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A two-stage stochastic programming approach for value-based closed-loop supply chain network design

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年第Sep.期105卷 1-17页

作者： Badri, Hossein Ghomi, S. M. T. Fatemi Hejazi, Taha-Hossein Amirkabir Univ Technol Dept Ind Engn Tehran Iran Amirkabir Univ Technol Dept Ind Engn Garmsar Campus Garmsar Iran

The ultimate goal for almost all companies in the current business world is to increase their value;therefore, managers concentrate their efforts on the control of value drivers. This research develops a two-stage stochastic programming model for the value-based supply chain network design, where all parts of a supply chain are configured and controlled in such a way that the total value of the company increases. The proposed model is a three-echelon, multi-commodity, and multi-period model for the tactical and strategic decision making. Also, a comprehensive computational analysis is conducted to evaluate the performance of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Closed-loop supply chain network design Value-based management two-stage stochastic programming

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Accelerated sample average approximation method for two-stage stochastic programming with binary first-stage variables

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APPLIED MATHEMATICAL MODELLING 2017年 41卷 582-595页

作者： Bidhandi, Hadi Mohammadi Patrick, Jonathan Islamic Azad Univ South Tehran Branch Fac Ind Engn Tehran Iran Univ Ottawa Telfer Sch Management Ottawa ON K1N 6N5 Canada

This paper proposes an accelerated solution method to solve two-stage stochastic programming problems with binary variables in the first stage and continuous variables in the second stage. To develop the solution method, an accelerated sample average approximation approach is combined with an accelerated Benders' decomposition algorithm. The accelerated sample average approximation approach improves the main structure of the original technique through the reduction in the number of mixed integer programming problems that need to be solved. Furthermore, the recently accelerated Benders' decomposition approach is utilized to expedite the solution time of the mixed integer programming problems. In order to examine the performance of the proposed solution method, the computational experiments are performed on developed stochastic supply chain network design problems. The computational results show that the accelerated solution method solves these problems efficiently. The synergy of the two accelerated approaches improves the computational procedure by an average factor of over 42%, and over 12% in comparison with the original and the recently modified methods, respectively. Moreover, the betterment of the computational process increases substantially with the size of the problem. (C) 2016 Elsevier Inc. All rights reserved.

关键词： two-stage stochastic programming Sample average approximation Mixed integer linear programming Benders' decomposition Supply chain network design

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two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty

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ENERGY 2016年第Part1期116卷 1145-1157页

作者： Huang, Yun-Hsun Wu, Jung-Hua Hsu, Yu-Ju Ind Technol Res Inst Ind Econ & Knowledge Ctr Hsinchu 310 Taiwan Natl Cheng Kung Univ Dept Resources Engn Tainan 701 Taiwan

Traditional electricity supply planning models regard the electricity demand as a deterministic parameter and require the total power output to satisfy the aggregate electricity demand. But in today's world, the electric system planners are facing tremendously complex environments full of uncertainties, where electricity demand is a key source of uncertainty. In addition, electricity demand patterns are considerably different for different regions. This paper developed a multi-region optimization model based on two-stage stochastic programming framework to incorporate the demand uncertainty. Furthermore, the decision tree method and Monte Carlo simulation approach are integrated into the model to simplify electricity demands in the form of nodes and determine the values and probabilities. The proposed model was successfully applied to a real case study (i.e. Taiwan's electricity sector) to show its applicability. Detail simulation results were presented and compared with those generated by a deterministic model. Finally, the long-term electricity development roadmap at a regional level could be provided on the basis of our simulation results. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multi-region optimization model two-stage stochastic programming Demand uncertainty Monte Carlo simulation

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A cross-decomposition scheme with integrated primal-dual multi-cuts for two-stage stochastic programming investment planning problems

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MATHEMATICAL programming 2016年第1期157卷 95-119页

作者： Mitra, Sumit Garcia-Herreros, Pablo Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

We describe a cross-decomposition algorithm that combines Benders and scenario-based Lagrangean decomposition for two-stage stochastic programming investment planning problems with complete recourse, where the first-stage variables are mixed-integer and the second-stage variables are continuous. The algorithm is a novel cross-decomposition scheme and fully integrates primal and dual information in terms of primal-dual multi-cuts added to the Benders and the Lagrangean master problems for each scenario. The potential benefits of the cross-decomposition scheme are demonstrated with numerical experiments on a number of instances of a facility location problem under disruptions. In the original formulation, where the underlying LP relaxation is weak, the cross-decomposition method outperforms multi-cut Benders decomposition. If the formulation is improved with the addition of tightening constraints, the performance of both decomposition methods improves but cross-decomposition clearly remains the best method for large-scale problems.

