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Optimal transportation and shoreline infrastructure investment planning under a stochastic climate future

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2017年 100卷 156-174页

作者： Asadabadi, Ali Miller-Hook, Elise George Mason Univ Sid & Reva Dewberry Dept Civil Environm & Infrast Fairfax VA 22030 USA

This paper studies the problem of optimal long-term transportation investment planning to protect from and mitigate impacts of climate change on roadway performance. The problem of choosing the extent, specific system components, and timing of these investments over a long time horizon (e.g., 40-60 years) is modeled as a multi-stage, stochastic, bi-level, mixed-integer program wherein cost-effective investment decisions are taken in the upper level. The effects of possible episodic precipitation events on experienced travel delays are estimated from solution of a lower-level, traffic equilibrium problem. The episodic events and longer-term sea level changes exist on different time scales, making their integration a crucial element in model development. The optimal investment strategy is obtained at a Stackelberg equilibrium that is reached upon solution to the bilevel program. A recursive noisy genetic algorithm (rNGA), designed to address large-scale applications, is proposed for this purpose. The rNGA seeks the optimal combination of investment decisions to take now given only probabilistic information on the predicted sea level rise trend for a long planning horizon and associated likely extreme climatic events (in terms of their frequencies and intensities) that might arise over that planning period. The proposed solution method enables the evaluation of decisions concerning where, when and to what level to make infrastructure investments. The proposed rNGA has broad applicability to more general multi-stage, stochastic, bilevel, nonconvex, mixed integer programs that arise in many applications. The proposed solution methodology is demonstrated on an example representing a portion of the Washington, D.C. Greater Metropolitan area adjacent to the Potomac River. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Sea level rise Climate change multi-stage stochastic programming Resilience multi-temporal and multi-scale Noisy genetic algorithms

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Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年 101卷 176-200页

作者： Fattahi, Mohammad Govindan, Kannan Keyvanshokooh, Esmaeil Univ Southern Denmark Dept Technol & Innovat Ctr Engn Operat Management Campusvej 55 Odense Denmark Shahrood Univ Technol Sch Ind Engn & Management Shahrood Iran Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

We address a multi-period supply chain (SC) network design where demands of customers depend on facilities serving them based on their delivery lead-times. Potential customer demands are stochastic, and facilities' capacity varies randomly because of possible disruptions. Accordingly, we develop a multi-stage stochastic program, and model disruptions' effect on facilities' capacity. The SC responsiveness risk is limited and, to obtain a resilient network, both mitigation and contingency strategies are exploited. Computational results on a real-life case study and randomly generated problem instances demonstrate the model's applicability, risk-measurement policies' performance, and the influence of mitigation and contingency strategies on SC's resiliency. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Supply chain network design Risk management multi-stage stochastic programming Scenario reduction Mitigation and contingency strategies Disruption risk

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Optimization of the issuance of evacuation orders under evolving hurricane conditions

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2017年 95卷 285-304页

作者： Yi, Wenqi Nozick, Linda Davidson, Rachel Blanton, Brian Coile, Brian Cornell Univ Sch Civil & Environm Engn Ithaca NY 14850 USA Univ Delaware Dept Civil & Environm Engn Newark DE 19716 USA Univ North Carolina Chapel Hill Renaissance Comp Inst 100 Europa DrSuite 540 Chapel Hill NC 27517 USA SUNY Stony Brook Sch Marine & Atmospher Sci New York NY USA

This paper develops a bi-level programming model to optimize the issuance of evacuation orders with explicit consideration of (i) the highly uncertain evolution of the storm, and (ii) the complexity of the behavioral reaction to evolving storm conditions. A solution procedure based on progressive hedging is developed. A realistic case study for the eastern portion of the state of North Carolina is presented. Through the case study we demonstrate (1) the value of developing an evacuation order policy based on the evolution of the storm in contrast to a static policy;(2) the richness in the insights that can be provided by linking the behavioral models for evacuation decision-making with dynamic traffic assignment-based network flow models in a hurricane context;and (3) the computational promise of a progressive hedging-based solution procedure to solve large instances of the model. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Hurricane evacuation modeling Dynamic traffic assignment multi-stage stochastic programming Progressive hedging

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Manufacturing capacity planning and the value of multi-stage stochastic programming under Markovian demand

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2010年第3-4期22卷 143-162页

作者： Stephan, Holger A. Gschwind, Timo Minner, Stefan Univ Vienna Dept Business Adm Vienna Austria Johannes Gutenberg Univ Mainz Chair Logist Management Mainz Germany Daimler AG Prod Network Planning Trucks Stuttgart Germany

