaye, Sepehr; Niroomand, Behzad

    2007-01-01

    Modeling and simulation of heat exchanger networks for estimating the amount of fouling, variations in overall heat transfer coefficient, and variations in outlet temperatures of hot and cold streams has a significant effect on production analysis. In this analysis, parameters such as the exchangers' types and arrangements, their heat transfer surface areas, mass flow rates of hot and cold streams, heat transfer coefficients and variations of fouling with time are required input data. The main goal is to find the variations of the outlet temperatures of the hot and cold streams with time to plan the optimum cleaning schedule of heat exchangers that provides the minimum operational cost or maximum amount of savings. In this paper, the simulation of heat exchanger networks is performed by choosing an asymptotic fouling function. Two main parameters in the asymptotic fouling formation model, i.e. the decay time of fouling formation (τ) and the asymptotic fouling resistance (R f ∼ ) were obtained from empirical data as input parameters to the simulation relations. These data were extracted from the technical history sheets of the Khorasan Petrochemical Plant to guaranty the consistency between our model outputs and the real operating conditions. The output results of the software program developed, including the variations with time of the outlet temperatures of the hot and cold streams, the heat transfer coefficient and the heat transfer rate in the exchangers, are presented for two case studies. Then, an objective function (operational cost) was defined, and the optimal cleaning schedule of the HEN (heat exchanger network) in the Urea and Ammonia units were found by minimizing the objective function using a numerical search method. Based on this minimization procedure, the decision was made whether a heat exchanger should be cleaned or continue to operate. The final result was the most cost effective plan for the HEN cleaning schedule. The corresponding savings by

  8. Simulation of heat exchanger network (HEN) and planning the optimum cleaning schedule

    Energy Technology Data Exchange (ETDEWEB)

    Sanaye, Sepehr [Energy Systems Improvement Laboratory, Mechanical Engineering Department, Iran University of Science and Technology (IUST), Narmak, Tehran 16488 (Iran, Islamic Republic of)]. E-mail: sepehr@iust.ac.ir; Niroomand, Behzad [Energy Systems Improvement Laboratory, Mechanical Engineering Department, Iran University of Science and Technology (IUST), Narmak, Tehran 16488 (Iran, Islamic Republic of)

    2007-05-15

    Modeling and simulation of heat exchanger networks for estimating the amount of fouling, variations in overall heat transfer coefficient, and variations in outlet temperatures of hot and cold streams has a significant effect on production analysis. In this analysis, parameters such as the exchangers' types and arrangements, their heat transfer surface areas, mass flow rates of hot and cold streams, heat transfer coefficients and variations of fouling with time are required input data. The main goal is to find the variations of the outlet temperatures of the hot and cold streams with time to plan the optimum cleaning schedule of heat exchangers that provides the minimum operational cost or maximum amount of savings. In this paper, the simulation of heat exchanger networks is performed by choosing an asymptotic fouling function. Two main parameters in the asymptotic fouling formation model, i.e. the decay time of fouling formation ({tau}) and the asymptotic fouling resistance (R{sub f}{sup {approx}}) were obtained from empirical data as input parameters to the simulation relations. These data were extracted from the technical history sheets of the Khorasan Petrochemical Plant to guaranty the consistency between our model outputs and the real operating conditions. The output results of the software program developed, including the variations with time of the outlet temperatures of the hot and cold streams, the heat transfer coefficient and the heat transfer rate in the exchangers, are presented for two case studies. Then, an objective function (operational cost) was defined, and the optimal cleaning schedule of the HEN (heat exchanger network) in the Urea and Ammonia units were found by minimizing the objective function using a numerical search method. Based on this minimization procedure, the decision was made whether a heat exchanger should be cleaned or continue to operate. The final result was the most cost effective plan for the HEN cleaning schedule. The

  9. Price for the quality of the electric power network

    International Nuclear Information System (INIS)

    Baarsma, B.E.; Berkhout, P.H.G.; Hop, J.P.; Van Gemert, M.

    2004-01-01

    Power failures cause societal costs. Therefore, it is important that in the decision making process with regard to investments network managers take into account not only private costs and benefits, but also societal benefits of their investments. The benefits can be quantified by means of the so-called conjoint analysis and compared with the contingent valuation method (CVM). The article is followed by a reaction of employees of the Dutch Office of Energy Regulation (DTe) [nl

  10. Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

    Science.gov (United States)

    Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

    The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

  11. Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

    International Nuclear Information System (INIS)

    Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.

    2015-01-01

    Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

  12. Energy-aware scheduling of surveillance in wireless multimedia sensor networks.

    Science.gov (United States)

    Wang, Xue; Wang, Sheng; Ma, Junjie; Sun, Xinyao

    2010-01-01

    Wireless sensor networks involve a large number of sensor nodes with limited energy supply, which impacts the behavior of their application. In wireless multimedia sensor networks, sensor nodes are equipped with audio and visual information collection modules. Multimedia contents are ubiquitously retrieved in surveillance applications. To solve the energy problems during target surveillance with wireless multimedia sensor networks, an energy-aware sensor scheduling method is proposed in this paper. Sensor nodes which acquire acoustic signals are deployed randomly in the sensing fields. Target localization is based on the signal energy feature provided by multiple sensor nodes, employing particle swarm optimization (PSO). During the target surveillance procedure, sensor nodes are adaptively grouped in a totally distributed manner. Specially, the target motion information is extracted by a forecasting algorithm, which is based on the hidden Markov model (HMM). The forecasting results are utilized to awaken sensor node in the vicinity of future target position. According to the two properties, signal energy feature and residual energy, the sensor nodes decide whether to participate in target detection separately with a fuzzy control approach. Meanwhile, the local routing scheme of data transmission towards the observer is discussed. Experimental results demonstrate the efficiency of energy-aware scheduling of surveillance in wireless multimedia sensor network, where significant energy saving is achieved by the sensor awakening approach and data transmission paths are calculated with low computational complexity.

  13. Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

    Directory of Open Access Journals (Sweden)

    Xiangming Yao

    2017-01-01

    Full Text Available The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.

  14. Variable Scheduling to Mitigate Channel Losses in Energy-Efficient Body Area Networks

    Directory of Open Access Journals (Sweden)

    Lavy Libman

    2012-11-01

    Full Text Available We consider a typical body area network (BAN setting in which sensor nodes send data to a common hub regularly on a TDMA basis, as defined by the emerging IEEE 802.15.6 BAN standard. To reduce transmission losses caused by the highly dynamic nature of the wireless channel around the human body, we explore variable TDMA scheduling techniques that allow the order of transmissions within each TDMA round to be decided on the fly, rather than being fixed in advance. Using a simple Markov model of the wireless links, we devise a number of scheduling algorithms that can be performed by the hub, which aim to maximize the expected number of successful transmissions in a TDMA round, and thereby significantly reduce transmission losses as compared with a static TDMA schedule. Importantly, these algorithms do not require a priori knowledge of the statistical properties of the wireless channels, and the reliability improvement is achieved entirely via shuffling the order of transmissions among devices, and does not involve any additional energy consumption (e.g., retransmissions. We evaluate these algorithms directly on an experimental set of traces obtained from devices strapped to human subjects performing regular daily activities, and confirm that the benefits of the proposed variable scheduling algorithms extend to this practical setup as well.

  15. Security-Reliability Trade-Off Analysis for Multiuser SIMO Mixed RF/FSO Relay Networks With Opportunistic User Scheduling

    KAUST Repository

    El-Malek, Ahmed H. Abd; Salhab, Anas M.; Zummo, Salam A.; Alouini, Mohamed-Slim

    2016-01-01

    In this paper, we study the performance of multiuser single-input multiple-output mixed radio frequency (RF)/free space optical (FSO) relay network with opportunistic user scheduling. The considered system includes multiple users, one amplify

  16. Day-ahead price forecasting in restructured power systems using artificial neural networks

    International Nuclear Information System (INIS)

    Vahidinasab, V.; Jadid, S.; Kazemi, A.

    2008-01-01

    Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg-Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania-New Jersey-Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. (author)

  17. Improved merit order and augmented Lagrange Hopfield network for short term hydrothermal scheduling

    International Nuclear Information System (INIS)

    Vo Ngoc Dieu; Ongsakul, Weerakorn

    2009-01-01

    This paper proposes an improved merit order (IMO) combined with an augmented Lagrangian Hopfield network (ALHN) for solving short term hydrothermal scheduling (HTS) with pumped-storage hydro plants. The proposed IMO-ALHN consists of a merit order based on the average production cost of generating units enhanced by heuristic search algorithm for finding unit scheduling and a continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation for solving constrained economic dispatch (CED). The proposed method is applied to solve the HTS problem in five stages including thermal, hydro and pumped-storage unit commitment by IMO and heuristic search, constraint violations repairing by heuristic search and CED by ALHN. The proposed method is tested on the 24-bus IEEE RTS with 32 units including 4 fuel-constrained, 4-hydro, and 2 pumped-storage units scheduled over a 24-h period. Test results indicate that the proposed IMO-ALHN is efficient for hydrothermal systems with various constraints.

  18. A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks

    OpenAIRE

    Shi, Binbin; Wei, Wei; Wang, Yihuai; Shu, Wanneng

    2016-01-01

    In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS) based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical d...

  19. Energy Efficient Routing and Node Activity Scheduling in the OCARI Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Saoucene Mahfoudh

    2010-08-01

    Full Text Available Sensor nodes are characterized by a small size, a low cost, an advanced communication technology, but also a limited amount of energy. Energy efficient strategies are required in such networks to maximize network lifetime. In this paper, we focus on a solution integrating energy efficient routing and node activity scheduling. The energy efficient routing we propose, called EOLSR, selects the route and minimizes the energy consumed by an end-to-end transmission, while avoiding nodes with low residual energy. Simulation results show that EOLSR outperforms the solution selecting the route of minimum energy as well as the solution based on node residual energy. Cross-layering allows EOLSR to use information from the application layer or the MAC layer to reduce its overhead and increase network lifetime. Node activity scheduling is based on the following observation: the sleep state is the least power consuming state. So, to schedule node active and sleeping periods, we propose SERENA that colors all network nodes using a small number of colors, such that two nodes with the same color can transmit without interfering. The node color is mapped into a time slot during which the node can transmit. Consequently, each node is awake during its slot and the slots of its one-hop neighbors, and sleeps in the remaining time. We evaluate SERENA benefits obtained in terms of bandwidth, delay and energy. We also show how cross-layering with the application layer can improve the end-to-end delays for data gathering applications.

  20. The Price of Anarchy in Network Creation Games Is (Mostly) Constant

    Science.gov (United States)

    Mihalák, Matúš; Schlegel, Jan Christoph

    We study the price of anarchy and the structure of equilibria in network creation games. A network creation game (first defined and studied by Fabrikant et al. [4]) is played by n players {1,2,...,n}, each identified with a vertex of a graph (network), where the strategy of player i, i = 1,...,n, is to build some edges adjacent to i. The cost of building an edge is α> 0, a fixed parameter of the game. The goal of every player is to minimize its creation cost plus its usage cost. The creation cost of player i is α times the number of built edges. In the SumGame (the original variant of Fabrikant et al. [4]) the usage cost of player i is the sum of distances from i to every node of the resulting graph. In the MaxGame (variant defined and studied by Demaine et al. [3]) the usage cost is the eccentricity of i in the resulting graph of the game. In this paper we improve previously known bounds on the price of anarchy of the game (of both variants) for various ranges of α, and give new insights into the structure of equilibria for various values of α. The two main results of the paper show that for α > 273·n all equilibria in SumGame are trees and thus the price of anarchy is constant, and that for α> 129 all equilibria in MaxGame are trees and the price of anarchy is constant. For SumGame this (almost) answers one of the basic open problems in the field - is price of anarchy of the network creation game constant for all values of α? - in an affirmative way, up to a tiny range of α.

  1. 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...

  2. Distributed Sleep Scheduling in Wireless Sensor Networks via Fractional Domatic Partitioning

    Science.gov (United States)

    Schumacher, André; Haanpää, Harri

    We consider setting up sleep scheduling in sensor networks. We formulate the problem as an instance of the fractional domatic partition problem and obtain a distributed approximation algorithm by applying linear programming approximation techniques. Our algorithm is an application of the Garg-Könemann (GK) scheme that requires solving an instance of the minimum weight dominating set (MWDS) problem as a subroutine. Our two main contributions are a distributed implementation of the GK scheme for the sleep-scheduling problem and a novel asynchronous distributed algorithm for approximating MWDS based on a primal-dual analysis of Chvátal's set-cover algorithm. We evaluate our algorithm with ns2 simulations.

  3. Real-time distributed scheduling algorithm for supporting QoS over WDM networks

    Science.gov (United States)

    Kam, Anthony C.; Siu, Kai-Yeung

    1998-10-01

    Most existing or proposed WDM networks employ circuit switching, typically with one session having exclusive use of one entire wavelength. Consequently they are not suitable for data applications involving bursty traffic patterns. The MIT AON Consortium has developed an all-optical LAN/MAN testbed which provides time-slotted WDM service and employs fast-tunable transceivers in each optical terminal. In this paper, we explore extensions of this service to achieve fine-grained statistical multiplexing with different virtual circuits time-sharing the wavelengths in a fair manner. In particular, we develop a real-time distributed protocol for best-effort traffic over this time-slotted WDM service with near-optical fairness and throughput characteristics. As an additional design feature, our protocol supports the allocation of guaranteed bandwidths to selected connections. This feature acts as a first step towards supporting integrated services and quality-of-service guarantees over WDM networks. To achieve high throughput, our approach is based on scheduling transmissions, as opposed to collision- based schemes. Our distributed protocol involves one MAN scheduler and several LAN schedulers (one per LAN) in a master-slave arrangement. Because of propagation delays and limits on control channel capacities, all schedulers are designed to work with partial, delayed traffic information. Our distributed protocol is of the `greedy' type to ensure fast execution in real-time in response to dynamic traffic changes. It employs a hybrid form of rate and credit control for resource allocation. We have performed extensive simulations, which show that our protocol allocates resources (transmitters, receivers, wavelengths) fairly with high throughput, and supports bandwidth guarantees.

  4. TRADING-OFF CONSTRAINTS IN THE PUMP SCHEDULING OPTIMIZATION OF WATER DISTRIBUTION NETWORKS

    Directory of Open Access Journals (Sweden)

    Gencer Genço\\u011Flu

    2016-01-01

    Full Text Available Pumps are one of the essential components of water supply systems. Depending of the topography, a water supply system may completely rely on pumping. They may consume non-negligible amount of water authorities' budgets during operation. Besides their energy costs, maintaining the healthiness of pumping systems is another concern for authorities. This study represents a multi-objective optimization method for pump scheduling problem. The optimization objective contains hydraulic and operational constraints. Switching of pumps and usage of electricity tariff are assumed to be key factors for operational reliability and energy consumption and costs of pumping systems. The local optimals for systems operational reliability, energy consumptions and energy costs are investigated resulting from trading-off pump switch and electricity tariff constraints within given set of boundary conditions. In the study, a custom made program is employed that combines genetic algorithm based optimization module with hydraulic network simulation software -EPANET. Developed method is applied on the case study network; N8-3 pressure zone of the Northern Supply of Ankara (Turkey Water Distribution Network. This work offers an efficient method for water authorities aiming to optimize pumping schedules considering expenditures and operational reliability mutually.

  5. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Science.gov (United States)

    Jin, Junghwan; Kim, Jinsoo

    2015-01-01

    Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  6. Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

    Directory of Open Access Journals (Sweden)

    Mohammad Fathian

    2012-04-01

    Full Text Available In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

  7. A Game Theoretic Framework for Bandwidth Allocation and Pricing in Federated Wireless Networks

    Science.gov (United States)

    Gu, Bo; Yamori, Kyoko; Xu, Sugang; Tanaka, Yoshiaki

    With the proliferation of IEEE 802.11 wireless local area networks, large numbers of wireless access points have been deployed, and it is often the case that a user can detect several access points simultaneously in dense metropolitan areas. Most owners, however, encrypt their networks to prevent the public from accessing them due to the increased traffic and security risk. In this work, we use pricing as an incentive mechanism to motivate the owners to share their networks with the public, while at the same time satisfying users' service demand. Specifically, we propose a “federated network” concept, in which radio resources of various wireless local area networks are managed together. Our algorithm identifies two candidate access points with the lowest price being offered (if available) to each user. We then model the price announcements of access points as a game, and characterize the Nash Equilibrium of the system. The efficiency of the Nash Equilibrium solution is evaluated via simulation studies as well.

  8. Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

    KAUST Repository

    Douik, Ahmed

    2016-03-28

    In the context of resource allocation in cloud- radio access networks, recent studies assume either signal-level or scheduling-level coordination. This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio- access network, so as to benefit from both scheduling policies. Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them. Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication. The frame structure of every BS is composed of various time/frequency blocks, called power- zones (PZs), and kept at fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs\\' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The scheduling problem is, then, shown to be equivalent to a maximum- weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association. Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

  9. Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network

    OpenAIRE

    Gezaq Abror; Rusminto Tjatur Widodo; M. Udin Harun Al Rasyid

    2018-01-01

    Wireless Sensor Network (WSN) can be applied for Air Pollution Level Monitoring System that have been determined by the Environmental Impact Management Agency which is  PM10, SO2, O3, NO2 and CO. In WSN, node system is constrained to a limited power supply, so that the node system has a lifetime. To doing lifetime maximization, power management scheme is required and sensor nodes should use energy efficiently. This paper proposes dynamic sleep scheduling using Time Category-Fuzzy Logic (Time-...

  10. Quantifying immediate price impact of trades based on the k-shell decomposition of stock trading networks

    Science.gov (United States)

    Xie, Wen-Jie; Li, Ming-Xia; Xu, Hai-Chuan; Chen, Wei; Zhou, Wei-Xing; Stanley, H. Eugene

    2016-10-01

    Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into k classes using the k-shell decomposition method. We investigate the influences of trading behaviors on the price impact by comparing a closed national market (A-shares) with an international market (B-shares), individuals and institutions, partially filled and filled trades, buyer-initiated and seller-initiated trades, and trades at different positions of a trading network. Institutional traders professionally use some trading strategies to reduce the price impact and individuals at the same positions in the trading network have a higher price impact than institutions. We also find that trades in the core have higher price impacts than those in the peripheral shell.

  11. A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES

    Directory of Open Access Journals (Sweden)

    M. Seidi

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT:Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. Remanufacturing is an industrial process that makes used products reusable. One of the important aspects in both reverse logistics and remanufacturing is the pricing of returned and remanufactured products (called cores. In this paper, we focus on pricing the cores and remanufactured cores. First we present a mathematical model for this purpose. Since this model does not satisfy our requirements, we propose a simulation optimisation approach. This approach consists of a hybrid genetic algorithm based on a neural network employed as the fitness function. We use automata learning theory to obtain the learning rate required for training the neural network. Numerical results demonstrate that the optimal value of the acquisition price of cores and price of remanufactured cores is obtained by this approach.

    AFRIKAANSE OPSOMMING: Volhoubaarheid het ‘n belangrike saak geword in die meeste ekonomieë, wat verskeie maatskappye genoop het om produkherwinning en omgekeerde logistiek te onder oë te neem. Hervervaardiging is ‘n industriële proses wat gebruikte produkte weer bruikbaar maak. Een van die belangrike aspekte in beide omgekeerde logistiek en hervervaardiging is die prysbepaling van herwinne en hervervaardigde produkte. Hierdie artikel fokus op die prysbepalingsaspekte by wyse van ‘n wiskundige model.

  12. Optimal Pricing and Power Allocation for Collaborative Jamming with Full Channel Knowledge in Wireless Sensor Networks.

    Science.gov (United States)

    Jeong, Dae-Kyo; Kim, Insook; Kim, Dongwoo

    2017-11-22

    This paper presents a price-searching model in which a source node (Alice) seeks friendly jammers that prevent eavesdroppers (Eves) from snooping legitimate communications by generating interference or noise. Unlike existing models, the distributed jammers also have data to send to their respective destinations and are allowed to access Alice's channel if it can transmit sufficient jamming power, which is referred to as collaborative jamming in this paper. For the power used to deliver its own signal, the jammer should pay Alice. The price of the jammers' signal power is set by Alice and provides a tradeoff between the signal and the jamming power. This paper presents, in closed-form, an optimal price that maximizes Alice's benefit and the corresponding optimal power allocation from a jammers' perspective by assuming that the network-wide channel knowledge is shared by Alice and jammers. For a multiple-jammer scenario where Alice hardly has the channel knowledge, this paper provides a distributed and interactive price-searching procedure that geometrically converges to an optimal price and shows that Alice by a greedy selection policy achieves certain diversity gain, which increases log-linearly as the number of (potential) jammers grows. Various numerical examples are presented to illustrate the behavior of the proposed model.

  13. Optimal Pricing and Power Allocation for Collaborative Jamming with Full Channel Knowledge in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Dae-Kyo Jeong

    2017-11-01

    Full Text Available This paper presents a price-searching model in which a source node (Alice seeks friendly jammers that prevent eavesdroppers (Eves from snooping legitimate communications by generating interference or noise. Unlike existing models, the distributed jammers also have data to send to their respective destinations and are allowed to access Alice’s channel if it can transmit sufficient jamming power, which is referred to as collaborative jamming in this paper. For the power used to deliver its own signal, the jammer should pay Alice. The price of the jammers’ signal power is set by Alice and provides a tradeoff between the signal and the jamming power. This paper presents, in closed-form, an optimal price that maximizes Alice’s benefit and the corresponding optimal power allocation from a jammers’ perspective by assuming that the network-wide channel knowledge is shared by Alice and jammers. For a multiple-jammer scenario where Alice hardly has the channel knowledge, this paper provides a distributed and interactive price-searching procedure that geometrically converges to an optimal price and shows that Alice by a greedy selection policy achieves certain diversity gain, which increases log-linearly as the number of (potential jammers grows. Various numerical examples are presented to illustrate the behavior of the proposed model.

  14. Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction

    Directory of Open Access Journals (Sweden)

    Magdalena Daniela NEMES

    2013-01-01

    Full Text Available Predicting future prices by using time series forecasting models has become a relevant trading strategy for most stock market players. Intuition and speculation are no longer reliable as many new trading strategies based on artificial intelligence emerge. Data mining represents a good source of information, as it ensures data processing in a convenient manner. Neural networks are considered useful prediction models when designing forecasting strategies. In this paper we present a series of neural networks designed for stock exchange rates forecasting applied on three Romanian stocks traded on the Bucharest Stock Exchange (BSE. A multistep ahead strategy was used in order to predict short-time price fluctuations. Later, the findings of our study can be integrated with an intelligent multi-agent system model which uses data mining and data stream processing techniques for helping users in the decision making process of buying or selling stocks.

  15. Integrated forward/reverse logistics network design under uncertainty with pricing for collection of used products

    DEFF Research Database (Denmark)

    Fattahi, Mohammad; Govindan, Kannan

    2017-01-01

    This paper addresses design and planning of an integrated forward/reverse logistics network over a planning horizon with multiple tactical periods. In the network, demand for new products and potential return of used products are stochastic. Furthermore, collection amounts of used products...... with different quality levels are assumed dependent on offered acquisition prices to customer zones. A uniform distribution function defines the expected price of each customer zone for one unit of each used product. Using two-stage stochastic programming, a mixed-integer linear programming model is proposed....... To cope with demand and potential return uncertainty, Latin Hypercube Sampling method is applied to generate fan of scenarios and then, backward scenario reduction technique is used to reduce the number of scenarios. Due to the problem complexity, a novel simulation-based simulated annealing algorithm...

  16. Joint optimization scheduling for water conservancy projects in complex river networks

    Directory of Open Access Journals (Sweden)

    Qin Liu

    2017-01-01

    Full Text Available In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

  17. Effective preemptive scheduling scheme for optical burst-switched networks with cascaded wavelength conversion consideration

    Science.gov (United States)

    Gao, Xingbo

    2010-03-01

    We introduce a new preemptive scheduling technique for next-generation optical burst switching (OBS) networks considering the impact of cascaded wavelength conversions. It has been shown that when optical bursts are transmitted all optically from source to destination, each wavelength conversion performed along the lightpath may cause certain signal-to-noise deterioration. If the distortion of the signal quality becomes significant enough, the receiver would not be able to recover the original data. Accordingly, subject to this practical impediment, we improve a recently proposed fair channel scheduling algorithm to deal with the fairness problem and aim at burst loss reduction simultaneously in OBS environments. In our scheme, the dynamic priority associated with each burst is based on a constraint threshold and the number of already conducted wavelength conversions among other factors for this burst. When contention occurs, a new arriving superior burst may preempt another scheduled one according to their priorities. Extensive simulation results have shown that the proposed scheme further improves fairness and achieves burst loss reduction as well.

