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Robust multicriteria risk-averse stochastic programming models

ANNALS OF OPERATIONS RESEARCH 2017年第1-2期259卷 259-294页

作者： Liu, Xiao Kucukyavuz, Simge Noyan, Nilay Ohio State Univ Dept Integrated Syst Engn Columbus OH 43210 USA Univ Washington Ind & Syst Engn Seattle WA 98195 USA Sabanci Univ Ind Engn Program Istanbul Turkey

In this paper, we study risk-averse models for multicriteria optimization problems under uncertainty. We use a weighted sum-based scalarization and take a robust approach by considering a set of scalarization vectors to address the ambiguity and inconsistency in the relative weights of each criterion. We model the risk aversion of the decision makers via the concept of multivariate conditional value-at-risk (CVaR). First, we introduce a model that optimizes the worst-case multivariate CVaR and show that its optimal solution lies on a particular type of stochastic efficient frontier. To solve this model, we develop a finitely convergent delayed cut generation algorithm for finite probability spaces. We also show that the proposed model can be reformulated as a compact linear program under certain assumptions. In addition, for the cut generation problem, which is in general a mixed-integer program, we give a stronger formulation than the existing ones for the equiprobable case. Next, we observe that similar polyhedral enhancements are also useful for a related class of multivariate CVaR-constrained optimization problems that has attracted attention recently. In our computational study, we use a budget allocation application to benchmark our proposed maximin type risk-averse model against its risk-neutral counterpart and a related multivariate CVaR-constrained model. Finally, we illustrate the effectiveness of the proposed solution methods for both classes of models.

关键词： stochastic programming Risk aversion Robust optimization Multicriteria optimization stochastic Pareto optimality Conditional value-at-risk Cut generation Mixed-integer programming McCormick envelopes RLT technique

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Asset liability management for open pension schemes using multistage stochastic programming under Solvency-II-based regulatory constraints

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INSURANCE MATHEMATICS & ECONOMICS 2017年 77卷 177-188页

作者： Duarte, Thiago B. Valladao, Davi M. Veiga, Alvaro Superintendencia Seguros Privados Susep Ave Presidente Vargas 73010 Andar BR-20071900 Ctr Rio De Janeiro RJ Brazil Pontifical Catholic Univ Rio de Janeiro PUC Rio Ind Engn Dept Rua Marques Sao Vicente 225 BR-22451900 Gavea Rio De Janeiro RJ Brazil Pontifical Catholic Univ Rio de Janeiro PUC Rio Elect Engn Dept Rua Marques Sao Vicente 225 BR-22451900 Gavea Rio De Janeiro RJ Brazil

Open private pension schemes are subject to risk-based regulation. In this context, asset and liability management (ALM) frameworks for pension plan operators are increasingly based on multistage stochastic programming (MSP). The significant advances in MSP modeling notwithstanding, previous works ignore risk-based regulatory constraints such as those in the Solvency II Directive. In this work, we propose an ALM model for open pension schemes based on an MSP model with a thorough representation of a risk based regulation. Our proposal aims to define a dynamic optimal asset allocation including a detailed depiction of bond coupon payments, based on insolvency risk measures over a planning horizon. We present a realistic case study based on the Brazilian market, where the regulator imposes Solvency-II-compatible constraints on credit, underwriting, and operational risks. We develop a computationally tractable MSP model with explicit regulatory constraints, which induce risk aversion for even risk-neutral open pension plan operators. (C) 2017 Elsevier B.V. All rights reserved.

关键词： ALM stochastic programming Insolvency risk Supplementary pension Optimal allocation

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A stochastic programming Model for Decision-Making Concerning Medical Supply Location and Allocation in Disaster Management

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DISASTER MEDICINE AND PUBLIC HEALTH PREPAREDNESS 2017年第6期11卷 747-755页

作者： Khojasteh, Samad Barri Macit, Irfan Sakarya Univ Dept Ind Engn TR-54187 Serdivan Sakarya Turkey Cukurova Univ Dept Ind Engn Adana Turkey

We propose a stochastic programming model as a solution for optimizing the problem of locating and allocating medical supplies used in disaster management. To prepare for natural disasters, we developed a stochastic optimization approach to select the storage location of medical supplies and determine their inventory levels and to allocate each type of medical supply. Our model also captures disaster elaborations and possible effects of disasters by using a new classification for major earthquake scenarios. We present a case study for our model for the preparedness phase. As a case study, we applied our model to earthquake planning in Adana, Turkey. The experimental evaluations showed that the model is robust and effective.