Capacity planning is a crucial part of global manufacturing strategies in the automotive industry, especially in the presence of volatile markets with high demand uncertainty. Capacity adjustments in machining intensive areas, e.g. body shop, paint shop, or aggregate machining face lead times exceeding a year, making an elaborated decision support indispensable. In this regard, two-stage stochastic programming is a frequently used framework to support capacity and flexibility decisions under uncertainty. However, it does not anticipate future capacity adjustment opportunities in response to market demand developments. Motivated by empirical findings from the automotive industry, we develop a multi-stage stochastic dynamic programming approach where the evolution of demand is represented by a Markov demand model. An efficient multi-stage solution algorithm is proposed and the benefits compared to a rolling horizon application of a two-stage approach are illustrated for different generic manufacturing networks. Especially network structures with limited flexibility might significantly benefit from applying a multi-stage framework.

关键词： Capacity planning multi-stage stochastic programming Markov demand model

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Electricity market operations under increasing uncertainty

Electricity market operations under increasing uncertainty

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作者： Farzaneh ABBASPOURTORBATI ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

学位级别：博士

Decision making in electricity markets under uncertainty has worldwide gained attention due to an increasing number of uncertain parameters associated to technology developments and market evolution. Hence, the market operator faces new challenges pertaining to technical and economic aspects of electricity markets. To tackle these challenges, appropriate models are necessary. This dissertation aims to analyze some of the challenges pertaining to the management in electricity markets under uncertainty and to provide the market operator with the models that enable it to make informed decisions in such uncertain market environments. In the context above, we categorize market operation problems into the following four groups. With the aim of obtaining informed day-ahead decisions in the presence of a number of intra- day markets and high renewable production, we propose a multi-stage stochastic clearing model, where the first stage represents the day-ahead market, n stages model n intra-day markets, and a final stage represents real-time operation. The proposed multi-stage clearing model considers not only different realizations of renewable power output, but also how these realizations evolve from day-ahead forecasts into real-time values, and allows flexibility for the contribution of renewable production in both the day-ahead and intra-day markets in form of scheduled productions and their adjustments. This improves the market outcomes and integration of renewable generation. With the purpose of obtaining marginal prices with cost-recovery features, we develop novel pricing methodologies in the presence of non-convexities and uncertainty in the market. These models minimize the duality gap of a stochastic non-convex clearing model and the dual problem of a relaxed version of this original model subject to primal constraints, dual constraints, cost-recovery constraints, and integrity constraints. The prices obtained deviate in the least possible manner from convention

关键词： Electricity Markets multi-stage stochastic programming Uncertainty Management Decision Making Pricing Schemes Flexible Demands Reserve Markets Renewable Production Intra-Day Markets Risk Nonconvexity Uplift Cost recovery Value of the stochastic Solution Informed Dayahead Decisions

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A hybrid scenario cluster decomposition algorithm for supply chain tactical planning under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第2期252卷 466-476页

作者： Zanjani, Masoumeh Kazemi Bajgiran, Omid Sanei Nourelfath, Mustapha Concordia Univ Dept Mech & Ind Engn Montreal PQ Canada Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada Univ Laval Dept Mech Engn Quebec City PQ Canada

We propose a Hybrid Scenario Cluster Decomposition (HSCD) heuristic for solving a large-scale multistage stochastic mixed-integer programming (MS-MIP) model corresponding to a supply chain tactical planning problem. The HSCD algorithm decomposes the original scenario tree into smaller sub-trees that share a certain number of predecessor nodes. Then, the MS-MIP model is decomposed into smaller scenario-cluster multi-stage stochastic sub-models coordinated by Lagrangian terms in their objective functions, in order to compensate the lack of non-anticipativity corresponding to common ancestor nodes of sub-trees. The sub-gradient algorithm is then implemented in order to guide the scenario-cluster sub models into an implementable solution. Moreover, a Variable Fixing Heuristic is embedded into the sub gradient algorithm in order to accelerate its convergence rate. Along with the possibility of parallelization, the HSCD algorithm provides the possibility of embedding various heuristics for solving scenario cluster sub-models. The algorithm is specialized to lumber supply chain tactical planning under demand and supply uncertainty. An ad-hoc heuristic, based on Lagrangian Relaxation, is proposed to solve each scenario-cluster sub-model. Our experimental results on a set of realistic-scale test cases reveal the efficiency of HSCD in terms of solution quality and computation time. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multi-stage stochastic programming Scenario decomposition Lagrangian Relaxation Sub-gradient method Variable Fixing Heuristic

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A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2010年第16期48卷 4701-4723页

作者： Zanjani, Masoumeh Kazemi Nourelfath, Mustapha Ait-Kadi, Daoud Univ Laval Fac Sci & Engn Dept Mech Engn Interuniv Res Ctr Enterprise Networks Logist & Tr Quebec City PQ Canada

