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General Meeting of the IEEE-Power-and-Energy-Society

作者： Bezerra, Bernardo Veiga, Alvaro Barroso, Luiz Augusto Pereira, Mario Pontificia Univ Catolica Rio de Janeiro Rio de Janeiro Brazil PSR Rio De Janeiro Brazil

ISBN: (纸本)9781467327299

Hydrothermal systems optimal scheduling requires the representation of uncertainties in future inflows in order to hedge against adverse future low inflows by committing thermal plants, and also to store water in reservoirs while avoiding spillage when high future inflows occur. stochastic optimization technics has been widely used as a tool for long-term hydrothermal scheduling. These models rely on Monte Carlo simulation in order to capture the inflow uncertainty during the planning horizon. Since the parameters of these models are typically estimated from historical data, it is not surprising that the actual performance of a chosen reservoirs strategy often significantly differs from the designer's initial expectations due to unavoidable modeling ambiguity. The objective of this work is to assess the impact of inflow parameter uncertainty on the stochastic hydrothermal scheduling. The results presented in this work may be useful for the improvement of stochastic optimization techniques. The results presented show that the uncertainty on the parameters of the stochastic model consists on a supplementary source of risk that should be taken into account in the scheduling model.

关键词： stochastic dynamic programming parameter uncertainty hydro scheduling bootstrap

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Optimal Electric Energy Storage Operation

Optimal Electric Energy Storage Operation

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Qin, Junjie Sevlian, Raffi Varodayan, David Rajagopal, Ram Stanford Univ Inst Computat & Math Engn Stanford CA 94305 USA

ISBN: (纸本)9781467327299

Estimating the arbitrage value of storage is an important problem in power systems planning. Various studies have reported different values based numerical solutions of variations of a basic model. In this paper, we instead rely on a closed form solution for storage control. The closed form highlights the right type of forecasting that is required and allows large horizon problems to be solved. We study various scenarios and provide a simple methodology for evaluating the arbitrage value of storage.

关键词： Energy storage operation stochastic dynamic programming warehousing problem

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Injection Mold Replacement Analysis in Automotive Industry

Injection Mold Replacement Analysis in Automotive Industry

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Suwannabool, T. Sutivong, D. Chulalongkorn Univ Dept Ind Engn Bangkok 10330 Thailand

ISBN: (纸本)9781467329453

Injection mold replacement decisions in the automotive industry are usually made by managers and engineers, who based primarily on their own experience. This paper presents an injection mold replacement analysis through the use of stochastic dynamic programming and benchmark from the case study. The algorithm determines a optimal production volume and age that give the minimum expected cost under time constraint. The solutions are presented as a decision chart for easy application and interpretation.

关键词： Injection mold replacement analysis stochastic dynamic programming maintenance decision chart

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Evaluation of electric power procurement strategies by stochastic dynamic programming

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ELECTRICAL ENGINEERING IN JAPAN 2007年第2期160卷 20-29页

作者： Saisho, Yuichi Hayashi, Taketo Fujii, Yasumasa Yamaji, Kenji Univ Tokyo Tokyo Japan

In deregulated electricity markets, the role of a distribution company is to purchase electricity from the wholesale electricity market at randomly fluctuating prices and to provide it to its customers at a given fixed price. Therefore, the company has to take risk stemming from the uncertainties of electricity prices and/or demand fluctuation instead of the customers. The way to avoid the risk is to make a bilateral contract with generating companies or install its own power generation facility. This entails the necessity to develop a certain method to make an optimal strategy for electric power procurement. In such a circumstance, this research proposes a mathematical method based on stochastic dynamic programming and considers the characteristics of the start-up cost of an electric power generation facility to evaluate strategies of combination of the bilateral contract and power auto-generation with its own facility for procuring electric power in a deregulated electricity market. In the beginning we proposed two approaches to solve the stochastic dynamic programming, and they are a Monte Carlo simulation method and a finite difference method to derive the solution of a partial differential equation of the total procurement cost of electric power. Finally we discussed the influences of the prime uncertainty on optimal strategies of power procurement. (c) 2007 Wiley Periodicals, Inc.

