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A stochastic dynamic programming based model for uncertain production planning of re-manufacturing system

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2009年第13期47卷 3657-3668页

作者： Li, Congbo Liu, Fei Cao, Huajun Wang, Qiulian Chongqing Univ Inst Mfg Engn Coll Mech Engn Chongqing Peoples R China

Re-manufacturing is recycling by manufacturing as-good-as-new products from used products, often involving disassembly, cleaning, testing, part replacement/repair, and re-assembly operations. Production planning and inventory control is one of the most important research issues for re-manufacturing system, which are faced with a greater degree of uncertainty and complexity. This leads to a critical need for planning and control systems designed to deal with the added uncertainty and complexity. We formulate a stochastic dynamic programming based model to study the production planning, i.e. dynamic lot sizing problem, of re-manufacturing systems. In the model the demand and return amounts are stochastic over the finite planning horizon. The objective is to determine the quantities that have to be re-manufactured at each period in order to minimise the total cost, including re-manufacturing cost, holding cost for returns and re-manufactured products and backlog cost. The optimal production plan of the re-manufacturing system over a finite planning horizon can be obtained with the policy iteration method. In the end, a numerical example is performed to illustrate how the model is applied and to prove its feasibility.

关键词： re-manufacturing system uncertainty production planning stochastic dynamic programming

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Improving the Robustness of Reservoir Operations with stochastic dynamic programming

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2021年第7期147卷 04021030-04021030页

作者： Kim, Gi Joo Kim, Young-Oh Reed, Patrick M. Seoul Natl Univ Dept Civil & Environm Engn 1 Gwanak Ro Seoul 08826 South Korea Cornell Univ Sch Civil & Environm Engn Engn Ithaca NY 14853 USA

Reservoir operations should consider both adaptiveness and robustness to deal with two of the main characteristics of climate change: nonstationarity and deep uncertainty. In particular, robust operational strategies are distinguished from risk-neutral expected value optimization in the sense that they should be satisfactory over a wider range of uncertainty and improve the ability of a reservoir system to adapt to climate change. In this study, a new framework named robust stochastic dynamic programming (RSDP) is proposed that couples robust optimization (RO) with the formulations of objective function or constraints used in stochastic dynamic programming (SDP). Two main approaches of RO, namely feasibility robustness and solution robustness, are both considered in the optimization algorithm. Consequently, this study uses the Boryeong multipurpose dam to evaluate three SDP framings: conventional-SDP (CSDP), RSDP-feasibility robustness (RSDP-F), and RSDP-solution robustness (RSDP-S). These three SDP formulations were used to derive optimal monthly release rules for the Boryeong Dam, and their relative performances were evaluated using simulations of a broader range of inflow scenarios. The simulation-based re-evaluations of the resulting reservoir operational policies were quantified using a wide range of metrics that include reliability, resiliency, and vulnerability, as well as regret-based robustness metrics. The results of this study suggest that the RSDP-S model not only increases the range of possible solutions, but also yields more desirable operation outcomes under extreme climate conditions with respect to both traditional and robustness metrics.

关键词： Reservoir operation stochastic dynamic programming Robust optimization

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FUZZY INFERENCE SYSTEM IN ORDER SIZING DECISIONS: BENCHMARKING AGAINST stochastic dynamic programming APPROACHES

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2019年第4期26卷 435-451页

作者： Cunha, Bruna Wanke, Peter Correa, Henrique Chen, Zhongfei Univ Fed Rio de Janeiro COPPEAD Grad Business Sch Rio De Janeiro Brazil Rollins Coll Crummer Sch Business Winter Pk FL 32789 USA Jinan Univ Dept Econ Jinan Shandong Peoples R China

This paper presents two innovative ways of considering demand uncertainty in stock control. The first approaches uncertainty as a stochastic phenomenon, which we address by using a stochastic dynamic programming (SDP) model. The second approaches demand uncertainty as a fuzzy-type phenomenon, which we address using Fuzzy Inference System (FIS). Our focus is to explore the application of FIS to the definition of order sizes. The most relevant contribution of the paper is the proposition of a novel FIS to address and help develop, through a linguistic and intuitive approach, the difficulty of using traditional stochastic programming models to handle uncertainty issues. We also benchmarked our proposed FIS against a well-known and well-established SDP model to help evaluate our model. The cost evaluation method used was a 2D Fuzzy Monte Carlo Simulation, in which 10,540 demand scenarios were analyzed. Our results show that the SDP results in terms of total costs was comparable to the FIS model. We also found that the distributions of FIS decisions were similar to those of the SDP and that costs varied up to 40% from the optimal value. Thus, in a situation where it is either impossible or unfeasible to solve the problem using SDP, FIS can be utilized alternatively.