关键词： Cross-decomposition two-stage stochastic programming Investment planning

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A two-stage stochastic programming Approach Considering Risk Level for Distribution Networks Operation With Wind Power

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IEEE SYSTEMS JOURNAL 2016年第1期10卷 117-126页

作者： Tan, Yi Cao, Yijia Li, Canbing Li, Yong Zhou, Jinju Song, Yan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China State Grid Jiangxi Pingxiang Power Supply Co Pingxiang 337100 Peoples R China

Wind power is a challenge for active operation of distribution networks because of its stochastic nature. In this paper, a two-stage stochastic-programming-based optimal power flow model is proposed for the short-term operation (e.g., 1 h ahead) of distribution networks with wind power. In this model, the wind power is dispatched in the first stage, and then, in the second stage, not only the additional cost caused by the difference between dispatched wind power and actual wind power is considered into the recourse cost but also the operational risk is introduced to characterize the system security level. Both the number and severities of violations and near violations can be reflected by the risk index. Thus, the impacts of the wind power dispatch decision on the system operation regarding the realizations of wind power can be comprehensively evaluated. The 69-bus system is used to verify the effectiveness of the proposed approach. Simulation results show that the proposed approach can give a much lower risk level with a small increase in operational cost.

关键词： Active distribution networks optimal power flow (OPF) two-stage stochastic programming wind power

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Water resources management under dual uncertainties: a factorial fuzzy two-stage stochastic programming approach

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stochastic ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2016年第3期30卷 795-811页

作者： Zhou, Yang Huang, Gordon Wang, Shuo Zhai, Yuanyuan Xin, Xiaying Univ Regina Fac Engn Regina SK S4S 0A2 Canada Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada

In this study we propose a factorial fuzzy two-stage stochastic programming (FFTSP) approach to support water resources management under dual uncertainties. The dual uncertainties in terms of fuzziness in modeling parameters and variability of alpha-cut levels are taken into account. As different alpha-cut levels are assigned to each fuzzy parameter (instead of an identical alpha-cut level), the effects of alpha-cut levels on fuzzy parameters can be considered. Factorial analysis method is integrated with fuzzy vertex method to tackle the interactive effects of fuzzy parameters within a two-stage stochastic programming framework. The effects of the interactions among fuzzy parameters under various alpha-cut level combinations can be examined. The FFTSP approach is applied to a water resources management case to demonstrate its applicability. The results show that this approach can not only give various optimized solutions according to decision makers' confidence levels but also provide in-depth analyses for the effects of fuzzy parameters and their interactions on the solutions. In addition, the results show that the effects of diverse alpha-cut combinations should not be disregarded because the results may differ under some specific alpha-cut combinations. The dual sequential factorial analyses embedded in the FFTSP approach guarantee most variations in a system can be analyzed. Therefore water managers are able to gain sufficient knowledge to make robust decisions under uncertainty.

关键词： Water resources management Uncertainty Factorial analysis Fuzzy vertex technique two-stage stochastic programming Decision making

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two-stage stochastic programming for Transportation Network Design Problem 21st

Two-Stage Stochastic Programming for Transportation Network ...

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21st International Conference on Soft Computing (MENDEL)

作者： Hrabec, Dusan Popela, Pavel Roupec, Jan Mazal, Jan Stodola, Petr Brno Univ Technol Fac Mech Engn Tech 2 Brno 61669 Czech Republic Univ Def Brno Fac Mil Leadership Brno 66210 Czech Republic

ISBN: (纸本)9783319198248;9783319198231

The transportation network design problem is a well-known optimization problem with many practical applications. This paper deals with demand-based applications, where the operational as well as many other decisions are often made under uncertainty. Capturing the uncertain demand by using scenario-based approach, we formulate the two-stage stochastic mixed-integer linear problem, where the decision, which is made under uncertainty, of the first-stage program, is followed by the second-stage decision that reacts to the observed demand. Such a program may reach solvability limitations of algorithms for large scale real world data, so we refer to the so-called hybrid algorithm that combines a traditional optimization algorithm and a suitable genetic algorithm. The obtained results are presented in an explanatory form with the use of a sequence of figures.