  18. Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

    KAUST Repository

    Celik, Abdulkadir

    2016-09-12

    In this paper, we consider heterogeneous cognitive radio networks (CRNs) comprising primary channels (PCs) with heterogeneous characteristics and secondary users (SUs) with various sensing and reporting qualities for different PCs. We first define the opportunity as the achievable total data rate and its cost as the energy consumption caused from sensing, reporting, and channel switching operations and formulate a joint spectrum discovery and energy efficiency objective to minimize the energy spent per unit of data rate. Then, a mixed integer nonlinear programming problem is formulated to determine: 1) the optimal subset of PCs to be scheduled for sensing; 2) the SU assignment set for each scheduled PC; and 3) sensing durations and detection thresholds of each SU on PCs it is assigned to sense. Thereafter, an equivalent convex framework is developed for specific instances of the above combinatorial problem. For comparison, optimal detection and sensing thresholds are also derived analytically under the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy, and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and is shown to perform very close to the exhaustive optimal solution. Finally, the behavior of the CRN is numerically analyzed under these regimes with respect to different numbers of SUs, PCs, and sensing qualities.

  19. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks.

    Science.gov (United States)

    Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

    2017-09-15

    Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller's direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20-40% while ensuring feasible data delay.

  20. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yunkai Wei

    2017-09-01

    Full Text Available Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs are an inexorable trend for Wireless Sensor Networks (WSNs, including Wireless Rechargeable Sensor Network (WRSNs. However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN controller’s direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20–40% while ensuring feasible data delay.

  1. Agent-Based Model of Price Competition and Product Differentiation on Congested Networks

    OpenAIRE

    Lei Zhang; David Levinson; Shanjiang Zhu

    2007-01-01

    Using consistent agent-based techniques, this research models the decision-making processes of users and infrastructure owner/operators to explore the welfare consequence of price competition, capacity choice, and product differentiation on congested transportation networks. Component models include: (1) An agent-based travel demand model wherein each traveler has learning capabilities and unique characteristics (e.g. value of time); (2) Econometric facility provision cost models; and (3) Rep...

  2. Risks, prices, and positions: A social network analysis of illegal drug trafficking in the world-economy.

    Science.gov (United States)

    Boivin, Rémi

    2014-03-01

    Illegal drug prices are extremely high, compared to similar goods. There is, however, considerable variation in value depending on place, market level and type of drugs. A prominent framework for the study of illegal drugs is the "risks and prices" model (Reuter & Kleiman, 1986). Enforcement is seen as a "tax" added to the regular price. In this paper, it is argued that such economic models are not sufficient to explain price variations at country-level. Drug markets are analysed as global trade networks in which a country's position has an impact on various features, including illegal drug prices. This paper uses social network analysis (SNA) to explain price markups between pairs of countries involved in the trafficking of illegal drugs between 1998 and 2007. It aims to explore a simple question: why do prices increase between two countries? Using relational data from various international organizations, separate trade networks were built for cocaine, heroin and cannabis. Wholesale price markups are predicted with measures of supply, demand, risks of seizures, geographic distance and global positioning within the networks. Reported prices (in $US) and purchasing power parity-adjusted values are analysed. Drug prices increase more sharply when drugs are headed to countries where law enforcement imposes higher costs on traffickers. The position and role of a country in global drug markets are also closely associated with the value of drugs. Price markups are lower if the destination country is a transit to large potential markets. Furthermore, price markups for cocaine and heroin are more pronounced when drugs are exported to countries that are better positioned in the legitimate world-economy, suggesting that relations in legal and illegal markets are directed in opposite directions. Consistent with the world-system perspective, evidence is found of coherent world drug markets driven by both local realities and international relations. Copyright © 2013 Elsevier B

  3. Sleep-time sizing and scheduling in green passive optical networks

    KAUST Repository

    Elrasad, Amr

    2012-08-01

    Next-generation passive optical network (PON) has been widely considered as a cost-effective broadband access technology. With the ever-increasing power saving concern, energy efficiency has been an important issue for its operations. In this paper, we present a novel sleep time sizing and scheduling framework that satisfies power efficient bandwidth allocation in PONs. We consider the downstream links from an optical line terminal (OLT) to an optical network unit (ONU). The ONU has two classes of traffic, control and data. Control traffic are delay intolerant with higher priority than the data traffic. Closed form model for average ONU sleeping time and end-to-end data traffic delay are presented and evaluated. Our framework decouples the dependency between ONU sleeping time and the QoS of the traffic.

  4. Pricing of embedded generation: Incorporation of externalities and avoided network losses

    International Nuclear Information System (INIS)

    Rodrigo, Asanka S.; Wijayatunga, Priyantha D.C.

    2007-01-01

    Traditionally, the electricity purchase tariff of embedded generators reflected only the cost of production and delivery of electricity to the consumers, which includes the costs of labor, capital, operation, taxes and insurance. However, the production of electricity causes adverse impacts on the environment. At present, this issue has not been widely addressed by the existing pricing methodologies. This paper proposes a pricing methodology for renewable energy based embedded electricity generation, incorporating the cost of externalities with a case study on the Sri Lanka power system. It recommends that the embedded generation tariff be based on the principle of 'avoided cost', considering the cost of energy production, cost of externalities and the cost of network losses. While the 'impact path way' approach is proposed for calculation of the cost of externalities of energy, the nodal-based cost calculation is proposed for the avoided cost of network losses calculation. The pricing methodology proposed in the paper provides important information for investors when choosing the most economical site for their development. It can also be used to optimize the network use. These will allow the developers of embedded generation facilities and the utilities operating the national grid to maximize the potential of embedded generation. (author)

  5. Enhanced capital-asset pricing model for the reconstruction of bipartite financial networks

    Science.gov (United States)

    Squartini, Tiziano; Almog, Assaf; Caldarelli, Guido; van Lelyveld, Iman; Garlaschelli, Diego; Cimini, Giulio

    2017-09-01

    Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets are strongly affected by the interconnections among financial institutions. Yet, while the aggregate balance sheets of institutions are publicly disclosed, information on single positions is mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of systemic risk. Moreover, reconstruction techniques are generally designed for monopartite networks of bilateral exposures between financial institutions, thus failing in reproducing bipartite networks of security holdings (e.g., investment portfolios). Here we propose a reconstruction method based on constrained entropy maximization, tailored for bipartite financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows us to reproduce the correct topology of the network. We test this enhanced CAPM (ECAPM) method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009-2015). Our approach outperforms the traditional CAPM and the recently proposed maximum-entropy CAPM both in reproducing the network topology and in estimating systemic risk due to fire sales spillovers. In general, ECAPM can be applied to the whole class of weighted bipartite networks described by the fitness model.

  6. Threshold Based Opportunistic Scheduling of Secondary Users in Underlay Cognitive Radio Networks

    KAUST Repository

    Song, Yao

    2011-12-01

    In underlay cognitive radio networks, secondary users can share the spectrum with primary users as long as the interference caused by the secondary users to primary users is below a certain predetermined threshold. It is reasonable to assume that there is always a large pool of secondary users trying to access the channel, which can be occupied by only one secondary user at a given time. As a result, a multi-user scheduling problem arises among the secondary users. In this thesis, by manipulating basic schemes based on selective multi-user diversity, normalized thresholding, transmission power control, and opportunistic round robin, we propose and analyze eight scheduling schemes of secondary users in an underlay cognitive radio set-up. The system performance of these schemes is quantified by using various performance metrics such as the average system capacity, normalized average feedback load, scheduling outage probability, and system fairness of access. In our proposed schemes, the best user out of all the secondary users in the system is picked to transmit at each given time slot in order to maximize the average system capacity. Two thresholds are used in the two rounds of the selection process to determine the best user. The first threshold is raised by the power constraint from the primary user. The second threshold, which can be adjusted by us, is introduced to reduce the feedback load. The overall system performance is therefore dependent on the choice of these two thresholds and the number of users in the system given the channel conditions for all the users. In this thesis, by deriving analytical formulas and presenting numerical examples, we try to provide insights of the relationship between the performance metrics and the involved parameters including two selection thresholds and the number of active users in the system, in an effort to maximize the average system capacity as well as satisfy the requirements of scheduling outage probability and

  7. Competitive closed-loop supply chain network design with price-dependent demands

    DEFF Research Database (Denmark)

    Rezapour, Shabnam; Farahani, Reza Zanjirani; Fahimnia, Behnam

    2015-01-01

    Abstract This paper presents a bi-level model for the strategic reverse network design (upper level) and tactical/operational planning (lower level) of a closed-loop single-period supply chain operating in a competitive environment with price-dependent market demand. An existing supply chain (SC...... for the supply of new and remanufactured products. The performance behaviors of both SCs are evaluated with specific focus placed on investigating the impacts of the strategic facility location decisions of the new SC on the tactical/operational transport and inventory decisions of the overall network. The bi...

  8. An adaptive random search for short term generation scheduling with network constraints.

    Directory of Open Access Journals (Sweden)

    J A Marmolejo

    Full Text Available This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.

  9. Low-Complexity Scheduling and Power Adaptation for Coordinated Cloud-Radio Access Networks

    KAUST Repository

    Douik, Ahmed

    2017-07-17

    In practical wireless systems, the successful implementation of resource allocation techniques strongly depends on the algorithmic complexity. Consider a cloud-radio access network (CRAN), where the central cloud is responsible for scheduling devices to the frames’ radio resources blocks (RRBs) of the single-antenna base-stations (BSs), adjusting the transmit power levels, and for synchronizing the transmit frames across the connected BSs. Previous studies show that the jointly coordinated scheduling and power control problem in the considered CRAN can be solved using an approach that scales exponentially with the number of BSs, devices, and RRBs, which makes the practical implementation infeasible for reasonably sized networks. This paper instead proposes a low-complexity solution to the problem, under the constraints that each device cannot be served by more than one BS but can be served by multiple RRBs within each BS frame, and under the practical assumption that the channel is constant during the duration of each frame. The paper utilizes graph-theoretical based techniques and shows that constructing a single power control graph is sufficient to obtain the optimal solution with a complexity that is independent of the number of RRBs. Simulation results reveal the optimality of the proposed solution for slow-varying channels, and show that the solution performs near-optimal for highly correlated channels.

  10. Price-based Energy Control for V2G Networks in the Industrial Smart Grid

    Directory of Open Access Journals (Sweden)

    Rong Yu

    2015-08-01

    Full Text Available The energy crisis and global warming call for a new industrial revolution in production and distribution of renewable energy. Distributed power generation will be well developed in the new smart electricity distribution grid, in which robust power distribution will be the key technology. In this paper, we present a new vehicle-to-grid (V2G network for energy transfer, in which distributed renewable energy helps the power grid balance demand and supply. Plug-in hybrid electric vehicles (PHEVs will act as transporters of electricity for distributed renewable energy dispatching. We formulate and analyze the V2G network within the theoretical framework of complex network. We also employ the generalized synchronization method to study the dynamic behavior of V2G networks. Furthermore, we develop a new price-based energy control method to stimulate the PHEV's behavior of charging and discharging. Simulation results indicate that the V2G network can achieve synchronization and each region is able to balance energy supply and demand through price-based control.

  11. Multi-operator collaboration for green cellular networks under roaming price consideration

    KAUST Repository

    Ghazzai, Hakim; Yaacoub, Elias E.; Alouini, Mohamed-Slim

    2014-01-01

    This paper investigates the collaboration between multiple mobile operators to optimize the energy efficiency of cellular networks. Our framework studies the case of LTE-Advanced networks deployed in the same area and owning renewable energy generators. The objective is to reduce the CO2 emissions of cellular networks via collaborative techniques and using base station sleeping strategy while respecting the network quality of service. Low complexity and practical algorithm is employed to achieve green goals during low traffic periods. Cooperation decision criteria are also established basing on derived roaming prices and profit gains of competitive mobile operators. Our numerical results show a significant save in terms of CO2 compared to the non-collaboration case and that cooperative mobile operator exploiting renewables are more awarded than traditional operators.

  12. Multi-operator collaboration for green cellular networks under roaming price consideration

    KAUST Repository

    Ghazzai, Hakim

    2014-09-01

    This paper investigates the collaboration between multiple mobile operators to optimize the energy efficiency of cellular networks. Our framework studies the case of LTE-Advanced networks deployed in the same area and owning renewable energy generators. The objective is to reduce the CO2 emissions of cellular networks via collaborative techniques and using base station sleeping strategy while respecting the network quality of service. Low complexity and practical algorithm is employed to achieve green goals during low traffic periods. Cooperation decision criteria are also established basing on derived roaming prices and profit gains of competitive mobile operators. Our numerical results show a significant save in terms of CO2 compared to the non-collaboration case and that cooperative mobile operator exploiting renewables are more awarded than traditional operators.

  13. An Enhanced Feedback-Base Downlink Packet Scheduling Algorithm for Mobile TV in WIMAX Networks

    Directory of Open Access Journals (Sweden)

    Joseph Oyewale

    2013-06-01

    Full Text Available With high speed access network technology like WIMAX, there is the need for efficient management of radio resources where the throughput and Qos requirements for Multicasting Broadcasting Services (MBS for example TV are to be met. An enhanced  feedback-base downlink Packet scheduling algorithm  that can be used in IEEE 802.16d/e networks for mobile TV “one way traffic”(MBS is needed to support many users utilizing multiuser diversity of the  broadband of WIMAX systems where a group of users(good/worst channels share allocated resources (bandwidth. This paper proposes a WIMAX framework feedback-base (like a channel-awareness downlink packet scheduling algorithm for Mobile TV traffics in IEEE806.16, in which network Physical Timing Slots (PSs resource blocks are allocated in a dynamic way to mobile TV subscribers based on the Channel State information (CSI feedback, and then considering users with worst channels with the aim of improving system throughput while system coverage is being guaranteed. The algorithm was examined by changing the PSs bandwidth allocation of the users and different number of users of a cell. Simulation results show our proposed algorithm performed better than other algorithms (blind algorithms in terms of improvement in system throughput performance. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso

  14. Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network

    Directory of Open Access Journals (Sweden)

    G. Kannan

    2015-07-01

    Full Text Available Wireless Sensor Network (WSN provides a significant contribution in the emerging fields such as ambient intelligence and ubiquitous computing. In WSN, optimization and load balancing of network resources are critical concern to provide the intelligence for long duration. Since clustering the sensor nodes can significantly enhance overall system scalability and energy efficiency this paper presents a distributed cluster head scheduling (DCHS algorithm to achieve the network longevity in WSN. The major novelty of this work is that the network is divided into primary and secondary tiers based on received signal strength indication of sensor nodes from the base station. The proposed DCHS supports for two tier WSN architecture and gives suggestion to elect the cluster head nodes and gateway nodes for both primary and secondary tiers. The DCHS mechanism satisfies an ideal distribution of the cluster head among the sensor nodes and avoids frequent selection of cluster head, based on Received Signal Strength Indication (RSSI and residual energy level of the sensor nodes. Since the RSSI is the key parameter for this paper, the practical experiment was conducted to measure RSSI value by using MSP430F149 processor and CC2500 transceiver. The measured RSSI values were given input to the event based simulator to test the DCHS mechanism. The real time experimental study validated the proposed scheme for various scenarios.

  15. Dynamics of global supply chain and electric power networks: Models, pricing analysis, and computations

    Science.gov (United States)

    Matsypura, Dmytro

    In this dissertation, I develop a new theoretical framework for the modeling, pricing analysis, and computation of solutions to electric power supply chains with power generators, suppliers, transmission service providers, and the inclusion of consumer demands. In particular, I advocate the application of finite-dimensional variational inequality theory, projected dynamical systems theory, game theory, network theory, and other tools that have been recently proposed for the modeling and analysis of supply chain networks (cf. Nagurney (2006)) to electric power markets. This dissertation contributes to the extant literature on the modeling, analysis, and solution of supply chain networks, including global supply chains, in general, and electric power supply chains, in particular, in the following ways. It develops a theoretical framework for modeling, pricing analysis, and computation of electric power flows/transactions in electric power systems using the rationale for supply chain analysis. The models developed include both static and dynamic ones. The dissertation also adds a new dimension to the methodology of the theory of projected dynamical systems by proving that, irrespective of the speeds of adjustment, the equilibrium of the system remains the same. Finally, I include alternative fuel suppliers, along with their behavior into the supply chain modeling and analysis framework. This dissertation has strong practical implications. In an era in which technology and globalization, coupled with increasing risk and uncertainty, complicate electricity demand and supply within and between nations, the successful management of electric power systems and pricing become increasingly pressing topics with relevance not only for economic prosperity but also national security. This dissertation addresses such related topics by providing models, pricing tools, and algorithms for decentralized electric power supply chains. This dissertation is based heavily on the following

  16. A Novel Model for Stock Price Prediction Using Hybrid Neural Network

    Science.gov (United States)

    Senapati, Manas Ranjan; Das, Sumanjit; Mishra, Sarojananda

    2018-06-01

    The foremost challenge for investors is to select stock price by analyzing financial data which is a menial task as of distort associated and massive pattern. Thereby, selecting stock poses one of the greatest difficulties for investors. Nowadays, prediction of financial market like stock market, exchange rate and share value are very challenging field of research. The prediction and scrutinization of stock price is also a potential area of research due to its vital significance in decision making by financial investors. This paper presents an intelligent and an optimal model for prophecy of stock market price using hybridization of Adaline Neural Network (ANN) and modified Particle Swarm Optimization (PSO). The connoted model hybrid of Adaline and PSO uses fluctuations of stock market as a factor and employs PSO to optimize and update weights of Adaline representation to depict open price of Bombay stock exchange. The prediction performance of the proposed model is compared with different representations like interval measurements, CMS-PSO and Bayesian-ANN. The result indicates that proposed scheme has an edge over all the juxtaposed schemes in terms of mean absolute percentage error.

  17. Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging.

    Science.gov (United States)

    Gençay, R; Qi, M

    2001-01-01

    We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

  18. Artificial neural networks applied to the prediction of spot prices in the market of electric energy

    International Nuclear Information System (INIS)

    Rodrigues, Alcantaro Lemes; Grimoni, Jose Aquiles Baesso

    2010-01-01

    The commercialization of electricity in Brazil as well as in the world has undergone several changes over the past 20 years. In order to achieve an economic balance between supply and demand of the good called electricity, stakeholders in this market follow both rules set by society (government, companies and consumers) and set by the laws of nature (hydrology). To deal with such complex issues, various studies have been conducted in the area of computational heuristics. This work aims to develop a software to forecast spot market prices in using artificial neural networks (ANN). ANNs are widely used in various applications especially in computational heuristics, where non-linear systems have computational challenges difficult to overcome because of the effect named 'curse of dimensionality'. This effect is due to the fact that the current computational power is not enough to handle problems with such a high combination of variables. The challenge of forecasting prices depends on factors such as: (a) foresee the demand evolution (electric load); (b) the forecast of supply (reservoirs, hydrology and climate), capacity factor; and (c) the balance of the economy (pricing, auctions, foreign markets influence, economic policy, government budget and government policy). These factors are considered be used in the forecasting model for spot market prices and the results of its effectiveness are tested and huge presented. (author)

  19. Exploring a QoS Driven Scheduling Approach for Peer-to-Peer Live Streaming Systems with Network Coding

    Science.gov (United States)

    Cui, Laizhong; Lu, Nan; Chen, Fu

    2014-01-01

    Most large-scale peer-to-peer (P2P) live streaming systems use mesh to organize peers and leverage pull scheduling to transmit packets for providing robustness in dynamic environment. The pull scheduling brings large packet delay. Network coding makes the push scheduling feasible in mesh P2P live streaming and improves the efficiency. However, it may also introduce some extra delays and coding computational overhead. To improve the packet delay, streaming quality, and coding overhead, in this paper are as follows. we propose a QoS driven push scheduling approach. The main contributions of this paper are: (i) We introduce a new network coding method to increase the content diversity and reduce the complexity of scheduling; (ii) we formulate the push scheduling as an optimization problem and transform it to a min-cost flow problem for solving it in polynomial time; (iii) we propose a push scheduling algorithm to reduce the coding overhead and do extensive experiments to validate the effectiveness of our approach. Compared with previous approaches, the simulation results demonstrate that packet delay, continuity index, and coding ratio of our system can be significantly improved, especially in dynamic environments. PMID:25114968

  20. QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach.

    Science.gov (United States)

    Yu, Rong; Zhong, Weifeng; Xie, Shengli; Zhang, Yan; Zhang, Yun

    2016-02-01

    As the next-generation power grid, smart grid will be integrated with a variety of novel communication technologies to support the explosive data traffic and the diverse requirements of quality of service (QoS). Cognitive radio (CR), which has the favorable ability to improve the spectrum utilization, provides an efficient and reliable solution for smart grid communications networks. In this paper, we study the QoS differential scheduling problem in the CR-based smart grid communications networks. The scheduler is responsible for managing the spectrum resources and arranging the data transmissions of smart grid users (SGUs). To guarantee the differential QoS, the SGUs are assigned to have different priorities according to their roles and their current situations in the smart grid. Based on the QoS-aware priority policy, the scheduler adjusts the channels allocation to minimize the transmission delay of SGUs. The entire transmission scheduling problem is formulated as a semi-Markov decision process and solved by the methodology of adaptive dynamic programming. A heuristic dynamic programming (HDP) architecture is established for the scheduling problem. By the online network training, the HDP can learn from the activities of primary users and SGUs, and adjust the scheduling decision to achieve the purpose of transmission delay minimization. Simulation results illustrate that the proposed priority policy ensures the low transmission delay of high priority SGUs. In addition, the emergency data transmission delay is also reduced to a significantly low level, guaranteeing the differential QoS in smart grid.

  1. Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network

    Directory of Open Access Journals (Sweden)

    Changhong Deng

    2016-11-01

    Full Text Available Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network’s dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs’ charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators’ charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs’ charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model.

  2. The Use of Neural Network and Portfolio Analysis in Forecasting Share Prices at the Stock Exchange

    Directory of Open Access Journals (Sweden)

    Przemyslaw Stochel

    2000-01-01

    Full Text Available The article presents the use of neural networks in decision making process on the capital market. The author tried to show the efficiency of established solution in Polish reality which features different conditions in comparison with the markets of higher developed countries. The aim of the paper was to prove that neural networks are flexible tools which on one hand might be adjusted to investor's requirements and on the other, can reduce equirements to his experience. The article is based on the author's own research carried out by modelling neural network operation with a simulation program. The established solutions are input which employs stocks portfolio computed on the basis of Markowitz portfolio theory and Sharpe's model. According to the established propositions, the portfolio created in such a way is modified by neutral network in order to optimise a criterion which maximises the income of such a modified portfolio. A detailed genesis of the established input vector and network structure are presented. It allows the reader to carry out his own research and create his own attitude towards applied values. The research results based on a real stock market database with the use of one-output networks predicting thc price of a single company - Agros as well as networks predicting the desirable structure of the whole portfolio are presented. The effect of the network structure leaming parameters, input vector (not only as to the input quantity but also as to period of time they were collected was examined. The dependence between the factors mentioned above such as input vector and network structure were discussed. lt seems that the presented paper has proved that some not widely spread methods with neural networks can become at competitive tool to optimisation methods.

  3. Modified weighted fair queuing for packet scheduling in mobile WiMAX networks

    Science.gov (United States)

    Satrya, Gandeva B.; Brotoharsono, Tri

    2013-03-01

    The increase of user mobility and the need for data access anytime also increases the interest in broadband wireless access (BWA). The best available quality of experience for mobile data service users are assured for IEEE 802.16e based users. The main problem of assuring a high QOS value is how to allocate available resources among users in order to meet the QOS requirement for criteria such as delay, throughput, packet loss and fairness. There is no specific standard scheduling mechanism stated by IEEE standards, which leaves it for implementer differentiation. There are five QOS service classes defined by IEEE 802.16: Unsolicited Grant Scheme (UGS), Extended Real Time Polling Service (ertPS), Real Time Polling Service (rtPS), Non Real Time Polling Service (nrtPS) and Best Effort Service (BE). Each class has different QOS parameter requirements for throughput and delay/jitter constraints. This paper proposes Modified Weighted Fair Queuing (MWFQ) scheduling scenario which was based on Weighted Round Robin (WRR) and Weighted Fair Queuing (WFQ). The performance of MWFQ was assessed by using above five QoS criteria. The simulation shows that using the concept of total packet size calculation improves the network's performance.

  4. Wake-on-a-Schedule: Energy-aware Communication in Wi-Fi Networks

    Directory of Open Access Journals (Sweden)

    PERKOVIC, T.