关键词： disaster management natural disaster stochastic programming decision-making

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Estimation of Levy Processes via stochastic programming and Kalman Filtering

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2017年第4期19卷 1211-1225页

作者： Caruana, Mark Anthony Univ Malta Dept Stat & Operat Res Fac Sci Msida Malta

The estimation of Levy process has received a lot of attention in recent years. Evidence of this is the extensive amount of literature concerning this problem which can be classified in two categories: the nonparametric approach, and the parametric approach. In this paper, we shall concentrate on the latter, and in particular the parameters will be estimated within a stochastic programming framework. To be more specific, the first derivative of the characteristic function and its empirical version shall be used in objective function. Furthermore, the parameter estimates are recursively estimated by making use of a modified extended Kalman filter (MEKF). Some properties of the parameter estimates are studied. Finally, a number of simulations will be carried out and the results are presented and discussed.

关键词： Levy processes Kalman filter stochastic programming Characteristic function

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Assemble-to-Order Inventory Management via stochastic programming: Chained BOMs and the M-System

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PRODUCTION AND OPERATIONS MANAGEMENT 2017年第3期26卷 446-468页

作者： Dogru, Mustafa K. Reiman, Martin I. Wang, Qiong Verizon Commun One Verizon Way Basking Ridge NJ 07920 USA Columbia Univ Dept Ind Engn & Operat Res 500 West 120th St New York NY 10027 USA Univ Illinois Dept Ind & Enterprise Syst Engn 117 Transportat Bldg104 South Mathews Ave Urbana IL 61801 USA

We study an inventory management mechanism that uses two stochastic programs (SPs), the customary one-period assemble-to-order (ATO) model and its relaxation, to conceive control policies for dynamic ATO systems. We introduce a class of ATO systems, those that possess what we call a chained BOM. We prove that having a chained BOM is a sufficient condition for both SPs to be L? convex in the first-stage decision variables. We show by examples the necessity of the condition. For ATO systems with a chained BOM, our result implies that the optimal integer solutions of the SPs can be found efficiently, and thus expedites the calculation of control parameters. The M system is a representative chained BOM system with two components and three products. We show that in this special case, the SPs can be solved as a one-stage optimization problem. The allocation policy can also be reduced to simple, intuitive instructions, of which there are four distinct sets, one for each of four different parameter regions. We highlight the need for component reservation in one of these four regions. Our numerical studies demonstrate that achieving asymptotic optimality represents a significant advantage of the SP-based approach over alternative approaches. Our numerical comparisons also show that outside of the asymptotic regime, the SP-based approach has a commanding lead over the alternative policies. Our findings indicate that the SP-based approach is a promising inventory management strategy that warrants further development for more general systems and practical implementations.

关键词： assemble-to-Order stochastic programming discrete convexity bill of materials asymptotic optimality

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The Decomposition Method for Two-Stage stochastic Linear programming Problems with Quantile Criterion

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AUTOMATION AND REMOTE CONTROL 2018年第2期79卷 229-240页

作者： Zhenevskaya, I. D. Naumov, A. V. Moscow Aviat Inst Natl State Univ Moscow Russia

We consider the two-stage stochastic linear programming problem with quantile criterion in case when the vector of random parameters has a discrete distribution with a finite number of realizations. Based on the confi... 详细信息

关键词： stochastic programming two-stage problems linear programming quantile criterion decompositional algorithms

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A stochastic dynamic programming approach to analyze adaptation to climate change - Application to groundwater irrigation in India

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第3期265卷 1033-1045页

作者： Robert, Marion Bergez, Jacques-Eric Thomas, Alban Univ Fed Toulouse INRA AGIR INPT F-31326 Castanet Tolosan France Univ Toulouse INRA Toulouse Sch Econ F-31000 Toulouse France

Agricultural sustainability under climate change is a major challenge in semi-arid countries, mainly because of over-exploited water resources. This article explores short- and long-term consequences of farmers' adaptation decisions on groundwater resource use, under several climate change scenarios. We model farmer decisions on crop choice, investment in irrigation and water application rates, using a stochastic dynamic programming model with embedded year and season decision stages. Several sources of risk are considered that may impact farmer decisions, with poor rainfall affecting crop yield and market prices, while driving crop and borewell failure probabilities. We further investigate the performance of water management policies for groundwater resource conservation. This is achieved through policy simulations from a calibrated version of the stochastic dynamic model, using data from a field survey in the Berambadi watershed, Karnataka state, southern India. The most relevant and novel aspect of our model is the joint consideration of (i) investment decisions about irrigation over a long-term horizon and with the probability of borewell failure, (ii) several water management policies, and (iii) detailed farmers' water practices and the representation of crop choice for each agricultural season with crop failure. (C) 2017 Elsevier B.V. All rights reserved.

