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A Lagrangean decomposition approach for oil supply chain investment planning under uncertainty with risk considerations

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COMPUTERS & CHEMICAL ENGINEERING 2013年 50卷 184-195页

作者： Oliveira, F. Gupta, V. Hamacher, S. Grossmann, I. E. Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn BR-22451900 Rio De Janeiro RJ Brazil Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

We present a scenario decomposition framework based on Lagrangean decomposition for the multi-product, multi-period, supply investment planning problem considering network design and discrete capacity expansion under demand uncertainty. We also consider a risk measure that allows to reduce the probability of incurring in high costs while preserving the decomposable structure of the problem. To solve the resulting large-scale two-stage mixed-integer stochastic linear programming problem we propose a novel Lagrangean decomposition scheme, and compare different formulations for the non-anticipativity conditions. In addition, we present a new hybrid algorithm for updating the Lagrangean multiplier set based on the combination of cutting-plane, subgradient and trust-region strategies. Numerical results suggest that different formulations of the non-anticipativity conditions have a significant impact on the performance of the algorithm. Moreover, we observe that the proposed hybrid approach has superior performance in terms of faster computational times when compared with the traditional subgradient algorithm. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Oil and gas Supply chain investment planning stochastic integer programming Scenario decomposition Lagrangean decomposition Risk management

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Options analysis for long-term capacity design and operation of a lignocellulosic biomass refinery

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COMPUTERS & CHEMICAL ENGINEERING 2013年 58卷 178-202页

作者： Sharma, P. Romagnoli, J. A. Vlosky, R. Louisiana State Univ Dept Chem Engn Baton Rouge LA 70803 USA Louisiana State Univ Dept Renewable Nat Resources Baton Rouge LA 70803 USA

The growth of the lignocellulosic fuels has been hindered by technological and market uncertainty. This paper optimizes strategic investment decisions by prospective biobased fuel and chemical enterprises. A real options-based stochastic integer programming model is developed in this paper. We model a hypothetical, vertically integrated lignocellulosic enterprise that produces cellulosic ethanol and biosuccinic acid. Uncertainty is represented in bioproduct demands and prices. Strategic options including investment in research and development, investments in a flexible production platform and deferral of project investment are modeled. A hypothetical market model is also developed to correlate crude oil prices with the evolution of bioproduct markets. The discounted value of equity free cash flows is optimized. The optimal results include multiple capacity design plans based on the long term evolution of bioproduct markets. Monte Carlo simulations are also conducted to quantify the risk adjusted NPV's and returns on investment for the optimal capacity design trajectories. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Real options stochastic integer programming Multi-product biorefineries Uncertainty Lignocellulosic biomass

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Totally unimodular stochastic programs

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MATHEMATICAL programming 2013年第1-2期138卷 1-13页

作者： Kong, Nan Schaefer, Andrew J. Ahmed, Shabbir Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

We consider totally unimodular (TU) stochastic programs, that is, two-stage stochastic programs whose extensive-form constraint matrix is TU. We generalize the notion of total unimodularity to apply to sets of matrices and provide properties of such sets. We provide several sufficient conditions on stochastic programs to be TU. When solving TU stochastic problems using the L-shaped method, it is not clear whether the integrality restrictions should be imposed on the master problem. Such restrictions will make each master problem more difficult to solve. On the other hand, solving the linear relaxation of the master typically means sending fractional (and unlikely optimal) solutions to the subproblems, perhaps leading to more iterations. Our computational results investigate this trade-off and provide insight into which strategy is preferable.

关键词： stochastic integer programming Total unimodularity L-shaped method

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stochastic survivable network design problems

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Electronic Notes in Discrete Mathematics 2013年 41卷 245-252页

作者： Ljubić, Ivana Mutzel, Petra Zey, Bernd Department of Statistics and Operations Research University of Vienna Vienna Austria Department of Computer Science TU Dortmund Dortmund Germany

In this paper we introduce survivable network design problems under a two-stage stochastic model with fixed recourse and finitely many scenarios. We propose a new cut-based formulation based on orientation properties which is stronger than the undirected cut-based model. We use a two-stage branch&cut algorithm for solving the decomposed model to provable optimality. In order to accelerate the computations, we suggest a new cut strengthening technique for the decomposed L-shaped optimality cuts that is computationally fast and easy to implement. © 2013 Elsevier B.V.