    2014-02-01

    Full Text Available Excessive energy consumption of mobile device Wi-Fi (IEEE 802.11x interface is limiting its operational time on batteries, and impacts total energy consumption of electronic devices. In recent years research community has invested great effort in better efficiency of energy consumption. However, there is still a space for improvement. Wi-Fi devices connected to the single AP (Access Point compete for the medium during data exchange. However, due to the performance anomaly in 802.11 networks, a low data rate device will force all other devices connected to the AP to communicate at low rate, which will increase the total energy consumption of these devices. Wake-on-a-Schedule algorithm is proposed reducing the energy consumption of devices placed in the area with the weaker signal by scheduling the data packets for each client on the server side which will not allow clients to compete for the Wi-Fi medium. Through extensive measurements we show that our algorithm can save up to 60% of energy consumption on the client side.

  5. A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks.

    Science.gov (United States)

    Dao, Thi-Nga; Yoon, Seokhoon; Kim, Jangyoung

    2016-01-05

    Many applications in wireless sensor networks (WSNs) require energy consumption to be minimized and the data delivered to the sink within a specific delay. A usual solution for reducing energy consumption is duty cycling, in which nodes periodically switch between sleep and active states. By increasing the duty cycle interval, consumed energy can be reduced more. However, a large duty cycle interval causes a long end-to-end (E2E) packet delay. As a result, the requirement of a specific delay bound for packet delivery may not be satisfied. In this paper, we aim at maximizing the duty cycle while still guaranteeing that the packets arrive at the sink with the required probability, i.e., the required delay-constrained success ratio (DCSR) is achieved. In order to meet this objective, we propose a novel scheduling and forwarding scheme, namely the deadline-aware scheduling and forwarding (DASF) algorithm. In DASF, the E2E delay distribution with the given network model and parameters is estimated in order to determine the maximum duty cycle interval, with which the required DCSR is satisfied. Each node independently selects a wake-up time using the selected interval, and packets are forwarded to a node in the potential forwarding set, which is determined based on the distance between nodes and the sink. DASF does not require time synchronization between nodes, and a node does not need to maintain neighboring node information in advance. Simulation results show that the proposed scheme can satisfy a required delay-constrained success ratio and outperforms existing algorithms in terms of E2E delay and DCSR.

  6. A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Thi-Nga Dao

    2016-01-01

    Full Text Available Many applications in wireless sensor networks (WSNs require energy consumption to be minimized and the data delivered to the sink within a specific delay. A usual solution for reducing energy consumption is duty cycling, in which nodes periodically switch between sleep and active states. By increasing the duty cycle interval, consumed energy can be reduced more. However, a large duty cycle interval causes a long end-to-end (E2E packet delay. As a result, the requirement of a specific delay bound for packet delivery may not be satisfied. In this paper, we aim at maximizing the duty cycle while still guaranteeing that the packets arrive at the sink with the required probability, i.e., the required delay-constrained success ratio (DCSR is achieved. In order to meet this objective, we propose a novel scheduling and forwarding scheme, namely the deadline-aware scheduling and forwarding (DASF algorithm. In DASF, the E2E delay distribution with the given network model and parameters is estimated in order to determine the maximum duty cycle interval, with which the required DCSR is satisfied. Each node independently selects a wake-up time using the selected interval, and packets are forwarded to a node in the potential forwarding set, which is determined based on the distance between nodes and the sink. DASF does not require time synchronization between nodes, and a node does not need to maintain neighboring node information in advance. Simulation results show that the proposed scheme can satisfy a required delay-constrained success ratio and outperforms existing algorithms in terms of E2E delay and DCSR.

  7. Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

    International Nuclear Information System (INIS)

    Wang, Jie; Wang, Jun

    2016-01-01

    In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models. - Highlights: • A new forecasting model is developed by a random Elman recurrent neural network. • The forecasting accuracy of crude oil price fluctuations is improved by the model. • The forecasting results of the proposed model are more accurate than compared models. • Two new distance analysis methods are applied to confirm the predicting results.

  8. APPLYING ARTIFICIAL NEURAL NETWORK OPTIMIZED BY FIREWORKS ALGORITHM FOR STOCK PRICE ESTIMATION

    Directory of Open Access Journals (Sweden)

    Khuat Thanh Tung

    2016-04-01

    Full Text Available Stock prediction is to determine the future value of a company stock dealt on an exchange. It plays a crucial role to raise the profit gained by firms and investors. Over the past few years, many methods have been developed in which plenty of efforts focus on the machine learning framework achieving the promising results. In this paper, an approach based on Artificial Neural Network (ANN optimized by Fireworks algorithm and data preprocessing by Haar Wavelet is applied to estimate the stock prices. The system was trained and tested with real data of various companies collected from Yahoo Finance. The obtained results are encouraging.

  9. Distribution Locational Marginal Pricing through Quadratic Programming for Congestion Management in Distribution Networks

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei; Oren, Shmuel S.

    2015-01-01

    ) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the ag- gregator optimization which may cause......This paper presents the distribution locational mar- ginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO...

  10. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Junghwan Jin

    Full Text Available Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  11. Train Stop Scheduling in a High-Speed Rail Network by Utilizing a Two-Stage Approach

    Directory of Open Access Journals (Sweden)

    Huiling Fu

    2012-01-01

    Full Text Available Among the most commonly used methods of scheduling train stops are practical experience and various “one-step” optimal models. These methods face problems of direct transferability and computational complexity when considering a large-scale high-speed rail (HSR network such as the one in China. This paper introduces a two-stage approach for train stop scheduling with a goal of efficiently organizing passenger traffic into a rational train stop pattern combination while retaining features of regularity, connectivity, and rapidity (RCR. Based on a three-level station classification definition, a mixed integer programming model and a train operating tactics descriptive model along with the computing algorithm are developed and presented for the two stages. A real-world numerical example is presented using the Chinese HSR network as the setting. The performance of the train stop schedule and the applicability of the proposed approach are evaluated from the perspective of maintaining RCR.

  12. Channel access delay and buffer distribution of two-user opportunistic scheduling schemes in wireless networks

    KAUST Repository

    Hossain, Md Jahangir; Alouini, Mohamed-Slim; Bhargava, Vijay K.

    2010-01-01

    In our earlier works, we proposed rate adaptive hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) and hybrid two-user scheduling (HTS) schemes. The proposed schemes are innovative in the sense that they include a second

  13. A Metaheuristic Scheduler for Time Division Multiplexed Network-on-Chip

    DEFF Research Database (Denmark)

    Sørensen, Rasmus Bo; Sparsø, Jens; Pedersen, Mark Ruvald

    2014-01-01

    significant practical implications, is the minimization of the TDM schedule period by over-provisioning bandwidth to connections with the smallest bandwidth requirements. Our results show that this is possible with only negligible impact on the schedule period. We evaluate the scheduler with seven different...... applications from the MCSL NOC benchmark suite. In the special case of all-to-all communication with equal bandwidths on all communication channels, we obtain schedules with a shorter period than reported in previous work....

  14. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

  15. An Interference-Aware Traffic-Priority-Based Link Scheduling Algorithm for Interference Mitigation in Multiple Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Thien T. T. Le

    2016-12-01

    Full Text Available Currently, wireless body area networks (WBANs are effectively used for health monitoring services. However, in cases where WBANs are densely deployed, interference among WBANs can cause serious degradation of network performance and reliability. Inter-WBAN interference can be reduced by scheduling the communication links of interfering WBANs. In this paper, we propose an interference-aware traffic-priority-based link scheduling (ITLS algorithm to overcome inter-WBAN interference in densely deployed WBANs. First, we model a network with multiple WBANs as an interference graph where node-level interference and traffic priority are taken into account. Second, we formulate link scheduling for multiple WBANs as an optimization model where the objective is to maximize the throughput of the entire network while ensuring the traffic priority of sensor nodes. Finally, we propose the ITLS algorithm for multiple WBANs on the basis of the optimization model. High spatial reuse is also achieved in the proposed ITLS algorithm. The proposed ITLS achieves high spatial reuse while considering traffic priority, packet length, and the number of interfered sensor nodes. Our simulation results show that the proposed ITLS significantly increases spatial reuse and network throughput with lower delay by mitigating inter-WBAN interference.

  16. Algorithm for complete enumeration based on a stroke graph to solve the supply network configuration and operations scheduling problem

    Directory of Open Access Journals (Sweden)

    Julien Maheut

    2013-07-01

    Full Text Available Purpose: The purpose of this paper is to present an algorithm that solves the supply network configuration and operations scheduling problem in a mass customization company that faces alternative operations for one specific tool machine order in a multiplant context. Design/methodology/approach: To achieve this objective, the supply chain network configuration and operations scheduling problem is presented. A model based on stroke graphs allows the design of an algorithm that enumerates all the feasible solutions. The algorithm considers the arrival of a new customized order proposal which has to be inserted into a scheduled program. A selection function is then used to choose the solutions to be simulated in a specific simulation tool implemented in a Decision Support System. Findings and Originality/value: The algorithm itself proves efficient to find all feasible solutions when alternative operations must be considered. The stroke structure is successfully used to schedule operations when considering more than one manufacturing and supply option in each step. Research limitations/implications: This paper includes only the algorithm structure for a one-by-one, sequenced introduction of new products into the list of units to be manufactured. Therefore, the lotsizing process is done on a lot-per-lot basis. Moreover, the validation analysis is done through a case study and no generalization can be done without risk. Practical implications: The result of this research would help stakeholders to determine all the feasible and practical solutions for their problem. It would also allow to assessing the total costs and delivery times of each solution. Moreover, the Decision Support System proves useful to assess alternative solutions. Originality/value: This research offers a simple algorithm that helps solve the supply network configuration problem and, simultaneously, the scheduling problem by considering alternative operations. The proposed system

  17. Social Optimization and Pricing Policy in Cognitive Radio Networks with an Energy Saving Strategy

    Directory of Open Access Journals (Sweden)

    Shunfu Jin

    2016-01-01

    Full Text Available The rapid growth of wireless application results in an increase in demand for spectrum resource and communication energy. In this paper, we firstly introduce a novel energy saving strategy in cognitive radio networks (CRNs and then propose an appropriate pricing policy for secondary user (SU packets. We analyze the behavior of data packets in a discrete-time single-server priority queue under multiple-vacation discipline. With the help of a Quasi-Birth-Death (QBD process model, we obtain the joint distribution for the number of SU packets and the state of base station (BS via the Matrix-Geometric Solution method. We assess the average latency of SU packets and the energy saving ratio of system. According to a natural reward-cost structure, we study the individually optimal behavior and the socially optimal behavior of the energy saving strategy and use an optimization algorithm based on standard particle swarm optimization (SPSO method to search the socially optimal arrival rate of SU packets. By comparing the individually optimal behavior and the socially optimal behavior, we impose an appropriate admission fee to SU packets. Finally, we present numerical results to show the impacts of system parameters on the system performance and the pricing policy.

  18. Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013

    International Nuclear Information System (INIS)

    Dev, Priya; Martin, Michael A.

    2014-01-01

    Highlights: • Neural nets are unable to properly capture spiky price behavior found in the electricity market. • We modeled electricity price data from the Australian National Electricity Market over 15 years. • Neural nets need to be augmented with other modeling techniques to capture price spikes. • We fit a Generalized Pareto Distribution to price spikes using a peaks-over-thresholds approach. - Abstract: Competitors in the electricity supply industry desire accurate predictions of electricity spot prices to hedge against financial risks. Neural networks are commonly used for forecasting such prices, but certain features of spot price series, such as extreme price spikes, present critical challenges for such modeling. We investigate the predictive capacity of neural networks for electricity spot prices using Australian National Electricity Market data. Following neural net modeling of the data, we explore extreme price spikes through extreme value modeling, fitting a Generalized Pareto Distribution to price peaks over an estimated threshold. While neural nets capture the smoother aspects of spot price data, they are unable to capture local, volatile features that characterize electricity spot price data. Price spikes can be modeled successfully through extreme value modeling

  19. Demand Response Design and Use Based on Network Locational Marginal Prices

    DEFF Research Database (Denmark)

    Morais, Hugo; Faria, Pedro; Vale, Zita

    2014-01-01

    Power systems have been experiencing huge changes mainly due to the substantial increase of distributed generation (DG) and the operation in competitive environments. Virtual Power Players (VPP) can aggregate several players, namely a diversity of energy resources, including distributed generation...... (DG) based on several technologies, electric storage systems (ESS) and demand response (DR). Energy resources management gains an increasing relevance in this competitive context. This makes the DR use more interesting and flexible, giving place to a wide range of new opportunities. This paper...... proposes a methodology to support VPPs in the DR programs’ management, considering all the existing energy resources (generation and storage units) and the distribution network. The proposed method is based on locational marginal prices (LMP) values. The evaluation of the impact of using DR specific...

  20. Optimal Scheduling of Domestic Appliances via MILP

    Directory of Open Access Journals (Sweden)

    Zdenek Bradac

    2014-12-01

    Full Text Available This paper analyzes a consumption scheduling mechanism for domestic appliances within a home area network. The aim of the proposed scheduling is to minimize the total energy price paid by the consumer and to reduce power peaks in order to achieve a balanced daily load schedule. An exact and computationally efficient mixed-integer linear programming (MILP formulation of the problem is presented. This model is verified by several problem instances. Realistic scenarios based on the real price tariffs commercially available in the Czech Republic are calculated. The results obtained by solving the optimization problem are compared with a simulation of the ripple control service currently used by many domestic consumers in the Czech Republic.

  1. Pharma Pricing & Market Access Europe 2016--Health Network Communications' Tenth Annual Conference (February 23-25, 2016--London, UK).

    Science.gov (United States)

    D'Souza, P

    2016-03-01

    Tighter national budgets and escalating drug prices continue to present challenges for pharmaceutical market access strategies and societal cost of care. As pharmaceutical companies and medical governmental advisory organizations enter tougher negotiations, hospital trusts and other dispensary firms face barriers to receiving the best medical treatment, and as a result patient access is limited. The 2016 HealthNetwork Communications' Pharma Pricing & Market Access Europe meeting brought together pharmaceutical, medical governmental advisory and stakeholders and market access/pricing consultants, to encourage discussions and negotiations into how to improve the drug pricing system and consequential market access strategies while achieving the respective reimbursement and affordability objectives. Copyright 2016 Prous Science, S.A.U. or its licensors. All rights reserved.

  2. Analysis of the uranium price predicted to 24 months, implementing neural networks and the Monte Carlo method like predictive tools

    International Nuclear Information System (INIS)

    Esquivel E, J.; Ramirez S, J. R.; Palacios H, J. C.

    2011-11-01

    The present work shows predicted prices of the uranium, using a neural network. The importance of predicting financial indexes of an energy resource, in this case, allows establishing budgetary measures, as well as the costs of the resource to medium period. The uranium is part of the main energy generating fuels and as such, its price rebounds in the financial analyses, due to this is appealed to predictive methods to obtain an outline referent to the financial behaviour that will have in a certain time. In this study, two methodologies are used for the prediction of the uranium price: the Monte Carlo method and the neural networks. These methods allow predicting the indexes of monthly costs, for a two years period, starting from the second bimonthly of 2011. For the prediction the uranium costs are used, registered from the year 2005. (Author)

  3. A Metaheuristic Scheduler for Time Division Multiplexed Network-on-Chip

    DEFF Research Database (Denmark)

    Sørensen, Rasmus Bo; Sparsø, Jens; Pedersen, Mark Ruvald

    that this is possible with only negligible impact on the schedule period. We evaluate the scheduler with seven different applications from the MCSL NOC benchmark suite. We observe that the metaheuristics perform better than the greedy solution. In the special case of all-to-all communication with equal bandwidths...

  4. Combined Rate and Power Allocation with Link Scheduling in Wireless Data Packet Relay Networks with Fading Channels

    OpenAIRE

    Subhrakanti Dey; Minyi Huang

    2007-01-01

    We consider a joint rate and power control problem in a wireless data traffic relay network with fading channels. The optimization problem is formulated in terms of power and rate selection, and link transmission scheduling. The objective is to seek high aggregate utility of the relay node when taking into account buffer load management and power constraints. The optimal solution for a single transmitting source is computed by a two-layer dynamic programming algorithm which leads to optimal ...

  5. Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

    Directory of Open Access Journals (Sweden)

    Yuqing Yang

    2015-09-01

    Full Text Available With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS has to be involved to mitigate the bad effects of DERs’ integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs considered as the auxiliary technique are also introduced to attenuate the DERs’ influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs’ operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration. After simulation, the proposed scheduling strategies have been verified to be extremely effective for the enhancement of the distribution network characteristics.

  6. On the performance of two-way multiuser mixed RF/FSO relay networks with opportunistic scheduling & asymmetric channel gains

    KAUST Repository

    Al-Eryani, Yasser F.; Salhab, Anas M.; Zummo, Salam A.; Alouini, Mohamed-Slim

    2017-01-01

    In this paper, the performance of two-way relaying (TWR) multiuser mixed radio frequency/free space optical (RF/FSO) relay networks with opportunistic user scheduling and asymmetric channel fading is studied. First, closed-form expressions for the exact outage probability, asymptotic (high signal-to-noise ration (SNR)) outage probability, and average ergodic channel capacity are derived assuming heterodyne detection (HD) scheme. Additionally, impacts of several system parameters including number of users, pointing errors, and atmospheric turbulence conditions on the overall network performance are investigated. All the theoretical results are validated by Monte-Carlo simulations. The results show that the TWR scheme almost doubles the network ergodic capacity compared to that of one-way relaying (OWR) scheme with the same outage performance. Additionally, the overall diversity order of the network is shown to be affected not only by the number of users, but it is also a function of the pointing error and atmospheric turbulence conditions.

  7. On the performance of two-way multiuser mixed RF/FSO relay networks with opportunistic scheduling & asymmetric channel gains

    KAUST Repository

    Al-Eryani, Yasser F.

    2017-07-20

    In this paper, the performance of two-way relaying (TWR) multiuser mixed radio frequency/free space optical (RF/FSO) relay networks with opportunistic user scheduling and asymmetric channel fading is studied. First, closed-form expressions for the exact outage probability, asymptotic (high signal-to-noise ration (SNR)) outage probability, and average ergodic channel capacity are derived assuming heterodyne detection (HD) scheme. Additionally, impacts of several system parameters including number of users, pointing errors, and atmospheric turbulence conditions on the overall network performance are investigated. All the theoretical results are validated by Monte-Carlo simulations. The results show that the TWR scheme almost doubles the network ergodic capacity compared to that of one-way relaying (OWR) scheme with the same outage performance. Additionally, the overall diversity order of the network is shown to be affected not only by the number of users, but it is also a function of the pointing error and atmospheric turbulence conditions.

  8. Using Artificial Neural Networks to Determine Significant Factors Affecting the Pricing of WPT Effluent for Industrial Uses in Isfahan

    Directory of Open Access Journals (Sweden)

    Masoud Mirmohamadsaseghi

    2017-03-01

    Full Text Available The evidence indicates increasing trend of use of municipal wastewater treatment effluent as an alternative source of water both in developed and developing countries. Proper pricing of this unconventional water is one of the most effective economic tools to encourage optimum use of fresh water resources. In this study, artificial neural network is employed to identify and assess the factors affecting effluent tariffs supplied to local industries in Isfahan region. Given the wide variety of factors involved in the ultimate value of wastewater traement plant effluent, an assortment of relevant factors  has been considered in this study; the factors include the population served by the treatment plant, volume of effluent produced, maintenance, repair and replacement. costs of operating plants, topography, different water uses in the region, industrial wastewater collection fees, unit cost of pipe and fittings, and the volumes of water supplied from springs and aqueducts  in the region. Neural network modeling is used as a tool to determine the significance of each factor for pricing effluent. Based on the available data and the neural network models, the effects of different model architectures with different intermediate layers and numbers of nodes in each layer on the price of wastewater were investigated to develop aand adopt a final neural network model. Results indicate that the proposed neural network model enjoys a high potential and has been well capable of determining the weights of the parameter affecting in pricing effluent. Based on the the results of this study, the factors with the greatest role in effluent pricing are unit cost of pipe and fittings, industrial use of water, and the costs of plant maintentance, repair and replacement.

  9. Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

    KAUST Repository

    Celik, Abdulkadir; Kamal, Ahmed E.

    2016-01-01

    the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy, and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and is shown to perform very

  10. Third party access pricing to the network, secondary capacity market and economic optimum: the case of natural gas

    Energy Technology Data Exchange (ETDEWEB)

    David, L.; Percebois, J

    2002-09-01

    The gas deregulation process implies crucial choices concerning access to transportation networks. These choices deal with the nature, the structure and the level of access fees. This paper proposes an evaluation of different systems implemented both in Europe and North America, in relation to normative pricing references. The rules according to which shippers can buy or sell capacity represent another kind of choice that Regulators have to make. This paper proposes a simple model which demonstrates that secondary market prices should not be subject to a cap and emphasizes the need of a 'use-it-or-lose-it' rule on this market. (authors)

  11. Third party access pricing to the network, secondary capacity market and economic optimum: the case of natural gas

    International Nuclear Information System (INIS)

    David, L.; Percebois, J.

    2002-09-01

    The gas deregulation process implies crucial choices concerning access to transportation networks. These choices deal with the nature, the structure and the level of access fees. This paper proposes an evaluation of different systems implemented both in Europe and North America, in relation to normative pricing references. The rules according to which shippers can buy or sell capacity represent another kind of choice that Regulators have to make. This paper proposes a simple model which demonstrates that secondary market prices should not be subject to a cap and emphasizes the need of a 'use-it-or-lose-it' rule on this market. (authors)

  12. A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands

    DEFF Research Database (Denmark)

    Fattahi, Mohammad; Govindan, Kannan; Keyvanshokooh, Esmaeil

    2018-01-01

    In this paper, we address a multi-period supply chain network redesign problem in which customer zones have price-dependent stochastic demand for multiple products. A novel multi-stage stochastic program is proposed to simultaneously make tactical decisions including products' prices and strategic...... redesign decisions. Existing uncertainty in potential demands of customer zones is modeled through a finite set of scenarios, described in the form of a scenario tree. The scenarios are generated using a Latin Hypercube Sampling method and then a forward scenario construction technique is employed...

  13. A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks

    Science.gov (United States)

    Shi, Binbin; Wei, Wei; Wang, Yihuai; Shu, Wanneng

    2016-01-01

    In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS) based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. Secondly, the redundancy degree of a node is calculated according to its sensing area being covered by the neighboring sensors. Finally, a centralized approximation algorithm based on the partition of the target area is designed to obtain the approximate minimum set of nodes, which can retain the sufficient coverage of the target region and ensure the connectivity of the network at the same time. The simulation results show that the proposed CPCSS can balance the energy consumption and optimize the coverage performance of the sensor network. PMID:27754405

  14. Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling.

    Science.gov (United States)

    Sefuba, Maria; Walingo, Tom; Takawira, Fambirai

    2015-09-18

    This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols.

  15. A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks.

    Science.gov (United States)

    Shi, Binbin; Wei, Wei; Wang, Yihuai; Shu, Wanneng

    2016-10-14

    In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS) based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. Secondly, the redundancy degree of a node is calculated according to its sensing area being covered by the neighboring sensors. Finally, a centralized approximation algorithm based on the partition of the target area is designed to obtain the approximate minimum set of nodes, which can retain the sufficient coverage of the target region and ensure the connectivity of the network at the same time. The simulation results show that the proposed CPCSS can balance the energy consumption and optimize the coverage performance of the sensor network.

  16. A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Binbin Shi

    2016-10-01

    Full Text Available In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. Secondly, the redundancy degree of a node is calculated according to its sensing area being covered by the neighboring sensors. Finally, a centralized approximation algorithm based on the partition of the target area is designed to obtain the approximate minimum set of nodes, which can retain the sufficient coverage of the target region and ensure the connectivity of the network at the same time. The simulation results show that the proposed CPCSS can balance the energy consumption and optimize the coverage performance of the sensor network.

  17. Sleep/wake scheduling scheme for minimizing end-to-end delay in multi-hop wireless sensor networks

    Directory of Open Access Journals (Sweden)

    Madani Sajjad

    2011-01-01

    Full Text Available Abstract We present a sleep/wake schedule protocol for minimizing end-to-end delay for event driven multi-hop wireless sensor networks. In contrast to generic sleep/wake scheduling schemes, our proposed algorithm performs scheduling that is dependent on traffic loads. Nodes adapt their sleep/wake schedule based on traffic loads in response to three important factors, (a the distance of the node from the sink node, (b the importance of the node's location from connectivity's perspective, and (c if the node is in the proximity where an event occurs. Using these heuristics, the proposed scheme reduces end-to-end delay and maximizes the throughput by minimizing the congestion at nodes having heavy traffic load. Simulations are carried out to evaluate the performance of the proposed protocol, by comparing its performance with S-MAC and Anycast protocols. Simulation results demonstrate that the proposed protocol has significantly reduced the end-to-end delay, as well as has improved the other QoS parameters, like average energy per packet, average delay, packet loss ratio, throughput, and coverage lifetime.

