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2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023

作者： Mussawar, Osama Sleptchenko, Andrei Mayyas, Ahmad Khalifa University Department of Management Science and Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350323153

Uncertainties in our energy system, such as consumption and renewables, pose a challenge to system-wide planning. Deterministic approaches are thus insufficient for making techno-economically optimal decisions for the type, scale, and operations of energy supply and storage facilities. This work adopts a stochastic approach and develops a general multi-period optimization model of an energy system consisting of renewable and non-renewable energy supply sources, storage facilities, and uncertain parameters such as energy demand. It uses multi-stage stochastic programming to address multiple probabilistic scenarios for the stochastic parameters modeled as scenario trees. The model evaluates all the scenarios including the deterministic case and prescribes minimum cost options for the energy supply and storage capacities, and multi-period supply allocations to meet energy demand over a time horizon. The model enables assessment of the potential impacts of variations in demand on energy supply and storage costs and plans as demonstrated by the results of the numerical cases. © 2023 IEEE.

关键词： Energy storage Energy supply multistage stochastic programming Optimization modeling Planning Uncertainty

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Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties

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哈尔滨工程大学学报(英文版) 2025年第1期24卷 194-209页

作者： Lijun Liu Pu Cao Yajing zhou Zhixin Long Zuhua Jiang College of Mechanical&Electrical Engineering Shaanxi University of Science&TechnologyXi’an 710021China School of management Hefei University of TechnologyHefei 230009China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Ship outfitting is a key process in *** and high-quality ship outfitting is a top priority for modern *** activities are conducted at different stations of *** outfitting plan is one of the crucial issues in *** this paper,production scheduling and material ordering with endogenous uncertainty of the outfitting process are *** uncertain factors in outfitting equipment production are usually decision-related,which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to *** uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the *** address this problem,a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process.A practical case of the shipyard of China Merchants Heavy Industry Co.,*** used to evaluate the performance of the proposed *** results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.

关键词： Ship outfitting Production scheduling Purchase planning Endogenous uncertainty multistage stochastic programming

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Parallel and distributed computing for stochastic dual dynamic programming

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COMPUTATIONAL MANAGEMENT SCIENCE 2022年第2期19卷 199-226页

作者： Avila, D. Papavasiliou, A. Loehndorf, N. Catholic Univ Louvain Ctr Operat Res & Econometr Louvain Belgium Univ Luxembourg Luxembourg Ctr Logist & Supply Chain Management Esch Sur Alzette Luxembourg

We study different parallelization schemes for the stochastic dual dynamic programming (SDDP) algorithm. We propose a taxonomy for these parallel algorithms, which is based on the concept of parallelizing by scenario and parallelizing by node of the underlying stochastic process. We develop a synchronous and asynchronous version for each configuration. The parallelization strategy in the parallelscenario configuration aims at parallelizing the Monte Carlo sampling procedure in the forward pass of the SDDP algorithm, and thus generates a large number of supporting hyperplanes in parallel. On the other hand, the parallel-node strategy aims at building a single hyperplane of the dynamic programming value function in parallel. The considered algorithms are implemented using Julia and JuMP on a high performance computing cluster. We study the effectiveness of the methods in terms of achieving tight optimality gaps, as well as the scalability properties of the algorithms with respect to an increasing number of CPUs. In particular, we study the effects of the different parallelization strategies on performance when increasing the number of Monte Carlo samples in the forward pass, and demonstrate through numerical experiments that such an increase may be harmful. Our results indicate that a parallel-node strategy presents certain benefits as compared to a parallel-scenario configuration.