关键词： Benders decomposition Branch-and-cut stochastic integer programming stochastic survivable network design

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A two-phase approach to scheduling multi-category outpatient appointments - A case study of a women's clinic

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HEALTH CARE MANAGEMENT SCIENCE 2013年第3期16卷 197-216页

作者： Qu, Xiuli Peng, Yidong Kong, Nan Shi, Jing N Carolina Agr & Tech State Univ Dept Ind & Syst Engn Greensboro NC 27411 USA N Dakota State Univ Dept Ind & Mfg Engn Fargo ND 58102 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA

In this paper, we propose a two-phase approach for designing a weekly scheduling template for outpatient clinics providing multiple types of services. In many outpatient clinics, various service types are categorized to address the operational challenge of substantial changeover time between certain pairs of services. In the first phase of our approach, a mixed-integer program is formulated to assign service categories to clinic sessions during a week and determine the optimal number of appointments reserved for each service type in each clinic session. The objective in the first phase is to balance the workload of the providers among clinic sessions. In the second phase, a stochastic mixed-integer program is formulated for each clinic session to assign each contained appointment with a starting time based on several time-based performance measures. To solve the formulated stochastic program, we develop a Monte Carlo sampling based genetic algorithm. The two-phase approach is tested numerically with cases derived from a real women's clinic. Our results demonstrate that the two-phase approach can efficiently find promising weekly appointment scheduling templates for outpatient clinics. In addition, our results suggest that the best suboptimal scheduling templates found become more sensitive to the weighting coefficients of the time-based measures as the provider workload increases.

关键词： Appointment Scheduling Outpatient Clinics stochastic integer programming Monte Carlo Sampling Genetic Algorithms

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The DNA genetic algorithm applied for solving stochastic integer programming expected value models

The DNA genetic algorithm applied for solving stochastic int...

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7th International Conference on Machine Learning and Cybernetics

作者： Wang, Ming-Chun Tang, Wan-Sheng Liu, Xin Tianjin Univ Syst Engn Inst Tianjin 300072 Peoples R China Tianjin Univ Technol & Educ Tianjin 300072 Peoples R China

ISBN: (纸本)9781424420957

In this paper, how to use DNA genetic algorithm to solve stochastic integer programming expected value models is discussed. Since DNA Genetic algorithm has the merits of plentiful coding, and decoding, conveying complex knowledge flexibly. These merits and the technique of stochastic simulation are combined, which for estimating the random variables of stochastic integer programming expected value models problem. Base on them, a best solution of this problem can be found. The classical newspaper-selling boy problem is calculated for testifying the feasibility and effectiveness of this method.

关键词： stochastic integer programming expected value models DNA genetic algorithm stochastic simulation

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A mathematical model for maximizing the value of phase 3 drug development portfolios incorporating budget constraints and risk

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STATISTICS IN MEDICINE 2013年第10期32卷 1763-1777页

作者： Patel, Nitin R. Ankolekar, Suresh Antonijevic, Zoran Rajicic, Natasa Cytel Inc Cambridge MA 02139 USA MIT Ctr Biomed Innovat Cambridge MA 02139 USA Maastricht Sch Management NL-6229 EP Maastricht Netherlands Pfizer Inc Specialty Care Business Unit Med Dev Grp New York NY 10017 USA