Motivated by the challenges encountered in sawmill production planning, we study a multi-product, multi-period production planning problem with uncertainty in the quality of raw materials and consequently in processes yields, as well as uncertainty in products demands. As the demand and yield own different uncertain natures, they are modelled separately and then integrated. Demand uncertainty is considered as a dynamic stochastic data process during the planning horizon, which is modelled as a scenario tree. Each stage in the demand scenario tree corresponds to a cluster of time periods, for which the demand has a stationary behaviour. The uncertain yield is modelled as scenarios with stationary probability distributions during the planning horizon. Yield scenarios are then integrated in each node of the demand scenario tree, constituting a hybrid scenario tree. Based on the hybrid scenario tree for the uncertain yield and demand, a multi-stage stochastic programming (MSP) model is proposed which is full recourse for demand scenarios and simple recourse for yield scenarios. We conduct a case study with respect to a realistic scale sawmill. Numerical results indicate that the solution to the multi-stage stochastic model is far superior to the optimal solution to the mean-value deterministic and the two-stage stochastic models.

关键词： production planning random yield random demand sawmill scenario tree multi-stage stochastic programming

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Data Forwarding Scheme to Minimize End-to-End Delay in Opportunistic Networks 8

Data Forwarding Scheme to Minimize End-to-End Delay in Oppor...

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8th International Symposium on Telecommunications (IST)

作者： Arastouie, Narges Sabaei, Masoud Amirkabir Univ Technol Comp Engn & Informat Technol Tehran Iran

ISBN: (纸本)9781509034352

Opportunistic network can help in situations where network connectivity is sparse or with large variations in density, or even when there is no end-to-end connectivity by providing a communication solution for non-real-time applications. Unstable and fluctuate end-to-end path is let to have a significant challenge in routing messages. Uneven nodes' meeting can transmit the message to reach ultimate destination through the network without ever having an end-to-end connection. To cope with these conditions, optimal route in certain time window where the data is useful is very important. Making the best decision under non-deterministic and typically human-based environment, the undefined connection and meeting nodes, is our contribution in this paper. To our best knowledge, optimize stochastic model for routing message to gain the suitable delivery delay is the first time that the problem is addressed and it can be a valuable criterion to compare all previous models.

关键词： multi-stage stochastic programming opportunistic network delay ratio

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Optimal inventory control in a multi-period newsvendor problem with non-stationary demand

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ADVANCED ENGINEERING INFORMATICS 2015年第1期29卷 139-145页

作者： Kim, Gitae Wu, Kan Huang, Edward Nanyang Technol Univ Sch MAE Singapore 639798 Singapore George Mason Univ Syst Engn & Operat Res Dept Fairfax VA 22030 USA

The optimal control of inventory in supply chains plays a key role in the competiveness of a corporation. The inventory cost can account for half of company's logistics cost. The classical inventory models, e.g., newsvendor and EOQ models, assume either a single or infinite planning periods. However, these models may not be applied to perishable products which usually have a certain shelf life. To optimize the total logistic cost for perishable products, this paper presents a multi-period newsvendor model, and the problem is formulated as a multi-stage stochastic programming model with integer recourse decisions. We extend the progressive hedging method to solve the model efficiently. A numerical example and its sensitivity analysis are demonstrated. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Inventory models Newsvendor models multi-stage stochastic programming Progressive hedging methods

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Optimal annuity portfolio under inflation risk

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COMPUTATIONAL MANAGEMENT SCIENCE 2015年第3期12卷 461-488页

作者： Konicz, Agnieszka Karolina Pisinger, David Weissensteiner, Alex Tech Univ Denmark DTU Management Engn Management Sci Prod Storvet 426 DK-2800 Lyngby Denmark Free Univ Bolzano Bozen Sch Econ & Management I-39100 Bolzano Italy

The paper investigates the importance of inflation-linked annuities in retirement planning. Given nominal, inflation-linked, and variable annuities, as well as bonds and stocks, we search for optimal consumption and investment decisions under two different objective functions: (1) maximization of expected utility of real consumption, and (2) minimization of expected deviations from an inflation-adjusted target. When optimizing the objective, we allow for rebalancing the portfolio during retirement by buying additional annuities and by trading bonds and stocks. To find the optimal solution, we apply a multi-stage stochastic programming approach. Our findings indicate that independently of the considered objective function, real annuities are a crucial asset in every portfolio. In addition, without investing in real annuities, the retiree has to rebalance the portfolio more frequently, and still obtains a lower and more volatile real consumption.

关键词： Inflation-linked annuity Retirement planning CRRA utility Loss disutility multi-stage stochastic programming

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