关键词： multistage stochastic programming stochastic dual dynamic programming High performance computing Distributed computing Parallel computing

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multistage stochastic programming in strategic telecommunication network planning

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COMPUTATIONAL MANAGEMENT SCIENCE 2012年第3期9卷 303-321页

作者： Eisenblatter, Andreas Schweiger, Jonas Atesio GmbH D-12161 Berlin Germany Zuse Inst Berlin Dept Optimizat Takustr 7 D-14195 Berlin Germany

Mobile communication is taken for granted in these days. Having started primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. Operators are facing the challenge to match the demand by continuously expanding and upgrading the network infrastructure. However, the evolution of the customer's demand is uncertain. We introduce a novel (long-term) network planning approach based on multistage stochastic programming, where demand evolution is considered as a stochastic process and the network is extended so as to maximize the expected profit. The approach proves capable of designing large-scale realistic UMTS networks with a time horizon of several years. Our mathematical optimization model, the solution approach, and computational results are presented.

关键词： UMTS Network evolution multistage stochastic programming

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STABILITY OF A CLASS OF RISK-AVERSE multistage stochastic PROGRAMS AND THEIR DISTRIBUTIONALLY ROBUST COUNTERPARTS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2021年第5期17卷 2415-2440页

作者： Jiang, Jie Chen, Zhiping Hu, He Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Xian Int Acad Math & Math Technol Ctr Optimizat Tech & Quantitat Finance Xian 710049 Shaanxi Peoples R China

In this paper, we consider the quantitative stability of a class of risk-averse multistage stochastic programs, whose objective functions are defined by multi-period pth order lower partial moments (LPM) with given targets, and their distributionally robust counterparts. We first derive the upper bounds of feasible solutions as preliminaries. Then, by employing calm modifications, the quantitative stability results are obtained under a special measurable perturbation of stochastic process, which extend the present results under risk-neutral cases to risk-averse ones. Moreover, we recast the risk-averse model by probability measures of stochastic process, and obtain new quantitative stability estimations on the basis of proper probability metrics under the general perturbation of stochastic process. Finally, motivated by the availability of only partial information about probability measures, we further consider the distributionally robust counterpart of our recasting model, and establish the discrepancy of optimal values with respect to the perturbation of ambiguity sets.

关键词： risk-averse quantitative stability LPM distributionally robust multistage stochastic programming

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MATHEMATICAL FOUNDATIONS OF DISTRIBUTIONALLY ROBUST multistage OPTIMIZATION

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SIAM JOURNAL ON OPTIMIZATION 2021年第4期31卷 3044-3067页

作者： Pichler, Alois Shapiro, Alexander Tech Univ Chemnitz Fak Math D-09111 Chemnitz Germany Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

Distributionally robust optimization involves various probability measures in its problem formulation. They can be bundled to constitute a risk functional. For this equivalence, risk functionals constitute a fundamental building block in distributionally robust stochastic programming. multistage programming requires conditional versions of risk functionals to reassess future risk after partial realizations and after preceding decisions. This paper discusses a construction of the conditional counterpart of a risk functional by passing its genuine characteristics to its conditional counterparts. It turns out that the conditional risk functionals could be different from the nested analogues of the original (law invariant) risk measure. We also discuss an implication to formulations of distributionally robust stochastic programming and a relation to stochastic games.

关键词： multistage stochastic programming distributional robustness conditional risk measures dynamic equations stochastic games rectangularity

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Gas storage valuation in incomplete markets

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期288卷 318-330页

作者： Loehndorf, Nils Wozabal, David Univ Luxembourg Luxembourg Ctr Logist & Supply Chain Management L-1511 Luxembourg Luxembourg Tech Univ Munich TUM Sch Management D-80333 Munich Germany

Natural gas storage valuation is an important problem in energy trading, yet most valuation approaches are based on heuristics or ignore that gas markets are incomplete. We propose an exact valuation model for incomplete gas markets based on multistage stochastic programming. Market incompleteness structurally changes the problem of storage valuation and asset backed trading and the resulting model requires analysis of a combined control problem of storage operation and futures trading that takes risk preferences into account. As the problem is subject to the curse of dimensionality, we reduce the stochastic process to a scenario lattice and solve the resulting problem using stochastic dual dynamic programming. We show that the intrinsic value of storage corresponds to the value under perfect risk aversion and that the rolling intrinsic value, which is popular among practitioners and has been found to be near-optimal when markets are complete, is an inconsistent price rule in incomplete markets. Our results inform managerial decisions on risk management and asset pricing for natural gas highlighting the importance of explicitly modeling risk preferences. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multistage stochastic programming Asset pricing Natural gas industry Markov processes