关键词： electricity market uncertainty stochastic dynamic programming startup cost procurement cost

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An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

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COMPUTATIONAL MANAGEMENT SCIENCE 2012年第3期9卷 339-362页

作者： Webster, Mort Santen, Nidhi Parpas, Panos MIT Engn Syst Div 77 Massachusetts Ave Cambridge MA 02139 USA Imperial Coll London Dept Comp London SW7 2AZ England

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We showt hat increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R&D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that themagnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty.

关键词： Climate policy analysis Approximate dynamic programming Decision dependent uncertainty stochastic dynamic programming Endogenous uncertainty

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Planning water resources systems with interval stochastic dynamic programming

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WATER RESOURCES MANAGEMENT 2007年第6期21卷 997-1014页

作者： Luo, B. Maqsood, I. Huang, G. H. Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada

This study addresses water resources system planning problems with capacity expansion in an uncertain environment. An interval stochastic dynamic programming (SDP) model is presented, which is a hybrid of interval-number optimization and SDP. Besides the dynamic features of the model, it can incorporate and reflect uncertainties expressed as probability distribution functions and discrete intervals. The solution method for the proposed model is computationally effective, which makes it applicable to practical problems. The results acquired through a case study indicate that reasonable solutions have been obtained. They are further analyzed and interpreted for identifying significant factors that affect the system's performance. The information obtained through these post-optimality analyses can provide useful decision support for water authorities.

关键词： interval stochastic dynamic programming water resources planning uncertainty

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An asymptotically efficient simulation-based algorithm for finite horizon stochastic dynamic programming

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2007年第1期52卷 89-94页

作者： Chang, Hyeong Soo Fu, Michael C. Hu, Jiaqiao Marcus, Steven I. Sogang Univ Dept Comp Sci & Engn Seoul 121742 South Korea Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA Univ Maryland Syst Res Inst College Pk MD 20742 USA SUNY Stony Brook Dept Appl Math & Stat Stony Brook NY 11794 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

We present a simulation-based algorithm called "Simulated Annealing Multiplicative Weights" (SAMW) for solving large finite-horizon stochastic dynamic programming problems. At each iteration of the algorithm, a probability distribution over candidate policies is updated by a simple multiplicative weight rule, and with proper annealing of a control parameter, the generated sequence of distributions converges to a distribution concentrated only on the best policies. The algorithm is "asymptotically efficient," in the sense that for the goal of estimating the value of an optimal policy, a provably convergent finite-time upper bound for the sample mean is obtained.

关键词： learning algorithms Markov decision processes simulation simulated annealing stochastic dynamic programming

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THE STUDY OF A DETERIORATING MANUFACTURING SYSTEM USING SIMULATION AND RESPONSE METHODOLOGY

THE STUDY OF A DETERIORATING MANUFACTURING SYSTEM USING SIMU...

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24th European Modeling and Simulation Symposium (EMSS)

作者： Francie, Kouedeu Annie Jean-Pierre, Kenne Pierre, Dejax Victor, Songmene Univ Quebec Ecole Technol Super Dept Mech Engn Lab Integrated Prod Technol 1100 Notre Dame St West Montreal PQ H3C 1K3 Canada Univ Quebec Ecole Technol Super Dept Mech Engn Lab Integrated Prod Technol 1100 Notre Dame St West Montreal PQ H3C 1K3 Canada Ecole Mines Nantes Dept Automat Control & Ind Engn Optimizat Logist F-44307 Nantes 3 France

ISBN: (纸本)9788897999010;9788897999096

A deteriorating production system consisting of two parallel machines with the production dependent failure rates of the machine is investigated in this paper. The machines produce one type of final products. The demand rate for the final commodity is constant and unmet demand is backlogged. The goal of the control problem is to find the production rates of both machines so as to minimize a long term average expected cost which penalizes both the presence of waiting customers and the inventory. In the proposed model, the production rate of the first machine is higher than the production rate of the second machine. The failure rate of the first machine which is the main machine depends on its production rate. The failure rate of the second machine is constant. The proposed model is based on a Markov decision process, and the stochastic dynamic programming method is used to obtain the optimality conditions. Control policy parameters are obtained by combining analytical modelling, simulation experiments and response surface methodology. Sensitivity analyses of the optimal results with respect to the system parameters are also examined to illustrate the importance and effectiveness of the proposed methodology. The usefulness of the proposed approach is outlined for more complex situations where the system must deal with non-exponential failure and multiple machines.

