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Development of a Sequential Decision-Making Model for Controlling Multiple Air Pollutants Under stochastic Uncertainty

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WATER AIR AND SOIL POLLUTION 2012年第1期223卷 443-465页

作者： Lv, Ying Huang, Guohe Li, Yongping Yang, Zhifeng Sun, Wei N China Elect Power Univ MOE Key Lab Reg Energy Syst Optimizat SC Energy & Environm Res Acad Beijing 102206 Peoples R China Beijing Normal Univ Sch Environm Beijing 100875 Peoples R China Univ Regina Fac Engn Regina SK S4S 0A2 Canada

Most of previous programming methods for air-quality management merely considered single pollutant from point sources. However, air pollution control is characterized by multiple pollutants from various sources. Meanwhile, uncertain information in the decision-making process cannot be neglected in the real-world cases. Thus, an inexact multistage stochastic programming model with joint chance constraints based on the air quality index (air-quality management model with joint chance constraints (AQM-JCC)) is developed for controlling multiple pollutants deriving from point and mobile sources and applied to a regional air-quality management system. In the model, integrated air quality associated with the joint probability existing in terms of environmental constraints is evaluated;uncertainties expressed as probability distributions and interval values are addressed;risks of violating the overall air-quality target under joint chance constraints are examined;and dynamics of system uncertainties and decision processes under a complete set of scenarios within a multistage context are reflected. The results indicate that useful solutions for air quality management practices in sequential stochastic decision environments have been generated, which can help decision makers to identify cost-effective control strategies for overall air quality improvement under uncertainties.

关键词： Air quality Decision making Uncertainty multistage stochastic programming Joint chance constraint

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Optimal team deployment in urban search and rescue

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2012年第8期46卷 984-999页

作者： Chen, Lichun Miller-Hooks, Elise Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA

The problem of optimally deploying urban search and rescue (USAR) teams to disaster sites in post-disaster circumstances is formulated as a multistage stochastic program (MSP). A portion of sites requiring assistance arrive dynamically over the decision horizon and key problem characteristics are known only with uncertainty a priori. The problem seeks to identify a set of tours for USAR teams so as to maximize the total expected number of people that can be saved by attending to all or a subset of disaster sites within the disaster region. Decisions are taken dynamically over the decision horizon as situational awareness improves and survival likelihood diminishes with the aim of increasing the expected number of saved lives. To overcome the expensive computational effort associated with solving a MSP, a column generation-based strategy that consists of solving a series of interrelated two-stage stochastic programs with recourse within a shrinking time horizon is developed. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： multistage stochastic programming Two-stage stochastic program with recourse Column generation Disaster management Emergency response Dynamic routing

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Time consistency of dynamic risk measures

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OPERATIONS RESEARCH LETTERS 2012年第6期40卷 436-439页

作者： Shapiro, Alexander Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

In this paper we discuss time consistency of risk averse multistage stochastic programming problems. We show, in a framework of finite scenario trees, that composition of law invariant coherent risk measures can be la... 详细信息

关键词： multistage stochastic programming Risk averse optimization Dynamic programming Time consistency Coherent risk measures

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Scenario-based dynamic corporate bond portfolio management

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2012年第4期23卷 341-364页

作者： Beraldi, Patrizia De Simone, Francesco Violi, Antonio Consigli, Giorgio Iaquinta, Gaetano Univ Calabria Dept Elect Informat & Syst I-87030 Commenda Di Rende Italy Univ Bergamo Dept Math Stat & Comp Sci Bergamo Italy

The 2008 credit crisis has deeply affected the price of corporate liabilities in both equity and fixed income secondary markets leading to unprecedented portfolio losses by financial investors. A coordinated intervention by monetary institutions limited the systemic consequences of the crisis, without, however, avoiding a significant fall of corporate bond prices across international markets. In this article, we analyse alternative portfolio optimization approaches in the fixed income market over the 2008-2009 period, a time in which credit derivative markets became very illiquid. All policies are analysed relying on a unique set of market and credit scenarios generated by common and idiosyncratic risk factors on an extended investment universe. The crisis provides an interesting test period to analyse in particular the potential of dynamic versus static portfolio selection approaches. We also consider dynamic portfolio strategies based on multistage stochastic programming versus policy rule-based methods and analyse their relative performance against a corporate bond index widely adopted in practice as a market benchmark.

关键词： multistage stochastic programming scenario generation credit risk Monte Carlo simulation

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A mixed integer linear programming model for optimal sovereign debt issuance

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第3期214卷 749-758页

作者： Date, P. Canepa, A. Abdel-Jawad, M. Brunel Univ Ctr Anal Risk & Optimizat Modelling Applicat Dept Math Sci Uxbridge UB8 3PH Middx England

Governments borrow funds to finance the excess of cash payments or interest payments over receipts, usually by issuing fixed income debt and index-linked debt. The goal of this work is to propose a stochastic optimization-based approach to determine the composition of the portfolio issued over a series of government auctions for the fixed income debt, to minimize the cost of servicing debt while controlling risk and maintaining market liquidity. We show that this debt issuance problem can be modeled as a mixed integer linear programming problem with a receding horizon. The stochastic model for the interest rates is calibrated using a Kalman filter and the future interest rates are represented using a recombining trinomial lattice for the purpose of scenario-based optimization. The use of a latent factor interest rate model and a recombining lattice provides us with a realistic, yet very tractable scenario generator and allows us to do a multi-stage stochastic optimization involving integer variables on an ordinary desktop in a matter of seconds. This, in turn, facilitates frequent re-calibration of the interest rate model and re-optimization of the issuance throughout the budgetary year allows us to respond to the changes in the interest rate environment. We successfully demonstrate the utility of our approach by out-of-sample back-testing on the UK debt issuance data. (C) 2011 Elsevier B.V. All rights reserved.