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A time-consistent Benders decomposition method for multistage distributionally robust stochastic optimization with a scenario tree structure

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2021年第1期79卷 67-99页

作者： Yu, Haodong Sun, Jie Wang, Yanjun Shanghai Lixin Univ Accounting & Finance Sch Stat & Math Shanghai Peoples R China Natl Univ Singapore Sch Business Singapore Singapore Shanghai Univ Finance & Econ Sch Math Shanghai Peoples R China

A computational method is developed for solving time consistent distributionally robust multistage stochastic linear programs with discrete distribution. The stochastic structure of the uncertain parameters is described by a scenario tree. At each node of this tree, an ambiguity set is defined by conditional moment constraints to guarantee time consistency. This method employs the idea of nested Benders decomposition that incorporates forward and backward steps. The backward steps solve some conic programming problems to approximate the cost-to-go function at each node, while the forward steps are used to generate additional trial points. A new framework of convergence analysis is developed to establish the global convergence of the approximation procedure, which does not depend on the assumption of polyhedral structure of the original problem. Numerical results of a practical inventory model are reported to demonstrate the effectiveness of the proposed method.

关键词： multistage stochastic programming Distributionally robust Scenario tree model Decomposition method

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multistage stochastic fractionated intensity modulated radiation therapy planning

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期160卷

作者： Bodur, Merve Cevik, Mucahit Cire, Andre A. Ruschin, Mark Wang, Juyoung Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada Univ Toronto Scarborough Dept Management Scarborough ON Canada Rotman Sch Management Toronto ON Canada Univ Toronto Sunnybrook Hlth Sci Ctr Dept Radiat Oncol Toronto ON Canada

Intensity modulated radiation therapy (IMRT) is a widely used cancer treatment technique designed to target malignant cells. To enhance its effectiveness on tumors and reduce side effects, radiotherapy plans are usually divided into consecutive treatments, or fractions, that are delivered over multiple weeks. However, typical planning approaches have focused on finding the full sequence of radiation intensities prior to the treatment, or were restricted to a single treatment session. In this work, we investigate the fractionated IMRT planning problem that accounts for geometric motion-related uncertainty during treatment. We propose a novel multistage stochastic programming (MSP) modeling framework that incorporates the sequential decision-making nature of the problem and prevailing stochasticity in cancer treatment. We model the uncertainty in the form of a scenario tree following the multiple instance of geometry approximation, and solve the MSP model using stochastic dual dynamic programming considering a variety of risk measures. We conduct computational experiments on five test cases that are generated based on clinical data. Through extensive simulations, we show that our MSP model generates higher quality treatment plans compared to deterministic and two-stage program counterparts based on multiple performance measures. In particular, our model leads to a higher rate of tumor coverage and a lower rate of radiation exposure for healthy tissues. Accordingly, the proposed MSP framework can greatly contribute to the clinical practice in fractionated IMRT.

关键词： Intensity modulated radiation therapy (IMRT) multistage stochastic programming Fractionation

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On complexity of multistage stochastic programs under heavy tailed distributions

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OPERATIONS RESEARCH LETTERS 2021年第2期49卷 265-269页

作者： Jiang, Jie Li, Shengjie Chongqing Univ Coll Math & Stat Chongqing Peoples R China

In this paper, the complexity of sample average approximation (SAA) of multistage stochastic programs under heavy tailed distributions is investigated. Specifically, we estimate confidence levels when the accuracy parameter and sample size are given under independently and identically distributed (iid) and non-iid conditional samples, respectively. Different from the existing works, we emphasize the impact of heavy tailed distributions, non-iid conditional sampling and stages dependence of the random process in multistage stochastic programs. (C) 2021 Elsevier B.V. All rights reserved.

关键词： multistage stochastic programming Non-iid Rate of convergence Heavy tailed distribution

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