  18. ELIMINATION OF THE DISADVANTAGES OF SCHEDULING-NETWORK PLANNING BY APPLYING THE MATRIX OF KEY PROJECT EVENTS

    Directory of Open Access Journals (Sweden)

    Morozenko Andrey Aleksandrovich

    2017-07-01

    Full Text Available The article discusses the current disadvantages of the scheduling-network planning in the management of the terms of investment-construction project. Problems associated with the construction of the schedule and the definitions of the duration of the construction project are being studied. The problems of project management for the management apparatus are shown, which consists in the absence of mechanisms for prompt response to deviations in the parameters of the scheduling-network diagram. A new approach to planning the implementation of an investment-construction project based on a matrix of key events and a rejection of the current practice of determining the duration based on inauthentic regulatory data. An algorithm for determining the key events of the project is presented. For increase the reliability of the organizational structure, the load factor of the functional block in the process of achieving the key event is proposed. Recommendations for improving the interaction of the participants in the investment-construction project are given.

  19. Channel access delay and buffer distribution of two-user opportunistic scheduling schemes in wireless networks

    KAUST Repository

    Hossain, Md Jahangir

    2010-07-01

    In our earlier works, we proposed rate adaptive hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) and hybrid two-user scheduling (HTS) schemes. The proposed schemes are innovative in the sense that they include a second user in the transmission opportunistically using hierarchical modulations. As such the frequency of information access of the users increases without any degradation of the system spectral efficiency (SSE) compared to the classical opportunistic scheduling scheme. In this paper, we analyze channel access delay of an incoming packet at the base station (BS) buffer when our proposed TBS and HTS schemes are employed at the BS. Specifically, using a queuing analytic model we derive channel access delay as well as buffer distribution of the packets that wait at BS buffer for down-link (DL) transmission. We compare performance of the TBS and HTS schemes with that of the classical single user opportunistic schemes namely, absolute carrier-to-noise ratio (CNR)-based single user scheduling (ASS) and normalized CNR-based single user scheduling (NSS). For an independent and identically distributed (i.i.d.) fading environment, our proposed scheme can improve packet\\'s access delay performance compared to the ASS. Selected numerical results in an independent but non-identically distributed (i.n.d.) fading environment show that our proposed HTS achieves overall good channel access delay performance. © 2010 IEEE.

  20. Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network

    Directory of Open Access Journals (Sweden)

    Anup Kumar Paul

    2017-09-01

    Full Text Available With the advancement of the Long-Term Evolution (LTE network and smart-phones, most of today’s internet content is delivered via cellular links. Due to the nature of wireless signal propagation, the capacity of the last hop link can vary within a short period of time. Unfortunately, Transmission Control Protocol (TCP does not perform well in such scenarios, potentially leading to poor Quality of Service (QoS (e.g., end-to-end throughput and delay for the end user. In this work, we have studied the effect of Active Queue Management (AQM based congestion control and intra LTE handover on the performance of different Medium Access Control (MAC schedulers with TCP traffic by ns3 simulation. A proper AQM design in the Radio Link Control (RLC buffer of eNB in the LTE network leads to the avoidance of forced drops and link under-utilization along with robustness to a variety of network traffic-loads. We first demonstrate that the original Random Early Detection (RED linear dropping function cannot cope well with different traffic-load scenarios. Then, we establish a heuristic approach in which different non-linear functions are proposed with one parameter free to define. In our simulations, we demonstrate that the performance of different schedulers can be enhanced via proper dropping function.

  1. Two-user opportunistic scheduling using hierarchical modulations in wireless networks with heterogenous average link gains

    KAUST Repository

    Hossain, Md Jahangir; Alouini, Mohamed-Slim; Bhargava, Vijay K.

    2010-01-01

    -user opportunistic scheduling (HTS) scheme based on our earlier proposed TBS scheme. This HTS scheme selects the first user based on the largest absolute CNR value among all the users while the second user is selected based on the ratios of the absolute CNRs

  2. Integrated Optimization of Service-Oriented Train Plan and Schedule on Intercity Rail Network with Varying Demand

    Directory of Open Access Journals (Sweden)

    Wenliang Zhou

    2015-01-01

    Full Text Available For a better service level of a train operating plan, we propose an integrated optimization method of train planning and train scheduling, which generally are optimized, respectively. Based on the cost analysis of both passengers travelling and enterprises operation, and the constraint analysis of trains operation, we construct a multiobjective function and build an integrated optimization model with the aim of reducing both passenger travel costs and enterprise operating costs. Then, a solving algorithm is established based on the simulated annealing algorithm. Finally, using as an example the Changzhutan intercity rail network, as an example we analyze the optimized results and the influence of the model parameters on the results.

  3. HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

    Science.gov (United States)

    Hua, Hook; Mrozinski, Joseph J.; Elfes, Alberto; Adumitroaie, Virgil; Shelton, Kacie E.; Smith, Jeffrey H.; Lincoln, William P.; Weisbin, Charles R.

    2012-01-01

    HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

  4. Two-user opportunistic scheduling using hierarchical modulations in wireless networks with heterogenous average link gains

    KAUST Repository

    Hossain, Md Jahangir

    2010-03-01

    Our contribution, in this paper, is two-fold. First, we analyze the performance of a hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) scheme, which was proposed by the authors, in a fading environment where different users have different average link gains. Specifically, we present a new expression for the spectral efficiency (SE) of the users and using this expression, we compare the degrees of fairness (DOF) of the TBS scheme with that of classical single user opportunistic scheduling schemes, namely, absolute carrier-to-noise ratio (CNR) based single-best user scheduling (SBS) and normalized CNR based proportional fair scheduling (PFS) schemes. The second contribution is that we propose a new hybrid two-user opportunistic scheduling (HTS) scheme based on our earlier proposed TBS scheme. This HTS scheme selects the first user based on the largest absolute CNR value among all the users while the second user is selected based on the ratios of the absolute CNRs to the corresponding average CNRs of the remaining users. The total transmission rate i.e., the constellation size is selected according to the absolute CNR of the first best user. The total transmission rate is then allocated among these selected users by joint consideration of their absolute CNRs and allocated number of information bit(s) are transmitted to them using hierarchical modulations. Numerical results are presented for a fading environment where different users experience independent but non-identical (i.n.d.) channel fading. These selected numerical results show that the proposed HTS scheme can considerably increase the system\\'s fairness without any degradation of the link spectral efficiency (LSE) i.e., the multiuser diversity gain compared to the classical SBS scheme. These results also show that the proposed HTS scheme has a lower fairness in comparison to the PFS scheme which suffers from a considerable degradation in LSE. © 2010 IEEE.

  5. A monopoly pricing model for diffusion maximization based on heterogeneous nodes and negative network externalities (Case study: A novel product

    Directory of Open Access Journals (Sweden)

    Aghdas Badiee

    2018-10-01

    Full Text Available Social networks can provide sellers across the world with invaluable information about the structure of possible influences among different members of a network, whether positive or negative, and can be used to maximize diffusion in the network. Here, a novel mathematical monopoly product pricing model is introduced for maximization of market share in noncompetitive environment. In the proposed model, a customer’s decision to buy a product is not only based on the price, quality and need time for the product but also on the positive and negative influences of his/her neighbors. Therefore, customers are considered heterogeneous and a referral bonus is granted to every customer whose neighbors also buy the product. Here, the degree of influence is directly related to the intensity of the customers’ relationships. Finally, using the proposed model for a real case study, the optimal policy for product sales that is the ratio of product sale price in comparison with its cost and also the optimal amounts of referral bonus per customer is achieved.

  6. Developing a Mathematical Model for Scheduling and Determining Success Probability of Research Projects Considering Complex-Fuzzy Networks

    Directory of Open Access Journals (Sweden)

    Gholamreza Norouzi

    2015-01-01

    Full Text Available In project management context, time management is one of the most important factors affecting project success. This paper proposes a new method to solve research project scheduling problems (RPSP containing Fuzzy Graphical Evaluation and Review Technique (FGERT networks. Through the deliverables of this method, a proper estimation of project completion time (PCT and success probability can be achieved. So algorithms were developed to cover all features of the problem based on three main parameters “duration, occurrence probability, and success probability.” These developed algorithms were known as PR-FGERT (Parallel and Reversible-Fuzzy GERT networks. The main provided framework includes simplifying the network of project and taking regular steps to determine PCT and success probability. Simplifications include (1 equivalent making of parallel and series branches in fuzzy network considering the concepts of probabilistic nodes, (2 equivalent making of delay or reversible-to-itself branches and impact of changing the parameters of time and probability based on removing related branches, (3 equivalent making of simple and complex loops, and (4 an algorithm that was provided to resolve no-loop fuzzy network, after equivalent making. Finally, the performance of models was compared with existing methods. The results showed proper and real performance of models in comparison with existing methods.

  7. Application of Neural Network Technologies for Price Forecasting in the Liberalized Electricity Market

    Science.gov (United States)

    Gerikh, Valentin; Kolosok, Irina; Kurbatsky, Victor; Tomin, Nikita

    2009-01-01

    The paper presents the results of experimental studies concerning calculation of electricity prices in different price zones in Russia and Europe. The calculations are based on the intelligent software "ANAPRO" that implements the approaches based on the modern methods of data analysis and artificial intelligence technologies.

  8. Access pricing on gas networks and capacity release markets: Lessons from North American and European experiences

    International Nuclear Information System (INIS)

    David, L.; Percebois, J.

    2004-01-01

    An evaluation of different access fee systems in North America and Europe in relation to normative prices is discussed. Among available alternatives the entry-exit pricing system as it is currently applied in the United Kingdom, the Netherlands, Italy and France, was judged to be the best solution to increased competition. Canadian and American experiences highlight the influence of the market power of shippers with regard to the efficacy of capping the market. Whether or not to cap the price on a capacity release market is a choice between the protection of shippers against market abuses and the promotion of secondary market liquidity, a choice that is linked to the level of congestion of a pipeline system. If there is much congestion, a price cap may be necessary; if there is little congestion, the need for market value given by an uncapped price may be more important than the market power of shippers. 15 refs., 2 tabs

  9. Network-based model for predicting the effect of fuel price on transit ridership and greenhouse gas emissions

    Directory of Open Access Journals (Sweden)

    Michael W. Levin

    2017-12-01

    Full Text Available As fuel prices increase, drivers may make travel choices to minimize not only travel time, but also fuel consumption. Consideration of fuel consumption would affect route choice and influence trip frequency and mode choice. For instance, travelers may elect to live closer to their workplace, or use public transit to avoid fuel consumption and the associated costs. To incorporate network characteristics into predictions of the effects of fuel prices, we develop a multi-class combined elastic demand, mode choice, and user equilibrium model using a generalized cost function of travel time and fuel consumption with a combined solution algorithm. The algorithm is implemented in a custom software package, and a case study application on the Austin, Texas network is presented. We evaluate the fuel-price sensitivity of key variables such as drive-alone and transit class proportions, person-miles traveled, link-level traffic flow and per capita fuel consumption and emissions. These effects are examined across a heterogeneous demand set, with multiple user-classes categorized based on their value of travel time. The highest relative transit elasticities against fuel price are observed among low value of time classes, as expected. Although total personal vehicle travel decreases, congestion increases on some roads due to the generalized cost function. Reductions in system-wide fuel consumption and greenhouse gas emissions are observed as well. The study uncovers the combined interactions among fuel prices, multi-modal choice behavior, travel performance, and resultant environmental impacts, all of which dictate the urban travel market. It also equips agencies with motivation to tailor emissions reduction and transit-ridership stimulus policies around the most responsive user classes.

  10. PRICE AND PRICING STRATEGIES

    OpenAIRE

    SUCIU Titus

    2013-01-01

    In individual companies, price is one significant factor in achieving marketing success. In many purchase situations, price can be of great importance to customers. Marketers must establish pricing strategies that are compatible with the rest of the marketing mix. Management should decide whether to charge the same price to all similar buyers of identical quantities of a product (a one-price strategy) or to set different prices (a flexible price strategy). Many organizations, especially retai...

  11. Sensors on speaking terms: Schedule-based medium access control protocols for wireless sensor networks

    NARCIS (Netherlands)

    van Hoesel, L.F.W.

    2007-01-01

    Wireless sensor networks make the previously unobservable, observable. The basic idea behind these networks is straightforward: all wires are cut in traditional sensing systems and the sensors are equipped with batteries and radio's to virtually restore the cut wires. The resulting sensors can be

  12. Enriching the tactical network design of express service carriers with fleet scheduling characteristics

    NARCIS (Netherlands)

    Meuffels, W.J.M.; Fleuren, H.A.; Cruijssen, F.C.A.M.; Dam, E.R.

    2010-01-01

    Express service carriers provide time-guaranteed deliveries of parcels via a network consisting of nodes and hubs. In this, nodes take care of the collection and delivery of parcels, and hubs have the function to consolidate parcels in between the nodes. The tactical network design problem assigns

  13. Electricity pricing

    International Nuclear Information System (INIS)

    Wijayatunga, P.D.C.

    1994-01-01

    Electricity pricing in most countries, especially in the developing world, has been determined by traditional accounting criteria where it raises revenue requirements to cover the operating costs and a return on past and future capital investments in possible power systems. The use of economic principles to improve the total economic efficiency in the electricity industry is discussed. Basic marginal cost theory, long run marginal costing (LRMC) cost categories and rating periods, marginal capacity costs, marginal energy costs, consumer costs, short run marginal costing (SRMC), marginal cost of fuel, marginal cost of network losses, market clearing price, value of unserved energy and network quality of supply cost are discussed

  14. Prices and Price Setting

    NARCIS (Netherlands)

    R.P. Faber (Riemer)

    2010-01-01

    textabstractThis thesis studies price data and tries to unravel the underlying economic processes of why firms have chosen these prices. It focuses on three aspects of price setting. First, it studies whether the existence of a suggested price has a coordinating effect on the prices of firms.

  15. Floyd-A∗ Algorithm Solving the Least-Time Itinerary Planning Problem in Urban Scheduled Public Transport Network

    Directory of Open Access Journals (Sweden)

    Yu Zhang

    2014-01-01

    Full Text Available We consider an ad hoc Floyd-A∗ algorithm to determine the a priori least-time itinerary from an origin to a destination given an initial time in an urban scheduled public transport (USPT network. The network is bimodal (i.e., USPT lines and walking and time dependent. The modified USPT network model results in more reasonable itinerary results. An itinerary is connected through a sequence of time-label arcs. The proposed Floyd-A∗ algorithm is composed of two procedures designated as Itinerary Finder and Cost Estimator. The A∗-based Itinerary Finder determines the time-dependent, least-time itinerary in real time, aided by the heuristic information precomputed by the Floyd-based Cost Estimator, where a strategy is formed to preestimate the time-dependent arc travel time as an associated static lower bound. The Floyd-A∗ algorithm is proven to guarantee optimality in theory and, demonstrated through a real-world example in Shenyang City USPT network to be more efficient than previous procedures. The computational experiments also reveal the time-dependent nature of the least-time itinerary. In the premise that lines run punctually, “just boarding” and “just missing” cases are identified.

  16. Heterogeneous Cellular Networks with Spatio-Temporal Traffic: Delay Analysis and Scheduling

    OpenAIRE

    Zhong, Yi; Quek, Tony Q. S.; Ge, Xiaohu

    2016-01-01

    Emergence of new types of services has led to various traffic and diverse delay requirements in fifth generation (5G) wireless networks. Meeting diverse delay requirements is one of the most critical goals for the design of 5G wireless networks. Though the delay of point-to-point communications has been well investigated, the delay of multi-point to multi-point communications has not been thoroughly studied since it is a complicated function of all links in the network. In this work, we propo...

  17. Power Saving Scheduling Scheme for Internet of Things over LTE/LTE-Advanced Networks

    OpenAIRE

    Kuo, Yen-Wei; Chou, Li-Der

    2015-01-01

    The devices of Internet of Things (IoT) will grow rapidly in the near future, and the power consumption and radio spectrum management will become the most critical issues in the IoT networks. Long Term Evolution (LTE) technology will become a promising technology used in IoT networks due to its flat architecture, all-IP network, and greater spectrum efficiency. The 3rd Generation Partnership Project (3GPP) specified the Discontinuous Reception (DRX) to reduce device’s power consumption. Howev...

  18. Planning and scheduling for maritime container yards supporting and facilitating the global supply network

    CERN Document Server

    Li, Wenkai; Goh, Mark

    2015-01-01

    Maximizing reader insights into the challenges facing maritime supply chains and container port logistics service providers in Asia, this book highlights their innovative responses to these challenges through real-world case studies. With a focus on mathematical modeling, simulation and heuristics approaches, this book provides academics, engineers, container terminal operators, students in logistics and supply chain management with the latest approaches that can be used to address the planning and scheduling problem in large container terminal yards. This book can be used on a self-contained basis as teaching cases in an undergraduate or specialist class setting, or on techniques applied to maritime container operations for port operations.

  19. Integrating piecewise linear representation and ensemble neural network for stock price prediction

    OpenAIRE

    Asaduzzaman, Md.; Shahjahan, Md.; Ahmed, Fatema Johera; Islam, Md. Monirul; Murase, Kazuyuki

    2014-01-01

    Stock Prices are considered to be very dynamic and susceptible to quick changes because of the underlying nature of the financial domain, and in part because of the interchange between known parameters and unknown factors. Of late, several researchers have used Piecewise Linear Representation (PLR) to predict the stock market pricing. However, some improvements are needed to avoid the appropriate threshold of the trading decision, choosing the input index as well as improving the overall perf...

  20. 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....

  1. Routing and Scheduling Algorithms in Resource-Limited Wireless Multi-Hop Networks

    National Research Council Canada - National Science Library

    Michail, Anastassios

    2001-01-01

    ...) to transmit their messages to the desired destinations. The distinguishing features of such all-wireless network architectures give rise to new trade-offs between traditional concerns in wireless communications...

  2. IPTV traffic management using topology-based hierarchical scheduling in Carrier Ethernet transport networks

    DEFF Research Database (Denmark)

    Yu, Hao; Yan, Ying; Berger, Michael Stubert

    2009-01-01

    Carrier Ethernet is becoming a favorable access technology for Next Generation Network (NGN). The features of cost-efficiency, operation flexibility and high bandwidth have a great attraction to service providers. However, to achieve these characteristics, Carrier Ethernet needs to have Quality o....... This work has been carried out as a part of the research project HIPT (High quality IP network for IPTV and VoIP) founded by Danish Advanced Technology Foundation....

  3. Scheduling optimization of a real-world multi product pipeline network; Otimizacao das operacoes de transporte de derivados de petroleo em redes de dutos

    Energy Technology Data Exchange (ETDEWEB)

    Boschetto, Suelen N.; Felizari, Luiz C.; Magatao, Leandro; Stebel, Sergio L.; Neves Junior, Flavio; Lueders, Ricardo; Arruda, Lucia V.R. de [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil); Ribas, Paulo Cesar; Bernardo, Luiz F.J. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas (CENPES)

    2008-07-01

    This work develops an optimization structure to aid the operational decision-making of scheduling activities in a real world pipeline network. The proposed approach is based on a decomposition method to address complex problems with high computational burden. The Pre-analysis makes a previous evaluation of a batch sequencing, getting information to be entered into optimization block. The continuous time Mixed Integer Linear Program (MILP) model gets such information and calculates the scheduling. The models are applied to a pipeline network that connects different areas including refineries, terminals, and final clients. Many oil derivatives (e.g. gasoline, liquefied petroleum gas, naphtha) can be sent or received in this network. The computational burden to determine a short-term scheduling within the considered scenario is a relevant issue. Many insights have been derived from the obtained solutions, which are given in a reduced computational time for oil industrial-size scenarios. (author)

  4. Deficit Round Robin with Fragmentation Scheduling to Achieve Generalized Weighted Fairness for Resource Allocation in IEEE 802.16e Mobile WiMAX Networks

    Directory of Open Access Journals (Sweden)

    Chakchai So-In

    2010-10-01

    Full Text Available Deficit Round Robin (DRR is a fair packet-based scheduling discipline commonly used in wired networks where link capacities do not change with time. However, in wireless networks, especially wireless broadband networks, i.e., IEEE 802.16e Mobile WiMAX, there are two main considerations violate the packet-based service concept for DRR. First, the resources are allocated per Mobile WiMAX frame. To achieve full frame utilization, Mobile WiMAX allows packets to be fragmented. Second, due to a high variation in wireless channel conditions, the link/channel capacity can change over time and location. Therefore, we introduce a Deficit Round Robin with Fragmentation (DRRF to allocate resources per Mobile WiMAX frame in a fair manner by allowing for varying link capacity and for transmitting fragmented packets. Similar to DRR and Generalized Processor Sharing (GPS, DRRF achieves perfect fairness. DRRF results in a higher throughput than DRR (80% improvement while causing less overhead than GPS (8 times less than GPS. In addition, in Mobile WiMAX, the quality of service (QoS offered by service providers is associated with the price paid. This is similar to a cellular phone system; the users may be required to pay air-time charges. Hence, we have also formalized a Generalized Weighted Fairness (GWF criterion which equalizes a weighted sum of service time units or slots, called temporal fairness, and transmitted bytes, called throughput fairness, for customers who are located in a poor channel condition or at a further distance versus for those who are near the base stations, or have a good channel condition. We use DRRF to demonstrate the application of GWF. These fairness criteria are used to satisfy basic requirements for resource allocation, especially for non real-time traffic. Therefore, we also extend DRRF to support other QoS requirements, such as minimum reserved traffic rate, maximum sustained traffic rate, and traffic priority. For real

  5. VNS (Variable Neighbourhood Search) applied to batch sequencing in operational scheduling of pipeline network; VNS (Variable Neighbourhood Search) aplicado ao sequenciamento de bateladas do 'scheduling' de operacoes de uma malha dutoviaria

    Energy Technology Data Exchange (ETDEWEB)

    Yamamoto, Lia; Arruda, Lucia Valeria Ramos de; Libert, Nikolas [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil)

    2008-07-01

    This work presents the VNS heuristic technique applied on batches ordering in a real network of petroleum derivatives distribution. These ordering have great influence in operational scheduling of a pipeline network. The operational scheduling purposes the efficient utilization of the resources, resulting on a better performance. Due to the great complexity of the real network problem and the necessity of its resolution in little computational time, it was adopted a problem subdivision in assignment of resources, sequencing and timing. In the resources assignment stage, it is considered the production/consumption functions and the products tankages to determine the total batches, including its volume, flow rate and the time-windows to satisfy the demand. These data are used in the sequencing stage, where a VNS based model determines the batches ordering. In a final step, the last block, realize the temporisation considering the network operational constraints. This work shows the results from the optimization of the sequencing stage which aims the improvement of the solution quality of scheduling. (author)

  6. Experimental demonstration of bandwidth on demand (BoD) provisioning based on time scheduling in software-defined multi-domain optical networks

    Science.gov (United States)

    Zhao, Yongli; Li, Yajie; Wang, Xinbo; Chen, Bowen; Zhang, Jie

    2016-09-01

    A hierarchical software-defined networking (SDN) control architecture is designed for multi-domain optical networks with the Open Daylight (ODL) controller. The OpenFlow-based Control Virtual Network Interface (CVNI) protocol is deployed between the network orchestrator and the domain controllers. Then, a dynamic bandwidth on demand (BoD) provisioning solution is proposed based on time scheduling in software-defined multi-domain optical networks (SD-MDON). Shared Risk Link Groups (SRLG)-disjoint routing schemes are adopted to separate each tenant for reliability. The SD-MDON testbed is built based on the proposed hierarchical control architecture. Then the proposed time scheduling-based BoD (Ts-BoD) solution is experimentally demonstrated on the testbed. The performance of the Ts-BoD solution is evaluated with respect to blocking probability, resource utilization, and lightpath setup latency.

  7. Frame Allocation and Scheduling for Relay Networks in the LTE Advanced Standard

    OpenAIRE

    Roth, Stefan

    2010-01-01

    The use of relays is seen as a promising way to extend cell coverage and increase rates in LTE Advanced networks. Instead of increasing the number of base stations (BS), relays with lower cost could provide similar gains. A relay will have a wireless link to the closest BS as only connection to the core network and will cover areas close to the cell edge or other areas with limited rates. Performing transmissions in several hops (BS-relay & relay-user) requires more radio resources than u...

  8. Reducing Communication Overhead by Scheduling TCP Transfers on Mobile Devices using Wireless Network Performance Maps

    DEFF Research Database (Denmark)

    Højgaard-Hansen, Kim; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter

    2012-01-01

    The performance of wireless communication networks has been shown to have a strong location dependence. Measuring the performance while having accurate location information available makes it possible to generate performance maps. In this paper we propose a framework for the generation and use...... of such performance maps. We demonstrate how the framework can be used to reduce the retransmissions and to better utilise network resources when performing TCP-based file downloads in vehicular M2M communication scenarios. The approach works on top of a standard TCP stack hence has to map identified transmission...