关键词： multistage stochastic programming Public debt management OR in government Finance

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A model for optimization of process integration investments under uncertainty

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ENERGY 2011年第5期36卷 2733-2746页

作者： Svensson, Elin Stromberg, Ann-Brith Patriksson, Michael Chalmers Univ Technol Environm & Energy Dept SE-41296 Gothenburg Sweden Univ Gothenburg Dept Math Sci SE-41296 Gothenburg Sweden Chalmers Univ Technol Dept Math Sci SE-41296 Gothenburg Sweden

The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Process integration multistage stochastic programming Mixed-integer linear programming Scenario-based modelling Decision support Investment planning

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A multistage formulation for generation companies in a multi-auction electricity market

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2010年第2期21卷 165-181页

作者： Musmanno, Roberto Scordino, Nadia Triki, Chefi Violi, Antonio Univ Calabria Supercomp Ctr Computat Engn I-87036 Arcavacata Di Rende Italy Univ Salento Dept Math I-73100 Lecce Italy Univ Calabria Dept Elect Informat & Syst I-87036 Arcavacata Di Rende Italy

In this paper. we deal with the definition of a decision model for a producer operating in a multi-auction electricity market. The decisions to be taken concern the commitment of the generation plants and the quantity of enemy required to offer to each auction and to cover the bilateral contracts. We propose a multistage stochastic programming model in which the randomness of the clearing prices is represented by means of a scenario tree. The risk is modelled using a Conditional Value at Risk term in the objective function. Experimental results are reported to show the validity of our model and to discuss the influence of the risk parameters on the optimal value.

关键词： electricity market multistage stochastic programming bidding strategy Conditional Value at Risk

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Optimization of R&D project portfolios under endogenous uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第1期207卷 420-433页

作者： Solak, Senay Clarke, John-Paul B. Johnson, Ellis L. Barnes, Earl R. Univ Massachusetts Isenberg Sch Management Dept Finance & Operat Management Amherst MA 01003 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

Project portfolio management deals with the dynamic selection of research and development (R&D) projects and determination of resource allocations to these projects over a planning period. Given the uncertainties and resource limitations over the planning period, the objective is to maximize the expected total discounted return or the expectation of some other function for all projects over a long time horizon. We develop a detailed formal description of this problem and the corresponding decision process, and then model it as a multistage stochastic integer program with endogenous uncertainty. Accounting for this endogeneity, we propose an efficient solution approach for the resulting model, which involves the development of a formulation technique that is amenable to scenario decomposition. The proposed solution algorithm also includes an application of the sample average approximation method, where the sample problems are solved through Lagrangian relaxation and a new lower bounding heuristic. The performance of the overall solution procedure is demonstrated using several implementations of the proposed approach. (C) 2010 Elsevier B.V. All rights reserved.

关键词： OR in research and development Project portfolio Technology management R&D multistage stochastic programming Endogenous uncertainty

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Numerical evaluation of approximation methods in stochastic programming

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OPTIMIZATION 2010年第3期59卷 401-415页

作者： Kuechler, Christian Vigerske, Stefan Humboldt Univ Dept Math D-10099 Berlin Germany

We study an approach for the evaluation of approximation and solution methods for multistage linear stochastic programmes by measuring the performance of the obtained solutions on a set of out-of-sample scenarios. The main point of the approach is to restore the feasibility of solutions to an approximate problem along the out-of-sample scenarios. For this purpose, we consider and compare different feasibility and optimality based projection methods. With this at hand, we study the quality of solutions to different test models based on classical as well as recombining scenario trees.

关键词： multistage stochastic programming scenario tree out-of-sample evaluation

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Airport terminal capacity planning

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2009年第6期43卷 659-676页

作者： Solak, Senay Clarke, John-Paul B. Johnson, Ellis L. Univ Massachusetts Dept Finance & Operat Management Isenberg Sch Management Amherst MA 01003 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

The airport terminal capacity planning problem deals with determining the optimal design and expansion capacities for different areas of the terminal in the presence of uncertainty with regards to future demand levels and expansion costs. Analytical modeling of passenger flow in airport terminals under transient demand patterns is especially difficult due to the complex structure of a terminal. Because of this difficulty, the airport terminal capacity planning problem has not been studied in a holistic fashion, such that studies in this area either do not account for expandability or focus only on one particular area of the terminal. In this study, we consider the airport terminal capacity planning problem as a whole. In this regard, we first derive time functions to approximate maximum delays in passageways and processing stations of an airport terminal. We then use these delay functions to develop a multistage stochastic programming model based on a multicommodity flow network representation of the whole airport terminal. The solution of the model, for which we develop an efficient solution algorithm, provides optimal capacity requirements for each area in an airport terminal during the initial building phase, as well as the optimal expansion policy under stochastic future demand. The results of the study are applicable to all similar queuing networks, including other types of passenger terminals. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Passenger terminal design Airport planning Capacity expansion multistage stochastic programming

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