关键词： production planning stochastic dynamic programming numerical methods simulation

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An integrated framework for freight forwarders: Exploitation of dynamic information for multimodal transportation

An integrated framework for freight forwarders: Exploitation...

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作者： Azadian, Farshid Wayne State University

学位级别：Ph.D.

Advent of real-time information broadcasting technologies, growth in demand for air-cargo, and increased congestion and variability on air-road network, are the main forces compelling today's air-freight forwarders to improve their operational decision-making to be more competitive and responsive to needs of customers. This research studies the air-cargo transportation on both road (short-haul) and air (long haul) network from the perspective of a mid-size freight forwarder. We develop a routing algorithm for congestion avoidance on air-network based on historical data and introduce an innovative approach to incorporate real-time information to enable dynamic routing of cargo on a stochastic air-network. In the road network, we introduce a new class of pickup and delivery problems to carry out the customer load pickups, fleet management, cargo-to-flight assignments, and airport deliveries in a multiple airport region under alternative access airport policy. The main contributions of this research to the air-cargo literature are the study of the value of real-time information and introduction of the concept of dynamic air-cargo routing. In addition, this is the first study that provides an operational framework to implement the alternative access airport policy. This research also contributes to operations research and logistics literature by introducing a new class of pickup and deliveries with time-sensitive and pair-dependent cost structure. It also contributes an innovative algorithm based on successive subproblem solving for Lagrangian decomposed mixed integer programming that shows to be efficient in obtaining near optimal solutions in reasonable time. The performances of the algorithms presented in this research are tested through experimental and real-world case studies. The results demonstrate that dynamic routing with real-time information can dramatically improve delivery reliability and reduce expected cost on the air-network. Moreover, they confirm that

关键词： Air-cargo Transportation Alternative Access Airports Intelligent Transportation Systems Multimodal Transportation Pickup and Delivery Problems stochastic dynamic programming

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Optimal Management of an Annual Weed: A stochastic dynamic programming Approach

Optimal Management of an Annual Weed: A Stochastic Dynamic P...

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International Congress on Modelling and Simulation (MODSIM07)

作者： Baxter, P. W. J. Wilcox, C. McCarthy, M. A. Possingham, H. P. Univ Queensland Ctr Ecol Appl Environm Decis Anal Ctr St Lucia Qld Australia Univ Queensland Australian Ctr Excellence Risk Anal Sch Integrat Biol St Lucia Qld Australia CSIRO Marine Res Pelag Fisheries & Ecosyst Hobart Tas Australia Univ Melbourne Sch Bot Australian Ctr Excellence Risk Anal Melbourne Vic Australia

ISBN: (纸本)9780975840047

Weeds are ecologically and economically disastrous. Invasive species in general are considered the greatest cause of global biodiversity loss after habitat destruction, and agricultural weeds cost the Australian economy $4 billion annually on average. Weed eradication is difficult. Managers must operate with limited budgets and search costs can increase as weed density decreases. Even if all plants are successfully removed, a seedbank may persist, leading to future outbreaks. Thus complete eradication is often an unrealistic target and the question becomes one of how much control is enough. The decision of what level of management resources to invest must not rely solely on management costs, but also take into account plant population dynamics in the context of a stochastic environment. Here we examine ongoing management of a contained annual weed with an established seedbank, which as a "sleeper" weed may yet escape and cause harm. Using stochastic dynamic programming, we find the optimal management effort of controlling the weed population, trading off expected costs of escape versus costs of searching and removal. The optimal removal effort increases non-linearly with the density of emerging plants until management becomes futile at high population densities. Most of the state space nevertheless recommends complete removal of emergent plants. The solution is most sensitive to population growth rate, the escape probability function and the relative costs of escape and management. Our simple model leads to valuable insights for the ongoing control of a sleeper weed. In our study we have assumed that immediate eradication is elusive. Nonetheless, continued long-term management may gradually deplete the seedbank and allow the possibility of eradication;thus the management time horizon (i.e., the length of the control program) can influence the optimal strategy. The net costs of management versus escape are also important whereas population dynamics - in this case the

关键词： Conservation efficiency invasive species management costs stochastic dynamic programming

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