关键词： order sizing fuzzy inference system stochastic dynamic programming 2D fuzzy Monte Carlo simulation stock control

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Linking genetic algorithms with stochastic dynamic programming to the long-term operation of a multireservoir system

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WATER RESOURCES RESEARCH 2002年第12期38卷 40-1-40-9页

作者： Huang, WC Yuan, LC Lee, CM Natl Taiwan Ocean Univ Dept Harbor & River Engn Chilung 202 Taiwan

[1] The objective of this paper is to present a genetic algorithm-based stochastic dynamic programming (GA-based SDP) to cope with the dimensionality problem of a multiple-reservoir system. The joint long-term operation of a parallel reservoir system in the Feitsui and Shihmen reservoirs in northern Taiwan demonstrates the successful application of the proposed GA-based SDP model. Within the case study system it is believed that GA is a useful technique in supporting optimization. Though the employment of GA-based SDP may be time consuming as it proceeds through generation by generation, the model can overcome the "dimensionality curse'' in searching solutions. Simulation results show Feitsui's surplus water can be utilized efficiently to fill Shihmen's deficit water without affecting Feitsui's main purpose as Taipei city's water supply. The optimal joint operation suggests that Feitsui, on average, can provide 650,000 m(3)/day and 920,000 m(3)/day to Shihmen during the wet season and dry season, respectively.

关键词： genetic algorithms stochastic dynamic programming multireservoir system dimensionality water supply

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Evaluating Transition Probabilities for a stochastic dynamic programming Model Used in Water System Optimization

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2018年第2期144卷 04017090-04017090页

作者： Haguma, Didier Leconte, Robert Cote, Pascal Univ Sherbrooke Dept Civil Engn 2500 Blvd Univ Sherbrooke PQ J1K 2R1 Canada Rio Tinto Alcan 1954 Davis Jonquiere PQ G7S 4R5 Canada

stochastic dynamic programming is one of the most widely used optimization techniques for water system optimization. In this study, four methods for estimating transition probabilities have been evaluated to determine how they influence water system performance for short-term operating policies. The methods are counting, ordinary least-squares regression, robust linear model regression and multivariate conditional distribution. Two discretization schemesequal-width interval and equal-frequency and data transformationhave also been included in the study as sources of uncertainty. The study was carried out for three water systems: the Outardes River, Manicouagan River, and Lac Saint-Jean, located in Quebec, Canada. The results show that the water system configuration played a significant role in the performance of the transition probabilities. The discretization scheme and data transformation had a considerable influence on the counting and regression methods, whereas they had less of an impact on the multivariate conditional distribution. The robust linear models with equal-frequency discretization without data transformation gave satisfactory results for all the water systems. (c) 2017 American Society of Civil Engineers.

关键词： Water resources system stochastic dynamic programming Transition probabilities

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Designing an Optimum Acceptance Sampling Plan Using Bayesian Inferences and a stochastic dynamic programming Approach

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SCIENTIA IRANICA TRANSACTION E-INDUSTRIAL ENGINEERING 2009年第1期16卷 19-25页

作者： Niaki, S. T. Akhavan Nezhad, M. S. Fallah Sharif Univ Technol Dept Ind Engn Tehran Iran

In this paper, we use both stochastic dynamic programming and Bayesian inference concepts to design an optimum-acceptance-sampling-plan policy in quality control environments. To determine the optimum policy, we employ a combination of costs and risk functions in the objective function. Unlike previous studies, accepting or rejecting a batch are directly included in the action space of the proposed dynamic programming model. Using the posterior probability of the batch being in state p (the probability of non-conforming products), first, we formulate the problem into a stochastic dynamic programming model. Then, we derive some properties for the optimal value of the objective function, which enable us to search for the optimal policy that minimizes the ratio of the total discounted system cost to the discounted system correct choice probability.

关键词： Quality inspection Acceptance sampling plan Bayesian inference stochastic dynamic programming

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A Multi-Quantile Approach for Open-Loop stochastic dynamic programming Problems *

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IFAC Proceedings Volumes 2009年第2期42卷 52-57页

作者： Rodrigo T.N. Cardoso Ricardo H.C. Takahashi Frederico R.B. Cruz Carlos M. Fonseca Department of Physics and Mathematics Centro Federal de Educação Tecnológica de Minas Gerais MG Brazil Department of Mathematics Universidade Federal de Minas Gerais Department of Statistics Universidade Federal de Minas Gerais Faculty of Science and Technology Universidade do Algarve Portugal CEG-IST Instituto Superior Técnico Universidade Técnica de Lisboa Portugal

This paper proposes a multi-quantile approach for solving open-loop continuous-variable discrete-time stochastic dynamic programming problems in systems with non-standard probability distribution functions. Instead of using the expected value of the objective function for building the optimization criterion, the decision maker performs a choice on the decision variables over the objective function value quantiles. The proposed procedure relies on a Monte Carlo simulation of the unknown process input outcomes, associated with an open-loop multiobjective optimization. The optimal control comes from a trade-off analysis that considers, for instance, the risk associated with each policy versus its yield.