关键词： OR in agriculture stochastic programming OR in environment and climate change Strategic planning Scenarios

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A comparison of four approaches from stochastic programming for large-scale unit-commitment

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2017年第1-2期5卷 119-147页

作者： van Ackooij, Wim OSIRIS EDF R&D 1 Ave Gen Gaulle F-92141 Clamart France

In energy management, the unit-commitment problem deals with computing the most cost-efficient production schedule that meets customer load, while satisfying the operational constraints of the units. When the problem is large scale and/or much modelling detail is required, decomposition approaches are vital for solving this problem. The recent strong increase in intermittent, relative unforeseeable production has brought forth the need of examining methods from stochastic programming. In this paper we investigate and compare four such methods: probabilistically constrained programming, robust optimization and 2-stage stochastic and robust programming, on several large-scale instances from practice. The results show that the robust optimization approach is computationally the least costly but difficult to parameterize and has the highest recourse cost. The probabilistically constrained approach is second as computational cost is concerned and improves significantly the recourse cost functions with respect to the robust optimization approach. The 2-stage optimization approaches do poorly in terms of robustness, because the recourse decisions can compensate for this. Their total computational cost is highest. This leads to the insight that 2-stage flexibility and robustness can be (practically) orthogonal concepts.

关键词： Probabilistically constrained programming Robust optimization stochastic programming Unit-commitment

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stochastic programming with a Joint Chance Constraint Model for Reservoir Refill Operation Considering Flood Risk

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2017年第1期143卷 1-11页

作者： Xu, Bin Boyce, Scott E. Zhang, Yu Liu, Qiang Guo, Le Zhong, Ping-An Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China US Geol Survey Calif Water Sci Ctr 4165 Spruance RdSuite 200 San Diego CA 92101 USA Hohai Univ Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China China Yangtze Power Co Ltd 19 Jinrong St Beijing 100032 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China

Reservoir refill operation modeling attempts to maximize a set of benefits while minimizing risks. The benefits and risks can be in opposition to each other, such as having enough water for hydropower generation while leaving enough room for flood protection. In addition to multiple objects, the uncertainty of streamflow can make decision making difficult. This paper develops a stochastic optimization model for reservoir refill operation with the objective of maximizing the expected synthesized energy production for a cascade system of hydropower stations while considering flood risk. Streamflow uncertainty is addressed by discretized streamflow scenarios and flood risk is controlled by a joint chance constraint restricting the occurrence probability. With the variability of flood risk level, two advancing refill scenarios for exploring operation benefit are presented. Scenario I loosens the current stagewise storage bounds conditions and allows advancing reservoir refills but keeps the flood risk level the same as the refill policies obtained under the current storage bounds. Scenario II keeps the current storage bounds unchanged but allows increases in flood risk level. The proposed methodology is applied to the Xiluodu cascade system of reservoirs in China and investigates the optimal refill policies obtained by both scenarios. Compared with the benchmark obtained under the current storage bounds and lowest flood risk level, the results show (1) the synthesized energy production can be improved by 2.13% without changing the flood risk level under Scenario I, and (2) the synthesized energy production can also be increased by 0.21% at the expense of increasing the flood risk level by 4.4% when Scenario II is employed. As Scenario I produces higher benefit and lower risk than Scenario II, it is recommended to loosen the current stagewise storage bounds but to keep the flood risk level unchanged during refill operations. (C) 2016 American Society of Civil Engineers.

关键词： Reservoir refill operation Flood risk stochastic programming Joint chance constraint Progressive optimality algorithm

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Hybrid robust, stochastic and possibilistic programming for closed-loop supply chain network design

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 123卷 220-231页

作者： Dehghan, Ehsan Nikabadi, Mohsen Shafiei Amiri, Maghsoud Jabbarzadeh, Armin Semnan Univ Fac Econ & Management Dept Ind Management Semnan Iran Allameh Tabatabai Univ Fac Management & Accounting Dept Ind Management Tehran Iran IUST Dept Ind Engn Tehran Iran

The main goal of this study is to address gap in the area of Closed-loop Supply Chain Network Design (CLSCND) under the hybrid uncertain conditions. To do this, a multi-product and multi-period model is developed in an edible oil supply chain. Since the proposed model includes two kinds of uncertain parameters, the scenario- and fuzzy-based parameters, a novel Robust stochastic-Possibilistic programming (RSPP) are proposed to cope with uncertain parameters, based on the Me measure. Furthermore, the performance of the RSPP model is reviewed, its weaknesses and strengths are studied, and it is compared with the other models. Finally, the usefulness and applicability of the RSPP model are tested by the real industrial case study.

关键词： Mixed-integer programming Edible oil supply chain Closed loop supply chain network design Robust possibilistic programming stochastic programming

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