  9. An Effective Scheduling-Based RFID Reader Collision Avoidance Model and Its Resource Allocation via Artificial Immune Network

    Directory of Open Access Journals (Sweden)

    Shanjin Wang

    2016-01-01

    Full Text Available Radio frequency identification, that is, RFID, is one of important technologies in Internet of Things. Reader collision does impair the tag identification efficiency of an RFID system. Many developed methods, for example, the scheduling-based series, that are used to avoid RFID reader collision, have been developed. For scheduling-based methods, communication resources, that is, time slots, channels, and power, are optimally assigned to readers. In this case, reader collision avoidance is equivalent to an optimization problem related to resource allocation. However, the existing methods neglect the overlap between the interrogation regions of readers, which reduces the tag identification rate (TIR. To resolve this shortage, this paper attempts to build a reader-to-reader collision avoidance model considering the interrogation region overlaps (R2RCAM-IRO. In addition, an artificial immune network for resource allocation (RA-IRO-aiNet is designed to optimize the proposed model. For comparison, some comparative numerical simulations are arranged. The simulation results show that the proposed R2RCAM-IRO is an effective model where TIR is improved significantly. And especially in the application of reader-to-reader collision avoidance, the proposed RA-IRO-aiNet outperforms GA, opt-aiNet, and PSO in the total coverage area of readers.

  10. Combined Rate and Power Allocation with Link Scheduling in Wireless Data Packet Relay Networks with Fading Channels

    Directory of Open Access Journals (Sweden)

    Subhrakanti Dey

    2007-08-01

    Full Text Available We consider a joint rate and power control problem in a wireless data traffic relay network with fading channels. The optimization problem is formulated in terms of power and rate selection, and link transmission scheduling. The objective is to seek high aggregate utility of the relay node when taking into account buffer load management and power constraints. The optimal solution for a single transmitting source is computed by a two-layer dynamic programming algorithm which leads to optimal power, rate, and transmission time allocation at the wireless links. We further consider an optimal power allocation problem for multiple transmitting sources in the same framework. Performances of the resource allocation algorithms including the effect of buffer load control are illustrated via extensive simulation studies.

  11. A real-time networked camera system : a scheduled distributed camera system reduces the latency

    NARCIS (Netherlands)

    Karatoy, H.

    2012-01-01

    This report presents the results of a Real-time Networked Camera System, com-missioned by the SAN Group in TU/e. Distributed Systems are motivated by two reasons, the first reason is the physical environment as a requirement and the second reason is to provide a better Quality of Service (QoS). This

  12. OPS: Opportunistic pipeline scheduling in long-strip wireless sensor networks with unreliable links

    NARCIS (Netherlands)

    Guo, Peng; Meratnia, Nirvana; Havinga, Paul J.M.; Jiang, He; Zhang, Kui

    2015-01-01

    Being deployed in narrow but long area, strip wireless sensor networks (SWSNs) have drawn much attention in applications such as coal mines, pipeline and structure monitoring. One of typical characteristics of SWSNs is the large hop counts, which leads to long end-to-end delivery delay in

  13. Scheduling Mission-Critical Flows in Congested and Contested Airborne Network Environments

    Science.gov (United States)

    2018-03-01

    networks for atmospheric, wildlife, and ecological monitoring. They equip airborne nodes with off-the-shelf 802.15.4-compliant Zigbee radios. They...Theoretical Criminology, vol. 15, no. 3, pp. 239–254, 2011. [51] R. L. Finn and D. Wright, “Unmanned aircraft systems: Surveillance, ethics and privacy in

  14. A Statically Scheduled Time-Division-Multiplexed Network-on-Chip for Real-Time Systems

    DEFF Research Database (Denmark)

    Schoeberl, Martin; Brandner, Florian; Sparsø, Jens

    2012-01-01

    This paper explores the design of a circuit-switched network-on-chip (NoC) based on time-division-multiplexing (TDM) for use in hard real-time systems. Previous work has primarily considered application-specific systems. The work presented here targets general-purpose hardware platforms. We...

  15. Echoes from the deep - Communication scheduling, localization and time-synchronization in underwater acoustic sensor networks.

    NARCIS (Netherlands)

    van Kleunen, W.A.P.

    2014-01-01

    Wireless Sensor Networks (WSNs) caused a shift in the way things are monitored. While traditional monitoring was coarse-grained and offline, using WSNs allows fine-grained and real-time monitoring. While radio-based WSNs are growing out of the stage of research to commercialization and widespread

  16. Integration of wireless sensor networks into automatic irrigation scheduling of a center pivot

    Science.gov (United States)

    A six-span center pivot system was used as a platform for testing two wireless sensor networks (WSN) of infrared thermometers. The cropped field was a semi-circle, divided into six pie shaped sections of which three were irrigated manually and three were irrigated automatically based on the time tem...

  17. Scheduled MAC in Beacon Overlay Networks for Underwater Localization and Time-Synchronization

    NARCIS (Netherlands)

    van Kleunen, W.A.P.; Meratnia, Nirvana; Havinga, Paul J.M.

    2011-01-01

    In this article we introduce a MAC protocol designed for underwater localization and time-synchronisation. The MAC protocol assumes a network of static reference nodes and allows blind nodes to be localized by listening-only to the beacon messages. Such a system is known to be very scalable. We show

  18. A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Bangzhu Zhu

    2012-02-01

    Full Text Available Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD, genetic algorithm (GA and artificial neural network (ANN is proposed to forecast carbon price. Firstly, the proposed model uses EMD to decompose carbon price data into several intrinsic mode functions (IMFs and one residue. Then, the IMFs and residue are composed into a high frequency component, a low frequency component and a trend component which have similar frequency characteristics, simple components and strong regularity using the fine-to-coarse reconstruction algorithm. Finally, those three components are predicted using an ANN trained by GA, i.e., a GAANN model, and the final forecasting results can be obtained by the sum of these three forecasting results. For verification and testing, two main carbon future prices with different maturity in the European Climate Exchange (ECX are used to test the effectiveness of the proposed multiscale ensemble forecasting model. Empirical results obtained demonstrate that the proposed multiscale ensemble forecasting model can outperform the single random walk (RW, ARIMA, ANN and GAANN models without EMD preprocessing and the ensemble ARIMA model with EMD preprocessing.

  19. Electric space heating scheduling for real-time explicit power control in active distribution networks

    DEFF Research Database (Denmark)

    Costanzo, Giuseppe Tommaso; Bernstein, Andrey; Chamorro, Lorenzo Reyes

    2015-01-01

    This paper presents a systematic approach for abstracting the flexibility of a building space heating system and using it within a composable framework for real-time explicit power control of microgrids and, more in general, active distribution networks. In particular, the proposed approach...... is developed within the context of a previously defined microgrid control framework, called COMMELEC, conceived for the explicit and real-time control of these specific networks. The designed control algorithm is totally independent from the need of a building model and allows exploiting the intrinsic thermal...... inertia for real-time control. The paper first discusses the general approach, then it proves its validity via dedicated simulations performed on specific case study composed by the CIGRE LV microgrid benchmark proposed by the Cigré TF C6.04.02....

  20. Proficient Node Scheduling Protocol for Homogeneous and Heterogeneous Wireless Sensor Networks

    OpenAIRE

    R. Saravanakumar; N. Mohankumar; J. Raja

    2013-01-01

    Recent communications in wireless sensor networks (WSNs) have much new energy-efficient protocols specifically designed, where energy awareness is an essential consideration. In WSNs, large numbers of tiny sensor nodes are used as an effective way of data gathering in various environments. Since the sensor nodes operate on battery of limited power, it is a great challenging aim to design an energy-efficient routing protocol, which can minimize the delay while offering high-energy efficiency a...

  1. NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse

    OpenAIRE

    Suárez-Figueroa, Mari Carmen

    2010-01-01

    A new ontology development paradigm has started; its emphasis lies on the reuse and possible subsequent reengineering of knowledge resources, on the collaborative and argumentative ontology development, and on the building of ontology networks; this new trend is the opposite of building new ontologies from scratch. To help ontology developers in this new paradigm, it is important to provide strong methodological support. This thesis presents some contributions to the methodological area of...

  2. Controlling market power and price spikes in electricity networks: Demand-side bidding.

    Science.gov (United States)

    Rassenti, Stephen J; Smith, Vernon L; Wilson, Bart J

    2003-03-04

    In this article we report an experiment that examines how demand-side bidding can discipline generators in a market for electric power. First we develop a treatment without demand-side bidding; two large firms are allocated baseload and intermediate cost generators such that either firm might unilaterally withhold the capacity of its intermediate cost generators from the market to benefit from the supracompetitive prices that would result from only selling its baseload units. In a converse treatment, ownership of some of the intermediate cost generators is transferred from each of these firms to two other firms such that no one firm could unilaterally restrict output to spawn supracompetitive prices. Having established a well controlled data set with price spikes paralleling those observed in the naturally occurring economy, we also extend the design to include demand-side bidding. We find that demand-side bidding completely neutralizes the exercise of market power and eliminates price spikes even in the presence of structural market power.

  3. Freezing issue on stability master production scheduling for supplier network: Decision making view

    Directory of Open Access Journals (Sweden)

    Aisyati Azizah

    2017-01-01

    Full Text Available In the daily operation, there are frequently changes in customer order requirement which will induce instability of the MPS. Moreover, the frequently adjustment of MPS can induce fluctuation of production and increasing of inventory cost as well as decreasing service level of customer. Most of studies about instability of MPS use freezing method and rolling procedure to adjust MPS periodically. Freezing is the proportion of planning horizon being frozen, whereas rolling procedure is a method replanning periodically of MPS using newly updated demand data. This study is focused on interval freezing length as an issue of decision making. In supply chain, a manufacturer is supported by suppliers to supply material requirement. Since a manufacturer plan production schedule on MPS the freezing interval is determined that will be informed to suppliers which supply the material requirement. In previous research, the freezing interval is decided by manufacturer as necessary decision maker. This decision must be followed by suppliers though it is not beneficial for them. It can be concluded that this condition is no win-win situation. Hence, this research proposes that suppliers will be involved as decision maker besides a manufacturer so the interval freezing is decided by two-side decision maker.

  4. A Comparison of Software Schedule Estimators

    Science.gov (United States)

    1990-09-01

    SLIM ...................................... 33 SPQR /20 ................................... 35 System -4 .................................... 37 Previous...24 3. PRICE-S Outputs ..................................... 26 4. COCOMO Factors by Category ........................... 28 5. SPQR /20 Activities...actual schedules experienced on the projects. The models analyzed were REVIC, PRICE-S, System-4, SPQR /20, and SEER. ix A COMPARISON OF SOFTWARE

  5. Nonlinear Pricing to Produce Information

    OpenAIRE

    David J. Braden; Shmuel S. Oren

    1994-01-01

    We investigate the firm's dynamic nonlinear pricing problem when facing consumers whose tastes vary according to a scalar index. We relax the standard assumption that the firm knows the distribution of this index. In general the firm should determine its marginal price schedule as if it were myopic, and produce information by lowering the price schedule; “bunching” consumers at positive purchase levels should be avoided. As a special case we also consider a market characterized by homogeneous...

  6. Large deviations for Gaussian queues modelling communication networks

    CERN Document Server

    Mandjes, Michel

    2007-01-01

    Michel Mandjes, Centre for Mathematics and Computer Science (CWI) Amsterdam, The Netherlands, and Professor, Faculty of Engineering, University of Twente. At CWI Mandjes is a senior researcher and Director of the Advanced Communications Network group.  He has published for 60 papers on queuing theory, networks, scheduling, and pricing of networks.

  7. Experiences with Implementing a Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Gathering on a Real-Life Sensor Network Platform

    NARCIS (Netherlands)

    Zhang, Y.; Chatterjea, Supriyo; Havinga, Paul J.M.

    2007-01-01

    We report our experiences with implementing a distributed and self-organizing scheduling algorithm designed for energy-efficient data gathering on a 25-node multihop wireless sensor network (WSN). The algorithm takes advantage of spatial correlations that exist in readings of adjacent sensor nodes

  8. Enhanced capital-asset pricing model for the reconstruction of bipartite financial networks

    NARCIS (Netherlands)

    Squartini, Tiziano; Almog, Assaf; Caldarelli, Guido; Van Lelyveld, Iman; Garlaschelli, Diego; Cimini, Giulio

    2017-01-01

    Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets are

  9. Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Mario Manzano

    2015-01-01

    Full Text Available Within the challenging environment of intelligent transportation systems (ITS, networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA proposal combining time division multiple access (TDMA and frequency division multiple access (FDMA schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.

  10. Node Scheduling Strategies for Achieving Full-View Area Coverage in Camera Sensor Networks

    Science.gov (United States)

    Wu, Peng-Fei; Xiao, Fu; Sha, Chao; Huang, Hai-Ping; Wang, Ru-Chuan; Xiong, Nai-Xue

    2017-01-01

    Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI). To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA) in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA) for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions. PMID:28587304

  11. Node Scheduling Strategies for Achieving Full-View Area Coverage in Camera Sensor Networks

    Directory of Open Access Journals (Sweden)

    Peng-Fei Wu

    2017-06-01

    Full Text Available Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI. To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions.

  12. Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

    Directory of Open Access Journals (Sweden)

    Muqaddas Naz

    2018-02-01

    Full Text Available With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE, is proposed by merging enhanced differential evolution (EDE and gray wolf optimization (GWO scheme using real-time pricing (RTP and critical peak pricing (CPP. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI. On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

  13. The role of efficiency estimates in regulatory price reviews: Ofgem's approach to benchmarking electricity networks

    International Nuclear Information System (INIS)

    Pollitt, Michael

    2005-01-01

    Electricity regulators around the world make use of efficiency analysis (or benchmarking) to produce estimates of the likely amount of cost reduction which regulated electric utilities can achieve. This short paper examines the use of such efficiency estimates by the UK electricity regulator (Ofgem) within electricity distribution and transmission price reviews. It highlights the place of efficiency analysis within the calculation of X factors. We suggest a number of problems with the current approach and make suggestions for the future development of X factor setting. (author)

  14. Range Scheduling Aid (RSA)

    Science.gov (United States)

    Logan, J. R.; Pulvermacher, M. K.

    1991-01-01

    Range Scheduling Aid (RSA) is presented in the form of the viewgraphs. The following subject areas are covered: satellite control network; current and new approaches to range scheduling; MITRE tasking; RSA features; RSA display; constraint based analytic capability; RSA architecture; and RSA benefits.

  15. Design and Evaluation of the User-Adapted Program Scheduling system based on Bayesian Network and Constraint Satisfaction

    Science.gov (United States)

    Iwasaki, Hirotoshi; Sega, Shinichiro; Hiraishi, Hironori; Mizoguchi, Fumio

    In recent years, lots of music content can be stored in mobile computing devices, such as a portable digital music player and a car navigation system. Moreover, various information content like news or traffic information can be acquired always anywhere by a cellular communication and a wireless LAN. However, usability issues arise from the simple interfaces of mobile computing devices. Moreover, retrieving and selecting such content poses safety issues, especially while driving. Thus, it is important for the mobile system to recommend content automatically adapted to user's preference and situation. In this paper, we present the user-adapted program scheduling that generates sequences of content (Program) suiting user's preference and situation based on the Bayesian network and the Constraint Satisfaction Problem (CSP) technique. We also describe the design and evaluation of its realization system, the Personal Program Producer (P3). First, preference such as a genre ratio of content in a program is learned as a Bayesian network model using simple operations such as a skip behavior. A model including each content tends to become large-scale. In order to make it small, we present the model separation method that carries out losslessly compression of the model. Using the model, probabilistic distributions of preference to generate constraints are inferred. Finally satisfying the constraints, a program is produced. This kind of CSP has an issue of which the number of variables is not fixedness. In order to make it variable, we propose a method using metavariables. To evaluate the above methods, we applied them to P3 on a car navigation system. User evaluations helped us clarify that the P3 can produce the program that a user prefers and adapt it to the user.

  16. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  17. Optimizing Transmission and Shutdown for Energy-Efficient Real-time Packet Scheduling in Clustered Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Rajkumar Ragunathan

    2005-01-01

    Full Text Available Energy efficiency is imperative to enable the deployment of ad hoc networks. Conventional power management focuses independently on the physical or MAC layer and approaches differ depending on the abstraction level. At the physical layer, the fundamental tradeoff between transmission rate and energy is exploited, which leads to transmit as slow as possible. At MAC level, power reduction techniques aim to transmit as fast as possible to maximize the radios power-off interval. The two approaches seem conflicting and it is not obvious which one is the most appropriate. We propose a transmission strategy that optimally mixes both techniques in a multiuser context. We present a cross-layer solution considering the transceiver power characteristics, the varying system load, and the dynamic channel constraints. Based on this, we derive a low-complexity online scheduling algorithm. Results considering an -ary quadrature amplitude modulation radio show that for a range of scenarios a large power reduction is achieved, compared to the case where only scaling or shutdown is considered.

  18. Security-Reliability Trade-Off Analysis for Multiuser SIMO Mixed RF/FSO Relay Networks With Opportunistic User Scheduling

    KAUST Repository

    El-Malek, Ahmed H. Abd

    2016-05-24

    In this paper, we study the performance of multiuser single-input multiple-output mixed radio frequency (RF)/free space optical (FSO) relay network with opportunistic user scheduling. The considered system includes multiple users, one amplify-and-forward relay, one destination, and a multiple-antenna eavesdropper. The users are connected with the relay node through RF links and the relay is connected with the destination through an FSO link. Both maximum ratio combining and selection combining schemes are used at the multiple-antenna relay to combine the signal received from the best user on different antennas. The RF/FSO channels models are assumed to follow Nakagami-m/gamma-gamma fading models with pointing errors. Closed-form expressions are derived for the outage probability, average symbol error probability, and ergodic channel capacity. Then, the power of the selected best user is determined to minimize the system asymptotic outage probability under the dominant RF or FSO link. Then, the considered system secrecy performance is investigated, where the closed-form expressions for the intercept probability are derived. Finally, we propose a new cooperative jamming model in which the worst user is selected by the authorized system to jam the existing eavesdropper. Monte-Carlo simulations are provided to validate the achieved exact and asymptotic results.

  19. A distributed incentive compatible pricing mechanism for P2P networks

    Science.gov (United States)

    Zhang, Jie; Zhao, Zheng; Xiong, Xiao; Shi, Qingwei

    2007-09-01

    Peer-to-Peer (P2P) systems are currently receiving considerable interest. However, as experience with P2P networks shows, the selfish behaviors of peers may lead to serious problems of P2P network, such as free-riding and white-washing. In order to solve these problems, there are increasing considerations on reputation system design in the study of P2P networks. Most of the existing works is concerning probabilistic estimation or social networks to evaluate the trustworthiness for a peer to others. However, these models can not be efficient all the time. In this paper, our aim is to provide a general mechanism that can maximize P2P networks social welfare in a way of Vickrey-Clarke-Groves family, while assuming every peer in P2P networks is rational and selfish, which means they only concern about their own outcome. This mechanism has some desirable properties using an O(n) algorithm: (1) incentive compatibility, every peer truly report its connection type; (2) individually rationality; and (3) fully decentralized, we design a multiple-principal multiple-agent model, concerning about the service provider and service requester individually.

  20. Analysis of the impact of crude oil price fluctuations on China's stock market in different periods-Based on time series network model

    Science.gov (United States)

    An, Yang; Sun, Mei; Gao, Cuixia; Han, Dun; Li, Xiuming

    2018-02-01

    This paper studies the influence of Brent oil price fluctuations on the stock prices of China's two distinct blocks, namely, the petrochemical block and the electric equipment and new energy block, applying the Shannon entropy of information theory. The co-movement trend of crude oil price and stock prices is divided into different fluctuation patterns with the coarse-graining method. Then, the bivariate time series network model is established for the two blocks stock in five different periods. By joint analysis of the network-oriented metrics, the key modes and underlying evolutionary mechanisms were identified. The results show that the both networks have different fluctuation characteristics in different periods. Their co-movement patterns are clustered in some key modes and conversion intermediaries. The study not only reveals the lag effect of crude oil price fluctuations on the stock in Chinese industry blocks but also verifies the necessity of research on special periods, and suggests that the government should use different energy policies to stabilize market volatility in different periods. A new way is provided to study the unidirectional influence between multiple variables or complex time series.

  1. Price Competition in Two-Sided Markets with Heterogeneous Consumers and Network Effects

    NARCIS (Netherlands)

    Filistrucchi, L.; Klein, T.J.

    We model a two-sided market with heterogeneous customers and two heterogeneous network effects. In our model, customers on each market side care differently about both the number and the type of customers on the other side. Examples of two-sided markets are online platforms or daily newspapers. In

  2. A Branch-and-Price Approach to the Feeder Network Design Problem

    DEFF Research Database (Denmark)

    Santini, Alberto; Plum, Christian Edinger Munk; Røpke, Stefan

    2017-01-01

    In this paper we consider the problem of designing a container liner shipping feeder network. The designer has to choose which port to serve during many rotations that start and end at a central hub. Many operational characteristics are considered, such as variable leg-by-leg speeds and cargo...

  3. Automated and dynamic scheduling for geodetic VLBI - A simulation study for AuScope and global networks

    Science.gov (United States)

    Iles, E. J.; McCallum, L.; Lovell, J. E. J.; McCallum, J. N.

    2018-02-01

    As we move into the next era of geodetic VLBI, the scheduling process is one focus for improvement in terms of increased flexibility and the ability to react with changing conditions. A range of simulations were conducted to ascertain the impact of scheduling on geodetic results such as Earth Orientation Parameters (EOPs) and station coordinates. The potential capabilities of new automated scheduling modes were also simulated, using the so-called 'dynamic scheduling' technique. The primary aim was to improve efficiency for both cost and time without losing geodetic precision, particularly to maximise the uses of the Australian AuScope VLBI array. We show that short breaks in observation will not significantly degrade the results of a typical 24 h experiment, whereas simply shortening observing time degrades precision exponentially. We also confirm the new automated, dynamic scheduling mode is capable of producing the same standard of result as a traditional schedule, with close to real-time flexibility. Further, it is possible to use the dynamic scheduler to augment the 3 station Australian AuScope array and thereby attain EOPs of the current global precision with only intermittent contribution from 2 additional stations. We thus confirm automated, dynamic scheduling bears great potential for flexibility and automation in line with aims for future continuous VLBI operations.

  4. Null Space Based Preemptive Scheduling For Joint URLLC and eMBB Traffic in 5G Networks

    DEFF Research Database (Denmark)

    Abdul-Mawgood Ali Ali Esswie, Ali; Pedersen, Klaus

    2018-01-01

    In this paper, we propose a null-space-based preemptive scheduling framework for cross-objective optimization to always guarantee robust URLLC performance, while extracting the maximum possible eMBB capacity. The proposed scheduler perpetually grants incoming URLLC traffic a higher priority for i...

  5. User’s guide to SNAP for ArcGIS® :ArcGIS interface for scheduling and network analysis program

    Science.gov (United States)

    Woodam Chung; Dennis Dykstra; Fred Bower; Stephen O’Brien; Richard Abt; John. and Sessions

    2012-01-01

    This document introduces a computer software named SNAP for ArcGIS® , which has been developed to streamline scheduling and transportation planning for timber harvest areas. Using modern optimization techniques, it can be used to spatially schedule timber harvest with consideration of harvesting costs, multiple products, alternative...

  6. Internet resource pricing models

    CERN Document Server

    Xu, Ke; He, Huan

    2013-01-01

    This brief guides the reader through three basic Internet resource pricing models using an Internet cost analysis. Addressing the evolution of service types, it presents several corresponding mechanisms which can ensure pricing implementation and resource allocation. The authors discuss utility optimization of network pricing methods in economics and underline two classes of pricing methods including system optimization and entities' strategic optimization. The brief closes with two examples of the newly proposed pricing strategy helping to solve the profit distribution problem brought by P2P

  7. Forecasting the term structure of crude oil futures prices with neural networks

    Czech Academy of Sciences Publication Activity Database

    Baruník, Jozef; Malinská, B.