We describe a value-driven approach to optimizing pharmaceutical portfolios. Our approach incorporates inputs from research and development and commercial functions by simultaneously addressing internal and external factors. This approach differentiates itself from current practices in that it recognizes the impact of study design parameters, sample size in particular, on the portfolio value. We develop an integer programming (IP) model as the basis for Bayesian decision analysis to optimize phase 3 development portfolios using expected net present value as the criterion. We show how this framework can be used to determine optimal sample sizes and trial schedules to maximize the value of a portfolio under budget constraints. We then illustrate the remarkable flexibility of the IP model to answer a variety of what-if' questions that reflect situations that arise in practice. We extend the IP model to a stochastic IP model to incorporate uncertainty in the availability of drugs from earlier development phases for phase 3 development in the future. We show how to use stochastic IP to re-optimize the portfolio development strategy over time as new information accumulates and budget changes occur. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： phase 3 portfolio optimization budget constraints decision analysis stochastic integer programming risk portfolio re-optimization

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THE DNA GENETIC ALGORITHM APPLIED FOR SOLVING stochastic integer programming EXPECTED VALUE MODELS

THE DNA GENETIC ALGORITHM APPLIED FOR SOLVING STOCHASTIC INT...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： MING-CHUN WANG WAN-SHENG TANG XIN LIU System Engineering Institute of Tianjin University Tianjin 300072 China Tianjin University of Tec System Engineering Institute of Tianjin University Tianjin 300072 China Tianjin University of Technology and Education Tianjin 300222 China

关键词： stochastic integer programming Ezpected value models DNA genetic algorithm stochastic simulation

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Two-stage quadratic integer programs with stochastic right-hand sides

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MATHEMATICAL programming 2012年第1-2期133卷 121-158页

作者： Oezaltin, Osman Y. Prokopyev, Oleg A. Schaefer, Andrew J. Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA

We consider two-stage quadratic integer programs with stochastic right-hand sides, and present an equivalent reformulation using value functions. We propose a two-phase solution approach. The first phase constructs value functions of quadratic integer programs in both stages. The second phase solves the reformulation using a global branch-and-bound algorithm or a level-set approach. We derive some basic properties of value functions of quadratic integer programs and utilize them in our algorithms. We show that our approach can solve instances whose extensive forms are hundreds of orders of magnitude larger than the largest quadratic integer programming instances solved in the literature.

关键词： stochastic integer programming Quadratic integer programming Value functions Superadditive duality

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A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty

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COMPUTERS & OPERATIONS RESEARCH 2012年第9期39卷 2232-2241页

作者： Hernandez, Patricio Alonso-Ayuso, Antonio Bravo, Fernanda Escudero, Laureano F. Guignard, Monique Marianov, Vladimir Weintraub, Andres Univ Rey Juan Carlos Dpto Estadist & Invest Operativa Madrid 28933 Spain Univ Chile Dept Ingn Ind Santiago Chile MIT Sloan Sch Management Cambridge MA 02139 USA Univ Penn Wharton Sch Dept Operat & Informat Management Philadelphia PA 19104 USA Pontificia Univ Catolica Chile Dept Ingn Elect Santiago Chile

A multi-period stochastic model and an algorithmic approach to location of prison facilities under uncertainty are presented and applied to the Chilean prison system. The problem consists of finding locations and sizes of a preset number of new jails and determining where and when to increase the capacity of both new and existing facilities over a time horizon, while minimizing the expected costs of the prison system. Constraints include maximum inmate transfer distances, upper and lower bounds for facility capacities, and scheduling of facility openings and expansion, among others. The uncertainty lies in the future demand for capacity, because of the long time horizon under study and because of the changes in criminal laws, which could strongly modify the historical tendencies of penal population growth. Uncertainty comes from the effects of penal reform in the capacity demand. It is represented in the model through probabilistic scenarios, and the large-scale model is solved via a heuristic mixture of branch-and-fix coordination and branch-and-bound schemes to satisfy the constraints in all scenarios, the so-called branch-and-cluster coordination scheme. We discuss computational experience and compare the results obtained for the minimum expected cost and average scenario strategies. Our results demonstrate that the minimum expected cost solution leads to better solutions than does the average scenario approach. Additionally, the results show that the stochastic algorithmic approach that we propose outperforms the plain use of a state-of-the-art optimization engine, at least for the three versions of the real-life case that have been tested by us. (c) 2011 Published by Elsevier Ltd.

关键词： Prison facility location stochastic integer programming Multi-period scenario tree Branch-and-fix coordination

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