关键词： two-stage stochastic programming Scenario-based approach Transportation model Network design problem Genetic algorithm Hybrid algorithm

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A two-stage stochastic programming model for value-based supply chain network design

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SCIENTIA IRANICA 2016年第1期23卷 348-360页

作者： Badri, H. Ghomi, S. M. T. Fatemi Hejazi, T. H. Amirkabir Univ Technol Dept Ind Engn 424 Hafez AvePOB *** Tehran Iran Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran

Nowadays, Value-based Supply Chain Management (VbSCM) is considered a resource of competitive advantages, and companies with long term strategic plans find the VbSCM an effective factor in sustainability. In this context, supply chain network design has a significant impact on all value drivers (i.e. sales, supply chain costs, fixed assets and working capital). This paper proposes a stochastic mixed integer linear programming model for a value-based supply chain network design in which decisions on physical flow (raw materials and finished products) and financial flow are integrated. The proposed model is designed for a four-echelon, multi-commodity, multi-period supply chain, and it maximizes the value of the company, based on the economic value-added concept, by making some strategic and tactical decisions affecting the value drivers. Furthermore, a scenario-based two-stage stochastic programming model is developed with a scenario generation method based on Nataf transformation. Also, a computational analysis is undertaken to illustrate the performance of the proposed approach. (C) 2016 Sharif University of Technology. All rights reserved.

关键词： Supply chain network design Value-based management two-stage stochastic programming Correlated parameters

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Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2016年第2期64卷 407-431页

作者： Stockbridge, Rebecca Bayraksan, Guzin Gen Motors Warren MI 48092 USA Ohio State Univ Dept Integrated Syst Engn Columbus OH 43210 USA

This paper presents a comparative computational study of the variance reduction techniques antithetic variates and Latin hypercube sampling when used for assessing solution quality in stochastic programming. Three Monte Carlo sampling-based procedures that provide point and interval estimators of optimality gap are considered: one that uses multiple replications, and two others with an alternative sample variance estimator that use single or two replications. Theoretical justification for using these alternative sampling techniques is discussed. In particular, we discuss asymptotic properties of the resulting estimators using Latin hypercube sampling for single-and two-replication procedures in detail. These theoretical considerations result in some subtle changes in the implementation of the procedures. A collection of two-stage stochastic linear test problems with different characteristics is used to empirically compare the three procedures for assessing solution quality with these variance reduction techniques.

关键词： Variance reduction Antithetic variates Latin hypercube sampling Optimality gap estimation two-stage stochastic programming Monte Carlo sampling

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An inexact mixed risk-aversion two-stage stochastic programming model for water resources management under uncertainty

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2015年第4期22卷 2964-2975页

作者： Li, W. Wang, B. Xie, Y. L. Huang, G. H. Liu, L. N China Elect Power Univ Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Dalhousie Univ Dept Civil & Resource Engn Halifax NS B3H 4R2 Canada

Uncertainties exist in the water resources system, while traditional two-stage stochastic programming is risk-neutral and compares the random variables (e.g., total benefit) to identify the best decisions. To deal with the risk issues, a risk-aversion inexact two-stage stochastic programming model is developed for water resources management under uncertainty. The model was a hybrid methodology of interval-parameter programming, conditional value-at-risk measure, and a general two-stage stochastic programming framework. The method extends on the traditional two-stage stochastic programming method by enabling uncertainties presented as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. It could not only provide information on the benefits of the allocation plan to the decision makers but also measure the extreme expected loss on the second-stage penalty cost. The developed model was applied to a hypothetical case of water resources management. Results showed that that could help managers generate feasible and balanced risk-aversion allocation plans, and analyze the trade-offs between system stability and economy.

关键词： two-stage stochastic programming Interval-parameter programming Risk-aversion Water allocation Uncertainty

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