    2016-01-01

    Roč. 164, č. 1 (2016), s. 366-379 ISSN 0306-2619 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Term structure * Nelson–Siegel model * Dynamic neural networks * Crude oil futures Subject RIV: AH - Economics Impact factor: 7.182, year: 2016 http://library.utia.cas.cz/separaty/2016/E/barunik-0453168.pdf

  8. Comparison of Neural Networks and Regression Time Series in Estimating the Development of the Afternoon Price of Palladium on the New York Stock Exchange

    Directory of Open Access Journals (Sweden)

    Marek Vochozka

    2017-12-01

    Full Text Available Purpose of the article: Palladium is presently used for producing electronics, industrial products or jewellery, as well as products in the medical field. Its value is raised especially by its unique physical and chemical characteristics. Predicting the value of such a metal is not an easy matter (with regard to the fact that prices may change significantly in time. Methodology/methods: To carry out the analysis, London Fix Price PM data was used, i.e. amounts reported in the afternoon for a period longer than 10 years. To process the data, Statistica software is used. Linear regression is carried out using a whole range of functions, and subsequently regression via neural structures is performed, where several distributional functions are used again. Subsequently, 1000 neural networks are generated, out of which 5 proving the best characteristics are chosen. Scientific aim: The aim of the paper is to perform a regression analysis of the development of the palladium price on the New York Stock Exchange using neural structures and linear regression, then to compare the two methods and determine the more suitable one for a possible prediction of the future development of the palladium price on the New York Stock Exchange. Findings: Results are compared on the level of an expert perspective and the evaluator’s – economist’s experience. Within regression time lines, the curve obtained by the least squares methods via negative-exponential smoothing gets closest to Palladium price line development. Out of the neural networks, all 5 chosen networks prove to be the most practically useful. Conclusions: Because it is not possible to predict extraordinary situations and their impact on the palladium price (at most in the short term, but certainly not over a long period of time, simplification and the creation of a relatively simple model is appropriate and the result is useful.

  9. Distributed Multi-Cell Resource Allocation with Price Based ICI Coordination in Downlink OFDMA Networks

    Science.gov (United States)

    Lv, Gangming; Zhu, Shihua; Hui, Hui

    Multi-cell resource allocation under minimum rate request for each user in OFDMA networks is addressed in this paper. Based on Lagrange dual decomposition theory, the joint multi-cell resource allocation problem is decomposed and modeled as a limited-cooperative game, and a distributed multi-cell resource allocation algorithm is thus proposed. Analysis and simulation results show that, compared with non-cooperative iterative water-filling algorithm, the proposed algorithm can remarkably reduce the ICI level and improve overall system performances.

  10. Incorporating network effects in a competitive electricity industry. An Australian perspective

    International Nuclear Information System (INIS)

    Outhred, H.; Kaye, J.

    1996-01-01

    The role of an electricity network in a competitive electricity industry is reviewed, the nation's experience with transmission pricing is discussed, and a 'Nodal Auction Model' for incorporating network effects in a competitive electricity industry is proposed. The model uses a computer-based auction procedure to address both the spatial issues associated with an electricity network and the temporal issues associated with operation scheduling. The objective is to provide a market framework that addresses both network effects and operation scheduling in a coordinated implementation of spot pricing theory. 12 refs

  11. An Effective Scheduling-Based RFID Reader Collision Avoidance Model and Its Resource Allocation via Artificial Immune Network

    OpenAIRE

    Wang, Shanjin; Li, Zhonghua; He, Chunhui; Li, Jianming

    2016-01-01

    Radio frequency identification, that is, RFID, is one of important technologies in Internet of Things. Reader collision does impair the tag identification efficiency of an RFID system. Many developed methods, for example, the scheduling-based series, that are used to avoid RFID reader collision, have been developed. For scheduling-based methods, communication resources, that is, time slots, channels, and power, are optimally assigned to readers. In this case, reader collision avoidance is equ...

  12. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Directory of Open Access Journals (Sweden)

    Montri Inthachot

    2016-01-01

    Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

  13. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Science.gov (United States)

    Boonjing, Veera; Intakosum, Sarun

    2016-01-01

    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

  14. Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks

    Directory of Open Access Journals (Sweden)

    Salim Lahmiri

    2014-07-01

    Full Text Available This paper presents a forecasting model that integrates the discrete wavelet transform (DWT and backpropagation neural networks (BPNN for predicting financial time series. The presented model first uses the DWT to decompose the financial time series data. Then, the obtained approximation (low-frequency and detail (high-frequency components after decomposition of the original time series are used as input variables to forecast future stock prices. Indeed, while high-frequency components can capture discontinuities, ruptures and singularities in the original data, low-frequency components characterize the coarse structure of the data, to identify the long-term trends in the original data. As a result, high-frequency components act as a complementary part of low-frequency components. The model was applied to seven datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model that uses only low-frequency components. In addition, the presented model outperforms both the well-known auto-regressive moving-average (ARMA model and the random walk (RW process.

  15. Refinery scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Magalhaes, Marcus V.; Fraga, Eder T. [PETROBRAS, Rio de Janeiro, RJ (Brazil); Shah, Nilay [Imperial College, London (United Kingdom)

    2004-07-01

    This work addresses the refinery scheduling problem using mathematical programming techniques. The solution adopted was to decompose the entire refinery model into a crude oil scheduling and a product scheduling problem. The envelope for the crude oil scheduling problem is composed of a terminal, a pipeline and the crude area of a refinery, including the crude distillation units. The solution method adopted includes a decomposition technique based on the topology of the system. The envelope for the product scheduling comprises all tanks, process units and products found in a refinery. Once crude scheduling decisions are Also available the product scheduling is solved using a rolling horizon algorithm. All models were tested with real data from PETROBRAS' REFAP refinery, located in Canoas, Southern Brazil. (author)

  16. Architecture of a corporate system to aid the scheduling of a oil derivatives transport in a pipeline network; Arquitetura de um sistema corporativo para auxilio a programacao do transporte de derivados de petroleo em redes dutoviarias

    Energy Technology Data Exchange (ETDEWEB)

    Lara, Guilherme R.; Polli, Helton L.; Esser, Eduardo M.; Lueders, Ricardo; Neves Junior, Flavio; Magatao, Leandro; Stebel, Sergio L. [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil); Ribas, Paulo C. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas (CENPES)

    2008-07-01

    This paper addresses the development and the architecture of a corporative package to aid the operational decision-making of the scheduling activities in a real-world pipeline network for oil derivatives. The system was developed based on a service-oriented architecture, allowing the development of Web applications to define the network scheduling, as well as graphic display of the movements. The solution of the scheduling is generated by an optimization block as a service of this application. However, this paper emphasizes the description of the architecture and its functionalities, which was defined with the help of experienced programmers. (author)

  17. Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios

    Directory of Open Access Journals (Sweden)

    Núñez Tabales, Julia M.

    2013-01-01

    Full Text Available Econometric models, in the estimation of real estate prices, are a useful and realistic approach for buyers and for local and fiscal authorities. From the classical hedonic models to more data driven procedures, based on Artificial Neural Networks (ANN, many papers have appeared in economic literature trying to compare the results attained with both approaches. We insist on the use of ANN, when there is enough statistical information, and will detail some comparisons to hedonic modeling, in a medium size city in the South of Spain, with an extensive set of data spanning over several years, collected before the actual downturn of the market. Exogenous variables include each dwelling's external and internal data (both numerical and qualitative, and data from the building in which it is located and its surroundings. Alternative models are estimated for several time intervals, and enabling the comparison of the effects of the rising prices during the bull market over the last decade. || Los modelos econométricos en la valoración de precios inmobiliarios constituyen una herramienta útil tanto para los compradores como para las autoridades locales y fiscales. Desde los modelos hedónicos clásicos hasta los planteamientos actuales a través de redes neuronales artificiales (RNA, han tenido lugar numerosas aportaciones en la literatura económica que tratan de comparar los resultados de ambos métodos. Insistimos en el empleo de RNA en el caso de disponer de suficiente información estadística. En este trabajo se aplica dicha metodología en una ciudad de tamaño medio situada en el sur de España, utilizando una extensa muestra de datos que comprende varios años precedentes a la crisis actual. Las variables utilizadas -tanto cuantitativas como cualitativas- incluyen datos externos e internos de la vivienda, del edificio en el que está localizada, así como de su entorno. Se construyen varios modelos alternativos para distintos intervalos de

  18. Dynamic electricity pricing for electric vehicles using stochastic programming

    International Nuclear Information System (INIS)

    Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, Zita

    2017-01-01

    Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business. - Highlights: • A stochastic model for energy scheduling tackling several uncertainty sources. • A two-stage stochastic programming is used to tackle the developed model. • Optimal EV electricity pricing seems to improve the profits. • The propose results suggest to increase the customers' satisfaction.

  19. Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2016-09-01

    Full Text Available This paper presents novel intraday session models for price forecasts (ISMPF models for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL and the analysis of mean absolute percentage errors (MAPEs obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

  20. A USER-DEPENDENT PERFECT-SCHEDULING MULTIPLE ACCESS PROTOCOL FOR VOICE-DATA INTEGRATION IN WIRELESS NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Zhou Yajian; Li Jiandong; Liu Kai

    2002-01-01

    A novel Multiple Access Control (MAC) protocol - User-dependent Perfect-scheduling Multiple Access (UPMA) protocol, which supports joint transmission of voice and data packets,is proposed. By this protocol, the bandwidth can be allocated dynamically to the uplink and downlink traffic with on-demand assignment and the transmission of Mobile Terminals (MTs)can be perfectly scheduled by means of polling. Meanwhile, a unique frame structure is designed to guarantee Quality of Service (QoS) in voice traffic supporting. An effective collision resolution algorithm is also proposed to guarantee rapid channel access for activated MTs. Finally, performance of UPMA protocol is evaluated by simulation and compared with MPRMA protocol.Simulation results show that UPMA protocol has better performance.

  1. A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

    Directory of Open Access Journals (Sweden)

    Zhilong Wang

    2014-01-01

    Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

  2. 48 CFR 538.270 - Evaluation of multiple award schedule (MAS) offers.

    Science.gov (United States)

    2010-10-01

    ... SERVICES ADMINISTRATION SPECIAL CATEGORIES OF CONTRACTING FEDERAL SUPPLY SCHEDULE CONTRACTING Establishing and Administering Federal Supply Schedules 538.270 Evaluation of multiple award schedule (MAS) offers... determining the Government's price negotiation objectives, consider the following factors: (1) Aggregate...

  3. Schedule Analytics

    Science.gov (United States)

    2016-04-30

    Warfare, Naval Sea Systems Command Acquisition Cycle Time : Defining the Problem David Tate, Institute for Defense Analyses Schedule Analytics Jennifer...research was comprised of the following high- level steps :  Identify and review primary data sources 1...research. However, detailed reviews of the OMB IT Dashboard data revealed that schedule data is highly aggregated. Program start date and program end date

  4. Interactive Anticipatory Scheduling for Two Military Applications

    National Research Council Canada - National Science Library

    Howe, Adele

    2003-01-01

    ...; these models partially explain what makes some job shop scheduling problems difficult. For the second, several algorithms for Air Force Satellite Control Network scheduling have been compared on historical and recent data...

  5. Price fairness

    OpenAIRE

    Diller, Hermann

    2013-01-01

    Purpose – The purpose of this article is to integrate the various strands of fair price research into a concise conceptual model. Design/methodology/approach – The proposed price fairness model is based on a review of the fair pricing literature, incorporating research reported in not only English but also German. Findings – The proposed fair price model depicts seven components of a fair price: distributive fairness, consistent behaviour, personal respect and regard for the partner, fair dea...

  6. The welfare effects of different pricing schemes for electricity distribution in Finland

    International Nuclear Information System (INIS)

    Kopsakangas-Savolainen, Maria

    2004-01-01

    The main components of electricity prices can be divided into the wholesale price, the price of network operations and taxes. Even if the wholesale price is determined efficiently, total welfare can be significantly disturbed if network operations are priced inefficiently. In this study, we calculate network prices based on four alternative methods. These are marginal cost pricing, Ramsey pricing, FDC-pricing and optimal two-part tariffs. The welfare effects on the prevailing pricing system are compared. We show that potentially significant improvements in welfare can be achieved by using marginal cost prices or optimal two-part tariffs. Also Ramsey pricing indicates that prevailing prices are inefficient

  7. The welfare effects of different pricing schemes for electricity distribution in Finland

    International Nuclear Information System (INIS)

    Kopsakangas-Savolainen, Maria

    2004-01-01

    The main components of electricity prices can be divided into the wholesale price, the price of network operations and taxes. Even if the wholesale price is determined efficiently, total welfare can be significantly disturbed if network operations are priced inefficiently. In this study, we calculate network prices based on four alternative methods. These are marginal cost pricing, Ramsey pricing, FDC-pricing and optimal two-part tariffs. The welfare effects on the prevailing pricing system are compared. We show that potentially significant improvements in welfare can be achieved by using marginal cost prices or optimal two-part tariffs. Also Ramsey pricing indicates that prevailing prices are inefficient. (Author)

  8. Resource management framework for QoS scheduling in IEEE 802.16 WiMAX networks

    DEFF Research Database (Denmark)

    Wang, Hua; Dittmann, Lars

    2009-01-01

    IEEE 802.16, also known as WiMAX, has received much attention recently for its capability to support multiple types of applications with diverse Quality-of-Service (QoS) requirements. Beyond what the standard has defined, radio resource management (RRM) still remains an open issue, which plays...... an important role in QoS provisioning for different types of services. In this chapter, we propose a downlink resource management framework for QoS scheduling in OFDMA based WiMAX systems. Our framework consists of a dynamic resource allocation (DRA) module and a connection admission control (CAC) module...

  9. Downlink resource management for QoS scheduling in IEEE 802.16 WiMAX networks

    DEFF Research Database (Denmark)

    Wang, Hua; Dittmann, Lars

    2010-01-01

    IEEE 802.16, also known as WiMAX, has received much attention recently for its capability to support multiple types of applications with diverse Quality-of-Service (QoS) requirements. Beyond what the standard has defined, radio resource management (RRM) still remains an open issue, which plays...... an important role in QoS provisioning for different types of services. In this paper, we propose a downlink resource management framework for QoS scheduling in OFDMA based WiMAX systems. Our framework consists of a dynamic resource allocation (DRA) module and a connection admission control (CAC) module. A two...

  10. Dynamic Pricing

    DEFF Research Database (Denmark)

    Sharifi, Reza; Anvari-Moghaddam, Amjad; Fathi, S. Hamid

    2017-01-01

    Dynamic pricing scheme, also known as real-time pricing (RTP), can be more efficient and technically beneficial than the other price-based schemes (such as flat-rate or time-of-use (TOU) pricing) for enabling demand response (DR) actions. Over the past few years, advantages of RTP-based schemes h...... of dynamic pricing can lead to increased willingness of consumers to participate in DR programs which in turn improve the operation of liberalized electricity markets.......Dynamic pricing scheme, also known as real-time pricing (RTP), can be more efficient and technically beneficial than the other price-based schemes (such as flat-rate or time-of-use (TOU) pricing) for enabling demand response (DR) actions. Over the past few years, advantages of RTP-based schemes...

  11. Day-ahead resource scheduling of a renewable energy based virtual power plant

    International Nuclear Information System (INIS)

    Zamani, Ali Ghahgharaee; Zakariazadeh, Alireza; Jadid, Shahram

    2016-01-01

    Highlights: • Simultaneous energy and reserve scheduling of a VPP. • Aggregate uncertainties of electricity prices, renewable generation and load demand. • Develop a stochastic scheduling model using the point estimate method. - Abstract: The evolution of energy markets is accelerating in the direction of a greater reliance upon distributed energy resources (DERs). To manage this increasing two-way complexity, virtual power plants (VPPs) are being deployed today all over the world. In this paper, a probabilistic model for optimal day ahead scheduling of electrical and thermal energy resources in a VPP is proposed where participation of energy storage systems and demand response programs (DRPs) are also taken into account. In the proposed model, energy and reserve is simultaneously scheduled considering the uncertainties of market prices, electrical demand and intermittent renewable power generation. The Point Estimate Method (PEM) is applied in order to model the uncertainties of VPP’s scheduling problem. Moreover, the optimal reserve scheduling of VPP is presented which efficiently decreases VPP’s risk facing the unexpected fluctuations of uncertain parameters at the power delivery time. The results demonstrated that implementation of demand response programs (DRPs) would decrease total operation costs of VPP as well as its dependency on the upstream network.

  12. An algorithm for on-line price discrimination

    NARCIS (Netherlands)

    D.D.B. van Bragt; D.J.A. Somefun (Koye); E. Kutschinski; J.A. La Poutré (Han)

    2002-01-01

    textabstractThe combination of on-line dynamic pricing with price discrimination can be very beneficial for firms operating on the Internet. We therefore develop an on-line dynamic pricing algorithm that can adjust the price schedule for a good or service on behalf of a firm. This algorithm (a

  13. Adaptive Scheduling Applied to Non-Deterministic Networks of Heterogeneous Tasks for Peak Throughput in Concurrent Gaudi

    CERN Document Server

    AUTHOR|(CDS)2070032; Clemencic, Marco

    As much the e-Science revolutionizes the scientific method in its empirical research and scientific theory, as it does pose the ever growing challenge of accelerating data deluge. The high energy physics (HEP) is a prominent representative of the data intensive science and requires scalable high-throughput software to be able to cope with associated computational endeavors. One such striking example is $\\text G\\rm \\small{AUDI}$ -- an experiment independent software framework, used in several frontier HEP experiments. Among them stand ATLAS and LHCb -- two of four mainstream experiments at the Large Hadron Collider (LHC) at CERN, the European Laboratory for Particle Physics. The framework is currently undergoing an architectural revolution aiming at massively concurrent and adaptive data processing. In this work I explore new dimensions of performance improvement for the next generation $\\text G\\rm \\small{AUDI}$. I then propose a complex of generic task scheduling solutions for adaptive and non-intrusive throu...

  14. Joint flow routing-scheduling for energy efficient software defined data center networks : A prototype of energy-aware network management platform

    NARCIS (Netherlands)

    Zhu, H.; Liao, X.; de Laat, C.; Grosso, P.

    Data centers are a cost-effective infrastructure for hosting Cloud and Grid applications, but they do incur tremendous energy cost and CO2 emissions. Today's data center network architectures such as Fat-tree and BCube are over-provisioned to guarantee large network capacity and meet peak

  15. A Multi-Verse Optimizer with Levy Flights for Numerical Optimization and Its Application in Test Scheduling for Network-on-Chip.

    Directory of Open Access Journals (Sweden)

    Cong Hu

    Full Text Available We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO, which incorporates Levy flights into multi-verse optimizer (MVO algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC. Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.

  16. High-Speed Rail Train Timetabling Problem: A Time-Space Network Based Method with an Improved Branch-and-Price Algorithm

    Directory of Open Access Journals (Sweden)

    Bisheng He

    2014-01-01

    Full Text Available A time-space network based optimization method is designed for high-speed rail train timetabling problem to improve the service level of the high-speed rail. The general time-space path cost is presented which considers both the train travel time and the high-speed rail operation requirements: (1 service frequency requirement; (2 stopping plan adjustment; and (3 priority of train types. Train timetabling problem based on time-space path aims to minimize the total general time-space path cost of all trains. An improved branch-and-price algorithm is applied to solve the large scale integer programming problem. When dealing with the algorithm, a rapid branching and node selection for branch-and-price tree and a heuristic train time-space path generation for column generation are adopted to speed up the algorithm computation time. The computational results of a set of experiments on China’s high-speed rail system are presented with the discussions about the model validation, the effectiveness of the general time-space path cost, and the improved branch-and-price algorithm.

  17. Network planning for scheduling operations in air cargo handling : a tool in medium term goods flow control

    NARCIS (Netherlands)

    Verwijmeren, M.A.A.P.; Tilanus, C.B.

    1993-01-01

    In the hub of a hub-and-spokes network for airfreight transportation, the main part of the incoming and outgoing goodsflow is in special loading units for airfreight. These loading units are metal pallets and containers up to eighteen cubic meters in size. The key part of operations in the hub is

  18. Optimal joint scheduling of electrical and thermal appliances in a smart home environment

    International Nuclear Information System (INIS)

    Shirazi, Elham; Zakariazadeh, Alireza; Jadid, Shahram

    2015-01-01

    Highlights: • Thermal appliances are scheduled based on desired temperature and energy prices. • A discomfort index has been introduced within the home energy scheduling model. • Appliances are scheduled based on activity probability and desired options. • Starting probability depends on the social random factor and consumption behavior. - Abstract: With the development of home area network, residents have the opportunity to schedule their power usage in the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, a home energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented in which electrical and thermal appliances are jointly scheduled. The proposed method aims at minimizing the electricity cost of a residential customer by scheduling various type of appliances considering the residents consumption behavior, seasonal probability, social random factor, discomfort index and appliances starting probability functions. In this model, the home central controller receives the electricity price information, environmental factors data as well as the resident desired options in order to optimally schedule appliances including electrical and thermal. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, photovoltaic panel, combined heat and power unit, boiler and electrical and thermal storages over a 24-h period. The results show that the scheduling of different appliances can be reached simultaneously by using the proposed formulation. Moreover, simulation results evidenced that the proposed home energy management model exhibits a lower cost and, therefore, is more economical.

  19. Hybrid Particle Swarm Optimization based Day-Ahead Self-Scheduling for Thermal Generator in Competitive Electricity Market

    DEFF Research Database (Denmark)

    Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi-objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day-ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day...

  20. Transmission network price setting model for the promotion of liberalized market for the power industry in Taiwan

    International Nuclear Information System (INIS)

    Chen, Po-Han; Tsay, Ing-Sheng

    2017-01-01

    Currently, privatized state-owned Taiwan Power Company (Taipower) has a monopoly over energy production and vertically integrates the electricity market in Taiwan. Because of the geographical environments, economies of scales, social structures, and diverse political ideologies in Taiwan, the electricity market exhibits unique characteristics. To maximize the benefits of electricity liberalization, future market operations must not only involve conventional considerations for economic dispatch; overall social welfare should be included by incorporating the perspectives of fairness, responsibility, and the environment, and key electrical grid operations should be executed appropriately. We responded to the future market liberalization planning by including factors, such as, environmental effects and government policies, in addition to electrical line construction, and operation cost factors involved in the entire operational model planning. On the basis of responsibility sharing, this study used the simulated-responsibility three-phase pricing method. The proposed pricing method is expected to help attain the following policy goals: 1.Achieving reasonable power price allocations; 2.Simultaneously ensuring fair and efficient electric grid operation; 3.Changing the operation orientations of the power plants and electrical grid facilities; and 4.Increasing the rate of renewable energy use. - Highlights: • Maximization of the electricity liberalization benefits in Taiwan is studied. • A simulated-responsibility three-phase pricing method is proposed and its advantages are discussed. • Future liberalization of the electricity market in Taiwan is emphasized.

  1. MODELADO DEL PRECIO SPOT DE LA ELECTRICIDAD EN BRASIL USANDO UNA RED NEURONAL AUTORREGRESIVA ELECTRICITY SPOT PRICE MODELLING IN BRASIL USING AN AUTOREGRESSIVE NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Juan D Velásquez

    2008-12-01

    Full Text Available Una red neuronal autorregresiva es estimada para el precio mensual brasileño de corto plazo de la electricidad, la cual describe mejor la dinámica de los precios que un modelo lineal autorregresivo y que un perceptrón multicapa clásico que usan las mismas entradas y neuronas en la capa oculta. El modelo propuesto es especificado usando un procedimiento estadístico basado en el contraste del radio de verosimilitud. El modelo pasa una batería de pruebas de diagnóstico. El procedimiento de especificación propuesto permite seleccionar el número de unidades en la capa oculta y las entradas a la red neuronal, usando pruebas estadísticas que tienen en cuenta la cantidad de los datos y el ajuste del modelo a la serie de precios. La especificación del modelo final demuestra que el precio para el próximo mes es una función no lineal del precio actual, de la energía afluente actual y de la energía almacenada en el embalse equivalente en el mes actual y dos meses atrás.An autoregressive neural network model is estimated for the monthly Brazilian electricity spot price, which describes the prices dynamics better than a linear autoregressive model and a classical multilayer perceptron using the same input and neurons in the hidden layer. The proposed model is specified using a statistical procedure based on a likelihood ratio test. The model passes a battery of diagnostic tests. The proposed specification procedure allows us to select the number of units in hidden layer and the inputs to the neural network based on statistical tests, taking into account the number of data and the model fitting to the price time series. The final model specification demonstrates that the price for the next month is a nonlinear function of the current price, the current energy inflow, and the energy saved in the equivalent reservoir in the current month and two months ago.

  2. Transfer Pricing

    DEFF Research Database (Denmark)

    Nielsen, Søren Bo

    2014-01-01

    Against a background of rather mixed evidence about transfer pricing practices in multinational enterprises (MNEs) and varying attitudes on the part of tax authorities, this paper explores how multiple aims in transfer pricing can be pursued across four different transfer pricing regimes. A MNE h...