关键词： stochastic dynamic programming Multiobjective Optimization Model Predictive Control

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Valuation of an unexplored oilfield under uncertain oil price and reservoir condition: A stochastic dynamic programming approach with simulation-based reward function

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 223卷

作者： Pratikto, Fransiscus Indratno, Sapto Suryadi, Kadarsah Santoso, Djoko Bandung Inst Technol Ind Engn Dept Jl Ganesha 10 Bandung 40132 Indonesia Bandung Inst Technol Math Dept Jl Ganesha 10 Bandung 40132 Indonesia Bandung Inst Technol Geophys Engn Dept Jl Ganesha 10 Bandung 40132 Indonesia Parahyangan Catholic Univ Ind Engn Dept Jl Ciumbuleuit 94 Bandung 40141 Indonesia Bandung Inst Technol Univ Ctr Excellence Artificial Intelligence Vis Nat Language Proc & Big Data Analyt U CoE AI VLB Bandung 40132 West Java Indonesia

We develop an unexplored oilfield valuation model under uncertain exploration outcomes, reservoir conditions, and oil prices. The exploration outcomes follow a binomial process, while oil prices are represented using the Schwartz-Smith model. The reservoir conditions are characterized by the joint probability distribution of postdiscovery parameters estimated using a machine learning approach. The corresponding valuation model is a typical stochastic dynamic programming problem with a simulation-based reward function. The net cash flow lattice takes the form of a recombining quadrinomial derived from the discrete representation of the SchwartzSmith oil price model. We apply backward induction with embedded real-coded genetic algorithms to the net cash flow lattice to calculate the oilfield value. The model allows for field abandonment before lease expiration if the remaining reserve is uneconomical. To improve computational efficiency, we combine the Latin hypercube sampling and antithetic variates to reduce the variances. The model is implemented in an unexplored oilfield under two scenarios of oil presence probability: 100% and 75%. In the first scenario, we obtained a mean oilfield value of US$5.5 million with a CVaR of US$0.79 million, while in the second, we came up with a mean of -US $0.77 million and a CVaR of US$31.74 million.

关键词： Unexplored oilfield Valuation stochastic dynamic programming Simulation -based reward function Real -coded genetic algorithms

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An Equivalent Consumption Minimisation Strategy based on 1-Step Look-Ahead stochastic dynamic programming ∗

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IFAC-PapersOnLine 2015年第15期48卷 72-77页

作者： M. Fleuren T.C.J. Romijn M.C.F. Donkers Dept. of Elect. Eng. Eindhoven University of Technology Netherlands

In this paper, a systematic procedure to determine the equivalence factor in the equivalent consumption minimisation strategy (ECMS) is proposed. This is relevant when ECMS is not only used for controlling the power split between the internal combustion engine and the electric machine of a hybrid electric vehicle (HEV), but also for controlling several auxiliary systems. In this case, the number of controlled components and energy buffers increases, which causes the number of tunable equivalence factors to increase. The procedure to determine the equivalence factors proposed in this paper is based on the observation that ECMS can be considered as a time-invariant feedback policy and dynamic programming (DP) also yields a time-invariant feedback policy when the time horizon of the control problem approaches infinity and the disturbances are constant or absent. As the drive cycle can be considered as a (stationary stochastic) disturbance, we propose to formulate ECMS as the solution of a 1-step look-ahead stochastic dynamic program (1slSDP). This strategy results in an energy management strategy that performs close to optimal and yields similar fuel consumption results, when compared to a well-tuned ECMS. The absence of any parameters to be tuned and the fact that fuel consumption is similar to a well-tuned ECMS makes 1slSDP a useful strategy for energy management of HEVs.

关键词： Hybrid Electric Vehicle Energy Management stochastic dynamic programming Equivalent Consumption Minimisation Strategy

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On the use of Blackwell's approachability theorem for stochastic dynamic programming

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IFAC Proceedings Volumes 2004年第12期37卷 765-770页

作者： Hyeong Soo Chang Department of Computer Science and Engineering Sogang University Seoul Korea

We study solving large stochastic dynamic programming problems with simulation by using Blackwell’s approachability theorem to provide a rule of generating a (history-dependent) stochastic nonstationary policy from a given finite set of policies whose performance is asymptotically not worse than any policy in the set by a given error. We provide an analysis for almost sure convergence with an exponentially fast convergence rate.

关键词： Approachability theorem stochastic dynamic programming Sampling Learning algorithm

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