  3. Gold prices

    OpenAIRE

    Joseph G. Haubrich

    1998-01-01

    The price of gold commands attention because it serves as an indicator of general price stability or inflation. But gold is also a commodity, used in jewelry and by industry, so demand and supply affect its pricing and need to be considered when gold is a factor in monetary policy decisions.

  4. The Price-Anderson Act

    International Nuclear Information System (INIS)

    Jones, R.

    2000-01-01

    The Price-Anderson Act establishes nuclear liability law in the United States. First passed in 1957, it has influenced other nuclear liability legislation around the world. The insurer response the nuclear accident at Three Mile Island in 1979 demonstrates the application of the Act in a real life situation. The Price-Anderson Act is scheduled to be renewed in 2002, and the future use of commercial nuclear power in tge United States will be influenced by this renewal. (author)

  5. Estimating exponential scheduling preferences

    DEFF Research Database (Denmark)

    Hjorth, Katrine; Börjesson, Maria; Engelson, Leonid

    2015-01-01

    of car drivers' route and mode choice under uncertain travel times. Our analysis exposes some important methodological issues related to complex non-linear scheduling models: One issue is identifying the point in time where the marginal utility of being at the destination becomes larger than the marginal......Different assumptions about travelers' scheduling preferences yield different measures of the cost of travel time variability. Only few forms of scheduling preferences provide non-trivial measures which are additive over links in transport networks where link travel times are arbitrarily...... utility of being at the origin. Another issue is that models with the exponential marginal utility formulation suffer from empirical identification problems. Though our results are not decisive, they partly support the constant-affine specification, in which the value of travel time variability...

  6. A Monte-Carlo-Based Method for the Optimal Placement and Operation Scheduling of Sewer Mining Units in Urban Wastewater Networks

    Directory of Open Access Journals (Sweden)

    Eleftheria Psarrou

    2018-02-01

    Full Text Available Pressures on water resources, which have increased significantly nowadays mainly due to rapid urbanization, population growth and climate change impacts, necessitate the development of innovative wastewater treatment and reuse technologies. In this context, a mid-scale decentralized technology concerning wastewater reuse is that of sewer mining. It is based on extracting wastewater from a wastewater system, treating it on-site and producing recycled water applicable for non-potable uses. Despite the technology’s considerable benefits, several challenges hinder its implementation. Sewer mining disturbs biochemical processes inside sewers and affects hydrogen sulfide build-up, resulting in odor, corrosion and health-related problems. In this study, a tool for optimal sewer mining unit placement aiming to minimize hydrogen sulfide production is presented. The Monte-Carlo method coupled with the Environmental Protection Agency’s Storm Water Management Model (SWMM is used to conduct multiple simulations of the network. The network’s response when sewage is extracted from it is also examined. Additionally, the study deals with optimal pumping scheduling. The overall methodology is applied in a sewer network in Greece providing useful results. It can therefore assist in selecting appropriate locations for sewer mining implementation, with the focus on eliminating hydrogen sulfide-associated problems while simultaneously ensuring that higher water needs are satisfied.

  7. SPANR planning and scheduling

    Science.gov (United States)

    Freund, Richard F.; Braun, Tracy D.; Kussow, Matthew; Godfrey, Michael; Koyama, Terry

    2001-07-01

    SPANR (Schedule, Plan, Assess Networked Resources) is (i) a pre-run, off-line planning and (ii) a runtime, just-in-time scheduling mechanism. It is designed to support primarily commercial applications in that it optimizes throughput rather than individual jobs (unless they have highest priority). Thus it is a tool for a commercial production manager to maximize total work. First the SPANR Planner is presented showing the ability to do predictive 'what-if' planning. It can answer such questions as, (i) what is the overall effect of acquiring new hardware or (ii) what would be the effect of a different scheduler. The ability of the SPANR Planner to formulate in advance tree-trimming strategies is useful in several commercial applications, such as electronic design or pharmaceutical simulations. The SPANR Planner is demonstrated using a variety of benchmarks. The SPANR Runtime Scheduler (RS) is briefly presented. The SPANR RS can provide benefit for several commercial applications, such as airframe design and financial applications. Finally a design is shown whereby SPANR can provide scheduling advice to most resource management systems.

  8. An optimal power-dispatching system using neural networks for the electrochemical process of zinc depending on varying prices of electricity.

    Science.gov (United States)

    Yang, Chunhua; Deconinck, G; Gui, Weihua; Li, Yonggang

    2002-01-01

    Depending on varying prices of electricity, an optimal power-dispatching system (OPDS) is developed to minimize the cost of power consumption in the electrochemical process of zinc (EPZ). Due to the complexity of the EPZ, the main factors influencing the power consumption are determined by qualitative analysis, and a series of conditional experiments is conducted to acquire sufficient data, then two backpropagation neural networks are used to describe these relationships quantitatively. An equivalent Hopfield neural network is constructed to solve the optimization problem where a penalty function is introduced into the network energy function so as to meet the equality constraints, and inequality constraints are removed by alteration of the Sigmoid function. This OPDS was put into service in a smeltery in 1998. The cost of power consumption has decreased significantly, the total electrical energy consumption is reduced, and it is also beneficial to balancing the load of the power grid. The actual results show the effectiveness of the OPDS. This paper introduces a successful industrial application and mainly presents how to utilize neural networks to solve particular problems for the real world.

  9. Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

    Science.gov (United States)

    Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

    2018-03-01

    In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

  10. On Nonlinear Prices in Timed Automata

    Directory of Open Access Journals (Sweden)

    Devendra Bhave

    2016-12-01

    Full Text Available Priced timed automata provide a natural model for quantitative analysis of real-time systems and have been successfully applied in various scheduling and planning problems. The optimal reachability problem for linearly-priced timed automata is known to be PSPACE-complete. In this paper we investigate priced timed automata with more general prices and show that in the most general setting the optimal reachability problem is undecidable. We adapt and implement the construction of Audemard, Cimatti, Kornilowicz, and Sebastiani for non-linear priced timed automata using state-of-the-art theorem prover Z3 and present some preliminary results.

  11. Transfer Pricing

    DEFF Research Database (Denmark)

    Rohde, Carsten; Rossing, Christian Plesner

    trade internally as the units have to decide what prices should be paid for such inter-unit transfers. One important challenge is to uncover the consequences that different transfer prices have on the willingness in the organizational units to coordinate activities and trade internally. At the same time...... the determination of transfer price will affect the size of the profit or loss in the organizational units and thus have an impact on the evaluation of managers‟ performance. In some instances the determination of transfer prices may lead to a disagreement between coordination of the organizational units...

  12. Road pricing policy implementation

    NARCIS (Netherlands)

    Vonk Noordegraaf, D.M.

    2016-01-01

    Urban areas suffer from the negative externalities of road transport like congested road networks, air pollution and road traffic accidents. A measure to reduce these negative externalities is road pricing, meaning policies that impose direct charges on road use (Jones and Hervik, 1992). Since the

  13. Dynamic Oligopoly Pricing: Evidence from the Airline Industry

    OpenAIRE

    Siegert, Caspar; Ulbricht, Robert

    2014-01-01

    We explore how pricing dynamics in the European airline industry vary with the competitive environment. Our results highlight substantial variations in pricing dynamics that are consistent with a theory of intertemporal price discrimination. First, the rate at which prices increase towards the scheduled travel date is decreasing in competition, supporting the idea that competition restrains the ability of airlines to price-discriminate. Second, the sensitivity to competition is substantially ...

  14. 76 FR 4395 - Postal Service Price Adjustment

    Science.gov (United States)

    2011-01-25

    ... pricing design changes in First-Class Mail. One involves the introduction of two separate pricing... the value of the services the accounting fee supports and the goal of recovering institutional costs... INFORMATION: I. Introduction II. Class-Specific Summary III. Preferred Mail IV. Mail Classification Schedule...

  15. Cross-layer design for radio resource allocation based on priority scheduling in OFDMA wireless access network

    Directory of Open Access Journals (Sweden)

    Chen Yen-Wen

    2011-01-01

    Full Text Available Abstract The orthogonal frequency-division multiple access (OFDMA system has the advantages of flexible subcarrier allocation and adaptive modulation with respect to channel conditions. However, transmission overhead is required in each frame to broadcast the arrangement of radio resources to all mobile stations within the coverage of the same base station. This overhead greatly affects the utilization of valuable radio resources. In this paper, a cross layer scheme is proposed to reduce the number of traffic bursts at the downlink of an OFDMA wireless access network so that the overhead of the media access protocol (MAP field can be minimized. The proposed scheme considers the priorities and the channel conditions of quality of service (QoS traffic streams to arrange for them to be sent with minimum bursts in a heuristic manner. In addition, the trade-off between the degradation of the modulation level and the reduction of traffic bursts is investigated. Simulation results show that the proposed scheme can effectively reduce the traffic bursts and, therefore, increase resource utilization.

  16. Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads

    Directory of Open Access Journals (Sweden)

    Chong Chen

    2018-04-01

    Full Text Available In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP, and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL, are still imperfect, which make it difficult for the active distribution network (ADN to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified.

  17. Nonlinear Pricing in Energy and Environmental Markets

    Science.gov (United States)

    Ito, Koichiro

    This dissertation consists of three empirical studies on nonlinear pricing in energy and environmental markets. The first investigates how consumers respond to multi-tier nonlinear price schedules for residential electricity. Chapter 2 asks a similar research question for residential water pricing. Finally, I examine the effect of nonlinear financial rewards for energy conservation by applying a regression discontinuity design to a large-scale electricity rebate program that was implemented in California. Economic theory generally assumes that consumers respond to marginal prices when making economic decisions, but this assumption may not hold for complex price schedules. The chapter "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing" provides empirical evidence that consumers respond to average price rather than marginal price when faced with nonlinear electricity price schedules. Nonlinear price schedules, such as progressive income tax rates and multi-tier electricity prices, complicate economic decisions by creating multiple marginal prices for the same good. Evidence from laboratory experiments suggests that consumers facing such price schedules may respond to average price as a heuristic. I empirically test this prediction using field data by exploiting price variation across a spatial discontinuity in electric utility service areas. The territory border of two electric utilities lies within several city boundaries in southern California. As a result, nearly identical households experience substantially different nonlinear electricity price schedules. Using monthly household-level panel data from 1999 to 2008, I find strong evidence that consumers respond to average price rather than marginal or expected marginal price. I show that even though this sub-optimizing behavior has a minimal impact on individual welfare, it can critically alter the policy implications of nonlinear pricing. The second chapter " How Do

  18. Nonlinear Pricing in Markets with Interdependent Demand

    OpenAIRE

    Shmuel S. Oren; Stephen A. Smith; Robert B. Wilson

    1982-01-01

    This paper provides a mathematical framework for modeling demand and determining optimal price schedules in markets which have demand externalities and can sustain nonlinear pricing. These fundamental economic concepts appear in the marketplace in the form of mutual buyers' benefits and quantity discounts. The theory addressing these aspects is relevant to a wide variety of goods and services. Examples include tariffs for electronic communications services, pricing of franchises, and royalty ...

  19. Petroleum price

    International Nuclear Information System (INIS)

    Chevallier, B.

    2009-01-01

    The 'AFTP' conference on 'petroleum prices' organized by Total last March, tries to explain the different aspects of the crisis we undergo for July 2007 and its consequential effects on the petroleum markets (supply, demand evolvements, impacts on reserves, prices, refining...). (O.M.)

  20. Scheduling the scheduling task : a time management perspective on scheduling

    NARCIS (Netherlands)

    Larco Martinelli, J.A.; Wiers, V.C.S.; Fransoo, J.C.

    2013-01-01

    Time is the most critical resource at the disposal of schedulers. Hence, an adequate management of time from the schedulers may impact positively on the scheduler’s productivity and responsiveness to uncertain scheduling environments. This paper presents a field study of how schedulers make use of

  1. Harvesting and wood transport planning with SNAP III program (Scheduling and Network Analysis Program in a pine plantation in Southeast Brazil

    Directory of Open Access Journals (Sweden)

    Lopes Eduardo da Silva

    2003-01-01

    Full Text Available The objective of this study was to verify the potential of SNAP III (Scheduling and Network Analysis Program as a support tool for harvesting and wood transport planning in Brazil harvesting subsystem definition and establishment of a compatible route were assessed. Initially, machine operational and production costs were determined in seven subsystems for the study area, and quality indexes, construction and maintenance costs of forest roads were obtained and used as SNAP III program input data. The results showed, that three categories of forest road occurrence were observed in the study area: main, secondary and tertiary which, based on quality index, allowed a medium vehicle speed of about 41, 30 and 24 km/hours and a construction cost of about US$ 5,084.30, US$ 2,275.28 and US$ 1,650.00/km, respectively. The SNAP III program used as a support tool for the planning, was found to have a high potential tool in the harvesting and wood transport planning. The program was capable of defining efficiently, the harvesting subsystem on technical and economical basis, the best wood transport route and the forest road to be used in each period of the horizon planning.

  2. MPEG-compliant joint source/channel coding using discrete cosine transform and substream scheduling for visual communication over packet networks

    Science.gov (United States)

    Kim, Seong-Whan; Suthaharan, Shan; Lee, Heung-Kyu; Rao, K. R.

    2001-01-01

    Quality of Service (QoS)-guarantee in real-time communication for multimedia applications is significantly important. An architectural framework for multimedia networks based on substreams or flows is effectively exploited for combining source and channel coding for multimedia data. But the existing frame by frame approach which includes Moving Pictures Expert Group (MPEG) cannot be neglected because it is a standard. In this paper, first, we designed an MPEG transcoder which converts an MPEG coded stream into variable rate packet sequences to be used for our joint source/channel coding (JSCC) scheme. Second, we designed a classification scheme to partition the packet stream into multiple substreams which have their own QoS requirements. Finally, we designed a management (reservation and scheduling) scheme for substreams to support better perceptual video quality such as the bound of end-to-end jitter. We have shown that our JSCC scheme is better than two other two popular techniques by simulation and real video experiments on the TCP/IP environment.

  3. Gas prices and price process

    International Nuclear Information System (INIS)

    Groenewegen, G.G.

    1992-01-01

    On a conference (Gas for Europe in the 1990's) during the Gasexpo '91 the author held a speech of which the Dutch text is presented here. Attention is paid to the current European pricing methods (prices based on the costs of buying, transporting and distributing the natural gas and prices based on the market value, which is deducted from the prices of alternative fuels), and the transparency of the prices (lack of information on the way the prices are determined). Also attention is paid to the market signal transparency and gas-gas competition, which means a more or less free market of gas distribution. The risks of gas-to-gas competition for a long term price stability, investment policies and security of supply are discussed. Opposition against the Third Party Access (TPA), which is the program to implement gas-to-gas competition, is caused by the fear of natural gas companies for lower gas prices and lower profits. Finally attention is paid to government regulation and the activities of the European Commission (EC) in this matter. 1 fig., 6 ills., 1 tab

  4. A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

    Directory of Open Access Journals (Sweden)

    Jun Yang

    2016-08-01

    Full Text Available With the popularization of electric vehicles (EVs, the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU electricity price.

  5. Priced Timed Automata

    DEFF Research Database (Denmark)

    Behrmann, Gerd; Larsen, Kim Guldstrand; Rasmussen, Jacob Illum

    2004-01-01

    This contribution reports on the considerable effort made recently towards extending and applying well-established timed automata technology to optimal scheduling and planning problems. The effort of the authors in this direction has to a large extent been carried out as part of the European...... projects VHS [22] and AMETIST [17] and are available in the recently released UPPAAL CORA [12], a variant of the real-time verification tool UPPAAL [20,5] specialized for cost-optimal reachability for the extended model of priced timed automata....

  6. Price increase

    CERN Multimedia

    2006-01-01

    Please take note that after five years of stable prices at Restaurant No 1 a price increase will come into force on 1st January 2006. This increase has been agreed after discussions between the CSR (Comité de Surveillance des Restaurants) and the catering company Novae and will reflect the inflation rate of the last few years. In addition, a new children's menu will be introduced, as well as 'Max Havelaar' fair-trade coffee at a price of 1.70 CHF.

  7. Price increase

    CERN Multimedia

    2005-01-01

    Please take note that after five years of stable prices at Restaurant No 1 a price increase will come into force on 1st January 2006. This increase has been agreed after discussions between the CSR (Comité de Surveillance des Restaurants) and the catering company Novae and will reflect the inflation rate of the last few years. In addition, a new children's menu will be introduced as well as 'Max Havelaar' fair-trade coffee at a price of 1.70 CHF.

  8. Pricing Strategies for CD-ROM Products.

    Science.gov (United States)

    Rowley, J. E.

    1994-01-01

    Pricing strategies for subscriptions and licenses for CD-ROMs are different for single users and networks. The basic components of pricing strategies are charges for subscription, connect line, display/print, telecommunication, session rate, special commands, and special services. Highlights selected supplier pricing strategies for single users…

  9. 76 FR 77271 - Competitive Product Postal Price Changes

    Science.gov (United States)

    2011-12-12

    ... POSTAL REGULATORY COMMISSION [Docket No. CP2012-2; Order No. 997] Competitive Product Postal Price... recently-filed Postal Service request for a change in competitive products prices. The changes will take... and justification for the changes, the effective date, and a schedule of the changed rates. The price...

  10. Long-term home care scheduling

    DEFF Research Database (Denmark)

    Gamst, Mette; Jensen, Thomas Sejr

    In several countries, home care is provided for certain citizens living at home. The long-term home care scheduling problem is to generate work plans spanning several days such that a high quality of service is maintained and the overall cost is kept as low as possible. A solution to the problem...... provides detailed information on visits and visit times for each employee on each of the covered days. We propose a branch-and-price algorithm for the long-term home care scheduling problem. The pricing problem generates one-day plans for an employee, and the master problem merges the plans with respect...

  11. Freemium Pricing

    DEFF Research Database (Denmark)

    Runge, Julian; Wagner, Stefan; Claussen, Jörg

    Firms commonly run field experiments to improve their freemium pricing schemes. However, they often lack a framework for analysis that goes beyond directly measurable outcomes and focuses on longer term profit. We aim to fill this gap by structuring existing knowledge on freemium pricing...... into a stylized framework. We apply the proposed framework in the analysis of a field experiment that contrasts three variations of a freemium pricing scheme and comprises about 300,000 users of a software application. Our findings indicate that a reduction of free product features increases conversion as well...... as viral activity, but reduces usage – which is in line with the framework’s predictions. Additional back-of-the-envelope profit estimations suggest that managers were overly optimistic about positive externalities from usage and viral activity in their choice of pricing scheme, leading them to give too...

  12. Group Elevator Peak Scheduling Based on Robust Optimization Model

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2013-08-01

    Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.

  13. Understanding gasoline pricing in Canada

    International Nuclear Information System (INIS)

    2001-04-01

    This brochure is designed to help consumers understand how gasoline is priced and explained why prices increase, fluctuate and vary by location, city or region. The price of a litre of gasoline reflects the costs of crude oil, refining, retailing and taxes. Taxes are usually the largest single component of gasoline prices, averaging 40 to 50 per cent of the pump price. The cost of crude oil makes up another 35 to 45 per cent of the price. Refining costs make up 10 to 15 per cent while the remaining 5 to 10 per cent represents retail costs. Gasoline retailers make a profit of about 1 cent per litre. The latest network technology allows national and regional retail chains to constantly monitor price fluctuations to change their prices at gasoline stations at a moments notice to keep up with the competition and to protect their market shares. Several government studies, plus the Conference Board of Canada, have reported that competition is working in favour of Canadian motorists. This brochure also explained the drawbacks of regulating crude and pump prices with the reminder that crude prices were regulated in the 1970s with many negative consequences. 2 tabs., 1 fig

  14. Petroleum price

    International Nuclear Information System (INIS)

    Maurice, J.

    2001-01-01

    The oil market is the most volatile of all markets, with the exception of the Nasdaq. It is also the biggest commodity market in the world. Therefore one cannot avoid forecasting oil prices, nor can one expect to avoid the forecasting errors that have been made in the past. In his report, Joel Maurice draws a distinction between the short term and the medium-long term in analysing the outlook for oil prices. (author)

  15. A pre-analysis for the optimal operational scheduling of a pipeline network; Uma pre-analise do problema de otimizacao da programacao das operacoes de uma malha dutoviaria

    Energy Technology Data Exchange (ETDEWEB)

    Czaikowski, Daniel I.; Brondani, William M.; Arantes, Lucas G.; Boschetto, Suelen N.; Lueders, Ricardo; Magatao, Leandro; Stebel, Sergio L. [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil); Ribas, Paulo C. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas (CENPES)

    2008-07-01

    This work suggests a Pre-analysis in the input parameters of an optimization system (Bonacin et al., 2007; Boschetto et al., 2008). The proposed method is based on programming techniques that use lists of objects threaded, where objects are elements belonging to the same class, according to the concept of the object-oriented programming. The Preanalysis makes a previous evaluation of a batch sequencing, getting information to be entered into an optimization block. The continuous time Mixed Integer Linear Programming (MILP) model gets such information and determines the scheduling. The models are applied on a pipeline network that connects different areas including refineries, terminals, and final clients. Many oil derivatives (e.g. gasoline, liquefied petroleum gas, naphtha) can be sent or received in this network. The Pre-analysis objective is to reduce the computational time of an MILP model, and the proposed approach can aid the decision-making process to obtain a more detailed scheduling. (author)

  16. Estimating the Acquisition Price of Enshi Yulu Young Tea Shoots Using Near-Infrared Spectroscopy by the Back Propagation Artificial Neural Network Model in Conjunction with Backward Interval Partial Least Squares Algorithm

    Science.gov (United States)

    Wang, Sh.-P.; Gong, Z.-M.; Su, X.-Zh.; Liao, J.-Zh.

    2017-09-01

    Near infrared spectroscopy and the back propagation artificial neural network model in conjunction with backward interval partial least squares algorithm were used to estimate the purchasing price of Enshi yulu young tea shoots. The near-infrared spectra regions most relevant to the tea shoots price model (5700.5-5935.8, 7613.6-7848.9, 8091.8-8327.1, 8331-8566.2, 9287.5-9522.5, and 9526.6-9761.9 cm-1) were selected using backward interval partial least squares algorithm. The first five principal components that explained 99.96% of the variability in those selected spectral data were then used to calibrate the back propagation artificial neural tea shoots purchasing price model. The performance of this model (coefficient of determination for prediction 0.9724; root-mean-square error of prediction 4.727) was superior to those of the back propagation artificial neural model (coefficient of determination for prediction 0.8653, root-mean-square error of prediction 5.125) and the backward interval partial least squares model (coefficient of determination for prediction 0.5932, root-mean-square error of prediction 25.125). The acquisition price model with the combined backward interval partial least squares-back propagation artificial neural network algorithms can evaluate the price of Enshi yulu tea shoots accurately, quickly and objectively.

  17. Impacts of fuel oil substitution by natural gas in a pipeline network scheduling; Impactos da substituicao do oleo combustivel por gas natural na programacao de uma rede de dutos

    Energy Technology Data Exchange (ETDEWEB)

    Souza Filho, Erito M.; Bahiense, Laura; Ferreira Filho, Virgilio J.M. [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), RJ (Brazil)

    2012-07-01

    In recent decades, due to the advancement and computational methods for solving optimization problems, the number of articles addressing the scheduling of products has grown. The mathematical models developed have proved useful to schedule from a single pipeline with multiple products to complex networks of multiple pipelines. Moreover, the planning of these activities is of even greater importance when considering the existence of new environmental requirements to be applied to production and marketing of petroleum products. An example of this paradigm shift is the reduction in fuel oil consumption due to increased share of natural gas in the Brazilian energy matrix. In this context, this paper proposes a mathematical model to obtain feasible solutions for problems of scheduling a network of pipelines considering replacing all or part of the demand for fuel oil to natural gas. We tested the model on three real instances of a multi commodity network consists of 4 terminals, 4 refineries and 8 unidirectional pipelines, considering a planning horizon of one week. (author)

  18. GRASP (Greedy Randomized Adaptive Search Procedures) applied to optimization of petroleum products distribution in pipeline networks; GRASP (Greedy Randomized Adaptative Search Procedures) aplicado ao 'scheduling' de redes de distribuicao de petroleo e derivados

    Energy Technology Data Exchange (ETDEWEB)

    Conte, Viviane Cristhyne Bini; Arruda, Lucia Valeria Ramos de; Yamamoto, Lia [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil)

    2008-07-01

    Planning and scheduling of the pipeline network operations aim the most efficient use of the resources resulting in a better performance of the network. A petroleum distribution pipeline network is composed by refineries, sources and/or storage parks, connected by a set of pipelines, which operate the transportation of petroleum and derivatives among adjacent areas. In real scenes, this problem is considered a combinatorial problem, which has difficult solution, which makes necessary methodologies of the resolution that present low computational time. This work aims to get solutions that attempt the demands and minimize the number of batch fragmentations on the sent operations of products for the pipelines in a simplified model of a real network, through by application of the local search metaheuristic GRASP. GRASP does not depend of solutions of previous iterations and works in a random way so it allows the search for the solution in an ampler and diversified search space. GRASP utilization does not demand complex calculation, even the construction stage that requires more computational effort, which provides relative rapidity in the attainment of good solutions. GRASP application on the scheduling of the operations of this network presented feasible solutions in a low computational time. (author)

  19. Marketplace pricing

    International Nuclear Information System (INIS)

    Anon.

    1991-01-01

    As discussed in this chapter, interest in marketplace pricing has been increasing in recent years, reflecting the societal trend toward substituting competition for regulation where appropriate. Competition is valuable because it encourages utilities to make efficient decisions with a minimum of regulatory intervention. It enhances efficiency through the incentive for innovation by the regulated companies and by increasing the likelihood they will come forward with proposals for better services, lower prices or both. Ultimately, consumers are beneficiaries. Marketplace pricing is emblematic of the view that the degree of regulation should reflect the degree of market power, that workably competitive markets should be allowed to operate with as little regulatory interference as possible. The Edison Electric Institute has made perhaps the most detailed proposal on marketplace pricing. It and others perceive numerous benefits from this method of pricing transmission services. Given the undeniable market power resulting from line ownership, FERC has emphasized the need to find a workably competitive market before approving such proposals. The ability to make this distinction without a full-blown antitrust review for every transaction is questionable, and FERC has yet to provide generic guidance. Finally, FERC's legal ability to depart from cost-based standards is questionable

  20. Efficient Load Scheduling Method For Power Management

    Directory of Open Access Journals (Sweden)

    Vijo M Joy

    2015-08-01

    Full Text Available An efficient load scheduling method to meet varying power supply needs is presented in this paper. At peak load times the power generation system fails due to its instability. Traditionally we use load shedding process. In load shedding process disconnect the unnecessary and extra loads. The proposed method overcomes this problem by scheduling the load based on the requirement. Artificial neural networks are used for this optimal load scheduling process. For generate economic scheduling artificial neural network has been used because generation of power from each source is economically different. In this the total load required is the inputs of this network and the power generation from each source and power losses at the time of transmission are the output of the neural network. Training and programming of the artificial neural networks are done using MATLAB.

  1. Optimization of Trip-end Networks and Ride Price for Express Coach Systems in the High-speed Rail Era

    Directory of Open Access Journals (Sweden)

    Zhongzhen Yang

    2017-12-01

    Full Text Available Express coach (EC lost a considerable share of passengers after high-speed rail (HSR was implemented. This paper proposes a door-to-door operation mode for the EC system and builds a model to design an EC trip-end network in the origin city with the aim of maximizing the EC’s daily operating profit. A case study is undertaken, and the results show that the operating profit of the EC system first increases and then decreases with the growth of the trip-end routes. In the HSR era, door-to-door operation can effectively guarantee the market share and operating profits of the EC.

  2. PARTICAL SWARM OPTIMIZATION OF TASK SCHEDULING IN CLOUD COMPUTING

    OpenAIRE

    Payal Jaglan*, Chander Diwakar

    2016-01-01

    Resource provisioning and pricing modeling in cloud computing makes it an inevitable technology both on developer and consumer end. Easy accessibility of software and freedom of hardware configuration increase its demand in IT industry. It’s ability to provide a user-friendly environment, software independence, quality, pricing index and easy accessibility of infrastructure via internet. Task scheduling plays an important role in cloud computing systems. Task scheduling in cloud computing mea...

  3. A hybrid job-shop scheduling system

    Science.gov (United States)

    Hellingrath, Bernd; Robbach, Peter; Bayat-Sarmadi, Fahid; Marx, Andreas

    1992-01-01

    The intention of the scheduling system developed at the Fraunhofer-Institute for Material Flow and Logistics is the support of a scheduler working in a job-shop. Due to the existing requirements for a job-shop scheduling system the usage of flexible knowledge representation and processing techniques is necessary. Within this system the attempt was made to combine the advantages of symbolic AI-techniques with those of neural networks.

  4. Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model

    International Nuclear Information System (INIS)

    Koutroumanidis, Theodoros; Ioannou, Konstantinos; Arabatzis, Garyfallos

    2009-01-01

    Throughout history, energy resources have acquired a strategic significance for the economic growth and social welfare of any country. The large-scale oil crisis of 1973 coupled with various environmental protection issues, have led many countries to look for new, alternative energy sources. Biomass and fuelwood in particular, constitutes a major renewable energy source (RES) that can make a significant contribution, as a substitute for oil. This paper initially provides a description of the contribution of renewable energy sources to the production of electricity, and also examines the role of forests in the production of fuelwood in Greece. Following this, autoregressive integrated moving average (ARIMA) models, artificial neural networks (ANN) and a hybrid model are used to predict the future selling prices of the fuelwood (from broadleaved and coniferous species) produced by Greek state forest farms. The use of the ARIMA-ANN hybrid model provided the optimum prediction results, thus enabling decision-makers to proceed with a more rational planning for the production and fuelwood market. (author)

  5. House Price Prediction Using LSTM

    OpenAIRE

    Chen, Xiaochen; Wei, Lai; Xu, Jiaxin

    2017-01-01

    In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time...

  6. 48 CFR 915.404-4-71-5 - Fee schedules.

    Science.gov (United States)

    2010-10-01

    ... METHODS AND CONTRACT TYPES CONTRACTING BY NEGOTIATION Contract Pricing 915.404-4-71-5 Fee schedules. (a... subcontracting, normal contractor services performed by the government or another contractor: (1) The target fee...) The target fee schedule provides for 45 percent of the contract work to be subcontracted for such...

  7. Marginal pricing of transmission services. An analysis of cost recovery

    Energy Technology Data Exchange (ETDEWEB)

    Perez-Arriaga, I.J.., Rubio, F.J. [Instituto de Investigacion Technologica, Universidad Pontificia Comillas, Madrid (Spain); Puerta, J.F.; Arceluz, J.; Marin, J. [Unidad de Planificacion Estrategica, Iberdrola, Madrid (Spain)

    1996-12-31

    The authors present an in-depth analysis of network revenues that are computed with marginal pricing, and investigate the reasons why marginal prices in actual power systems fail to recover total incurred network costs. The major causes of the failure are identified and illustrated with numerical examples. The paper analyzes the regulatory implications of marginal network pricing in the context of competitive electricity markets and provides suggestions for the meaningful allocation of network costs among users. 5 figs., 9 tabs., 8 refs.

  8. Marginal pricing of transmission services. An analysis of cost recovery

    International Nuclear Information System (INIS)

    Perez-Arriaga, I.J.., Rubio, F.J.; Puerta, J.F.; Arceluz, J.; Marin, J.

    1996-01-01

    The authors present an in-depth analysis of network revenues that are computed with marginal pricing, and investigate the reasons why marginal prices in actual power systems fail to recover total incurred network costs. The major causes of the failure are identified and illustrated with numerical examples. The paper analyzes the regulatory implications of marginal network pricing in the context of competitive electricity markets and provides suggestions for the meaningful allocation of network costs among users. 5 figs., 9 tabs., 8 refs

  9. NASA policy on pricing shuttle launch services

    Science.gov (United States)

    Smith, J. M.

    1977-01-01

    The paper explains the rationale behind key elements of the pricing policy for STS, the major features of the non-government user policy, and some of the stimulating features of the policy which will open space to a wide range of new users. Attention is given to such major policy features as payment schedule, cost and standard services, the two phase pricing structure, optional services, shared flights, cancellation and postponement, and earnest money.

  10. Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

    DEFF Research Database (Denmark)

    Pindoriya, N.M.; Singh, Sri Niwas; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead...

  11. Electric power systems advanced forecasting techniques and optimal generation scheduling

    CERN Document Server

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  12. variances in consumers prices of selected food items among ...

    African Journals Online (AJOL)

    Admin

    the consumer prices of rice, beans and garri in the three markets; rice and garri had insignificant differences in ... and inappropriate response by farmers to price ... supply or demand side or both). .... road network, storage facilities, subsidized.

  13. Routing and scheduling problems

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander

    couple of decades. To deliver competitive service and price, transportation today needs to be cost effective. A company requiring for things to be shipped will aim at having the freight shipped as cheaply as possible while often satisfying certain time constraints. For the transportation company......, the effectiveness of the network is of importance aiming at satisfying as many costumer demands as possible at a low cost. Routing represent a path between locations such as an origin and destination for the object routed. Sometimes routing has a time dimension as well as the physical paths. This may...... set cost making the cost of the individual vehicle routes inter-dependant. Depending on the problem type, the size of the problems and time available for solving, different solution methods can be applicable. In this thesis both heuristic methods and several exact methods are investigated depending...

  14. Effects of series compensation on spot price power markets

    International Nuclear Information System (INIS)

    Shrestha, G.B.; Wang Feng

    2005-01-01

    The operation of a deregulated power market becomes more complex as the generation scheduling is dependent on suppliers' and consumers' bids. With large number of transactions in the power market changing in time, it is more likely for some transmission lines to face congestion. Series compensation, such as TCSC, with its ability to directly control the power flow can be very helpful to improve the operation of transmission networks. The effects of TCSC on the operation of a spot price power market are studied in this paper using the modified IEEE 14-bus system. Optimal Power Flow incorporating TCSC is used to implement the spot price market. Linear bids are used to model suppliers' and consumers' bids. Issues of location and cost of TCSC are discussed. The effects of levels of TCSC compensation on wide range of system quantities are studied. The effects on the total social benefit, the spot prices, transmission congestion, total generation and consumption, benefit to individual supplier and consumer etc. are discussed. It is demonstrated that though use of TCSC makes the system more efficient and augments competition in the market, it is not easy to establish general relationships between the levels of compensation and various market quantities. Simulation studies like these can be used to assess the effects of TCSC in specific systems. (Author)

  15. Artificial neural networks applied to the prediction of spot prices in the market of electric energy; Redes neurais artificiais aplicadas na previsao de precos spot no mercado de energia eletrica

    Energy Technology Data Exchange (ETDEWEB)

    Rodrigues, Alcantaro Lemes; Grimoni, Jose Aquiles Baesso [Univesidade de Sao Paulo (USP), SP (Brazil). Inst. de Eletrotecnica e Energia], emails: alcantaro@iee.usp.br, aquiles@iee.usp.br

    2010-07-01

    The commercialization of electricity in Brazil as well as in the world has undergone several changes over the past 20 years. In order to achieve an economic balance between supply and demand of the good called electricity, stakeholders in this market follow both rules set by society (government, companies and consumers) and set by the laws of nature (hydrology). To deal with such complex issues, various studies have been conducted in the area of computational heuristics. This work aims to develop a software to forecast spot market prices in using artificial neural networks (ANN). ANNs are widely used in various applications especially in computational heuristics, where non-linear systems have computational challenges difficult to overcome because of the effect named 'curse of dimensionality'. This effect is due to the fact that the current computational power is not enough to handle problems with such a high combination of variables. The challenge of forecasting prices depends on factors such as: (a) foresee the demand evolution (electric load); (b) the forecast of supply (reservoirs, hydrology and climate), capacity factor; and (c) the balance of the economy (pricing, auctions, foreign markets influence, economic policy, government budget and government policy). These factors are considered be used in the forecasting model for spot market prices and the results of its effectiveness are tested and huge presented. (author)

  16. Multiuser switched diversity scheduling schemes

    KAUST Repository

    Shaqfeh, Mohammad; Alnuweiri, Hussein M.; Alouini, Mohamed-Slim

    2012-01-01

    Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

  17. Multiuser switched diversity scheduling schemes

    KAUST Repository

    Shaqfeh, Mohammad

    2012-09-01

    Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

  18. Topology-based hierarchical scheduling using deficit round robin

    DEFF Research Database (Denmark)

    Yu, Hao; Yan, Ying; Berger, Michael Stubert

    2009-01-01

    according to the topology. The mapping process could be completed through the network management plane or by manual configuration. Based on the knowledge of the network, the scheduler can manage the traffic on behalf of other less advanced nodes, avoid potential traffic congestion, and provide flow...... protection and isolation. Comparisons between hierarchical scheduling, flow-based scheduling, and class-based scheduling schemes have been carried out under a symmetric tree topology. Results have shown that the hierarchical scheduling scheme provides better flow protection and isolation from attack...

  19. Platform pricing in matching markets

    NARCIS (Netherlands)

    Goos, M.; van Cayseele, P.; Willekens, B.

    2011-01-01

    This paper develops a simple model of monopoly platform pricing accounting for two pertinent features of matching markets. 1) The trading process is characterized by search and matching frictions implying limits to positive cross-side network effects and the presence of own-side congestion.

  20. Energy prices and taxes

    International Nuclear Information System (INIS)

    2004-01-01

    Energy Prices and Taxes contains a major international compilation of energy prices at all market levels: import prices, industry prices and consumer prices. The statistics cover main petroleum products, gas, coal and electricity, giving for imported products an average price both for importing country and country of origin. Every issue includes full notes on sources and methods and a description of price mechanisms in each country

  1. MARKET ECONOMICS PRICING PARTICULARS

    Directory of Open Access Journals (Sweden)

    V. I. Parshin

    2011-01-01

    Full Text Available The price performs several economic functions: accounting, stimulation, distribution, demand and offer balancing, serving as production site rational choice criterion, information. Most important pricing principles are: price scientific and purpose-aimed substantiation, single pricing and price control process. Pricing process factors are external, internal, basic (independent on money-market, market-determined and controlling. Different pricing methods and models are to be examined, recommendations on practical application of those chosen are to be written.

  2. Discount-Optimal Infinite Runs in Priced Timed Automata

    DEFF Research Database (Denmark)

    Fahrenberg, Uli; Larsen, Kim Guldstrand

    2009-01-01

    We introduce a new discounting semantics for priced timed automata. Discounting provides a way to model optimal-cost problems for infinite traces and has applications in optimal scheduling and other areas. In the discounting semantics, prices decrease exponentially, so that the contribution...

  3. Optimal scheduling of coproduction with a storage

    International Nuclear Information System (INIS)

    Ravn, H.F.; Rygard, J.M.

    1993-02-01

    We consider the problem of optimal scheduling of a system with combined heat and heat (CHP) units and a heat storege. The purpose of the heat storage is to permit a partial decoupling of the variations in the demand for heat and electrical power. We formulate the problem of optimal scheduling as that of minimizing the total costs over the planning period. The heat demand from the district heating system and the ''shadow prices'' for the electrical power system are taken as externally given parameters. The resulting model is solved by dynamic programming. We describe implementation details and we give examples of result of the optimization. (au) (12 refs.)

  4. Reduction of regional disparities in electric power prices by spatially effective measures and planning in the Federal Republic of Germany

    Energy Technology Data Exchange (ETDEWEB)

    Wagner, G

    1984-01-01

    For a long time energy policy has been trying to reduce disparities in electric power prices by administrative and financial measures alone. Governmental planning was opening up new prospects when long-range fuel transport - and in particular the transport of hard coal by ship or by rail - was seeing a drop in prices in the mid seventies. Since fuel transport has been lower-priced than the transport of equivalent quantities of electric power, regional disparities in electric power prices which are due to the respective supply structures may be levelled by way of power plant site selection and power plant installation according to the specific regional loads. A decentralized expansion of power generation within reach of the consumer requires but a minimum of wiring. Structural price disparities are reduced in particular in regions importing electric power at excessive prices. In addition, costs may be saved by rational energy utilization consisting above all in the application of dual-purpose power plants and by the rationalization of network infrastuctures. The study abstracted is part of a research project of the Federal Research Institute for Land Studies and Planning. The project is dealing with concepts of decentralized electric power supply and space heating. It adds to already existing related studies which deal above all with the basic problems of scheduling and planning the contents and inner structure of decentralized energy concepts.

  5. Immunization Schedules for Adults

    Science.gov (United States)

    ... ACIP Vaccination Recommendations Why Immunize? Vaccines: The Basics Immunization Schedule for Adults (19 Years of Age and ... diseases that can be prevented by vaccines . 2018 Immunization Schedule Recommended Vaccinations for Adults by Age and ...

  6. Instant Childhood Immunization Schedule

    Science.gov (United States)

    ... Recommendations Why Immunize? Vaccines: The Basics Instant Childhood Immunization Schedule Recommend on Facebook Tweet Share Compartir Get ... date. See Disclaimer for additional details. Based on Immunization Schedule for Children 0 through 6 Years of ...

  7. Integrated approach to transmission services pricing

    International Nuclear Information System (INIS)

    Yu, C.W.; David, A.K.

    1999-01-01

    The paper presents an intuitively logical split between: (a) embedded, (b) operating, and (c) expansion cost based pricing and methodologies for implementation, for transmission services. A conceptually straightforward mechanism for the equitable allocation of transmission network embedded cost recovery based on capacity-use and reliability benefit is proposed, expansion cost is charged on a long-run marginal cost basis and finally, operating cost recovery is based on short-run marginal pricing. This is followed by co-ordinating these alternatives and integrating the pricing mechanisms to achieve appropriate price signals for bulk power users of transmission systems. (author)

  8. Electricity prices differences between France and Germany

    International Nuclear Information System (INIS)

    Hensing, I.; Nolden, A.; Riechmann, Ch.; Schulz, W.

    1998-01-01

    High electricity prices in Germany especially as compared to France have played an important role in the electricity liberalization debate in Germany. The price differences can largely be explained by cost differences in electricity generation, the electricity grids, personnel cost and local taxes. Further analysis suggests that efficiency improvements upon market liberalization will only partly remove these price and cost differentials. Parts of the cost differentials are attributable to politically-motivated regulations and the (future) regulation of network functions. This implies that Germany can only expect to arrive at internationally comparable electricity prices if it advances with a reform of political and monopoly regulations alongside liberalizing electricity generation and trade. (author)

  9. Web Publishing Schedule

    Science.gov (United States)

    Section 207(f)(2) of the E-Gov Act requires federal agencies to develop an inventory and establish a schedule of information to be published on their Web sites, make those schedules available for public comment. To post the schedules on the web site.

  10. Preemptive scheduling with rejection

    NARCIS (Netherlands)

    Hoogeveen, H.; Skutella, M.; Woeginger, Gerhard

    2003-01-01

    We consider the problem of preemptively scheduling a set of n jobs on m (identical, uniformly related, or unrelated) parallel machines. The scheduler may reject a subset of the jobs and thereby incur job-dependent penalties for each rejected job, and he must construct a schedule for the remaining

  11. Preemptive scheduling with rejection

    NARCIS (Netherlands)

    Hoogeveen, J.A.; Skutella, M.; Woeginger, G.J.; Paterson, M.

    2000-01-01

    We consider the problem of preemptively scheduling a set of n jobs on m (identical, uniformly related, or unrelated) parallel machines. The scheduler may reject a subset of the jobs and thereby incur job-dependent penalties for each rejected job, and he must construct a schedule for the remaining

  12. Outage scheduling and implementation

    International Nuclear Information System (INIS)

    Allison, J.E.; Segall, P.; Smith, R.R.

    1986-01-01

    Successful preparation and implementation of an outage schedule and completion of scheduled and emergent work within an identified critical path time frame is a result of careful coordination by Operations, Work Control, Maintenance, Engineering, Planning and Administration and others. At the Fast Flux Test Facility (FFTF) careful planning has been responsible for meeting all scheduled outage critical paths

  13. Scheduling with Time Lags

    NARCIS (Netherlands)

    X. Zhang (Xiandong)

    2010-01-01

    textabstractScheduling is essential when activities need to be allocated to scarce resources over time. Motivated by the problem of scheduling barges along container terminals in the Port of Rotterdam, this thesis designs and analyzes algorithms for various on-line and off-line scheduling problems

  14. Three essays on access pricing

    Science.gov (United States)

    Sydee, Ahmed Nasim

    In the first essay, a theoretical model is developed to determine the time path of optimal access price in the telecommunications industry. Determining the optimal access price is an important issue in the economics of telecommunications. Setting a high access price discourages potential entrants; a low access price, on the other hand, amounts to confiscation of private property because the infrastructure already built by the incumbent is sunk. Furthermore, a low access price does not give the incumbent incentives to maintain the current network and to invest in new infrastructures. Much of the existing literature on access pricing suffers either from the limitations of a static framework or from the assumption that all costs are avoidable. The telecommunications industry is subject to high stranded costs and, therefore, to address this issue a dynamic model is imperative. This essay presents a dynamic model of one-way access pricing in which the compensation involved in deregulatory taking is formalized and then analyzed. The short run adjustment after deregulatory taking has occurred is carried out and discussed. The long run equilibrium is also analyzed. A time path for the Ramsey price is shown as the correct dynamic price of access. In the second essay, a theoretical model is developed to determine the time path of optimal access price for an infrastructure that is characterized by congestion and lumpy investment. Much of the theoretical literature on access pricing of infrastructure prescribes that the access price be set at the marginal cost of the infrastructure. In proposing this rule of access pricing, the conventional analysis assumes that infrastructure investments are infinitely divisible so that it makes sense to talk about the marginal cost of investment. Often it is the case that investments in infrastructure are lumpy and can only be made in large chunks, and this renders the marginal cost concept meaningless. In this essay, we formalize a model of

  15. Lift scheduling organization : Lift Concept for Lemminkainen

    OpenAIRE

    Mingalimov, Iurii

    2015-01-01

    The purpose of the work was to make a simple schedule for the main contractors and clients to check and control workflow connected with lifts. It gathers works with electricity, construction, engineering networks, installing equipment and commissioning works. The schedule was carried out during working on the building site Aino in Saint Petersburg in Lemminkӓinen. The duration of work was 5 months. The lift concept in Lemminkӓinen is very well controlled in comparison with other buil...

  16. Transfer Pricing: Is the Comparable Uncontrolled Price Method the Best Method in all Cases?

    Directory of Open Access Journals (Sweden)

    Pranvera Dalloshi

    2012-12-01

    Full Text Available The transfer price scope is becoming a very important issue for all companies that comprise from different departments or have a network of branches. These companies are obliged to present the way of price determination for transactions that they have with their branches or other relevant members of their network. The establishment of the multinational companies that develop their activities in various countries is being increased. It has increased the need to supervise their transactions and approval of laws and administrative orders that do not leave space for misuses. The paper is focused in the response to the question if the Comparable Uncontrolled Price Method is the best method to be used in all cases. It is presented through a concrete example that shows how the price of a product determined through the Comparable Uncontrolled Price Method or market price has an impact to the profit of the mother company and other subsidiaries.

  17. NASA scheduling technologies

    Science.gov (United States)

    Adair, Jerry R.

    1994-01-01

    This paper is a consolidated report on ten major planning and scheduling systems that have been developed by the National Aeronautics and Space Administration (NASA). A description of each system, its components, and how it could be potentially used in private industry is provided in this paper. The planning and scheduling technology represented by the systems ranges from activity based scheduling employing artificial intelligence (AI) techniques to constraint based, iterative repair scheduling. The space related application domains in which the systems have been deployed vary from Space Shuttle monitoring during launch countdown to long term Hubble Space Telescope (HST) scheduling. This paper also describes any correlation that may exist between the work done on different planning and scheduling systems. Finally, this paper documents the lessons learned from the work and research performed in planning and scheduling technology and describes the areas where future work will be conducted.

  18. Pricing and Trust

    DEFF Research Database (Denmark)

    Huck, Steffen; Ruchala, Gabriele K.; Tyran, Jean-Robert

    -competitive (monopolistic) markets. We then introduce a regulated intermediate price above the oligopoly price and below the monopoly price. The effect in monopolies is more or less in line with standard intuition. As price falls volume increases and so does quality, such that overall efficiency is raised by 50%. However......We experimentally examine the effects of flexible and fixed prices in markets for experience goods in which demand is driven by trust. With flexible prices, we observe low prices and high quality in competitive (oligopolistic) markets, and high prices coupled with low quality in non...

  19. 7 CFR 1000.50 - Class prices, component prices, and advanced pricing factors.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 9 2010-01-01 2009-01-01 true Class prices, component prices, and advanced pricing... advanced pricing factors. Class prices per hundredweight of milk containing 3.5 percent butterfat, component prices, and advanced pricing factors shall be as follows. The prices and pricing factors described...

  20. Competitive Pricing by a Price Leader

    OpenAIRE

    Abhik Roy; Dominique M. Hanssens; Jagmohan S. Raju

    1994-01-01

    We examine the problem of pricing in a market where one brand acts as a price leader. We develop a procedure to estimate a leader's price rule, which is optimal given a sales target objective, and allows for the inclusion of demand forecasts. We illustrate our estimation procedure by calibrating this optimal price rule for both the leader and the follower using data on past sales and prices from the mid-size sedan segment of the U.S. automobile market. Our results suggest that a leader-follow...