Summary and Info
Research on algorithms and applications of stochastic programming, the study of procedures for decision making under uncertainty over time, has been very active in recent years and deserves to be more widely known. This is the first book devoted to the full scale of applications of stochastic programming and also the first to provide access to publicly available algorithmic systems. The 32 contributed papers in this volume are written by leading stochastic programming specialists and reflect the high level of activity in recent years in research on algorithms and applications. The book introduces the power of stochastic programming to a wider audience and demonstrates the application areas where this approach is superior to other modeling approaches. Applications of Stochastic Programming consists of two parts. The first part presents papers describing publicly available stochastic programming systems that are currently operational. All the codes have been extensively tested and developed and will appeal to researchers and developers who want to make models without extensive programming and other implementation costs. The codes are a synopsis of the best systems available, with the requirement that they be user-friendly, ready to go, and publicly available. The second part of the book is a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity. It contains the most complete collection of real applications using stochastic programming available in the literature. The papers show how leading researchers choose to treat randomness when making planning models, with an emphasis on modeling, data, and solution approaches. Contents Preface: Part I: Stochastic Programming Codes; Chapter 1: Stochastic Programming Computer Implementations, Horand I. Gassmann, SteinW.Wallace, and William T. Ziemba; Chapter 2: The SMPS Format for Stochastic Linear Programs, Horand I. Gassmann; Chapter 3: The IBM Stochastic Programming System, Alan J. King, Stephen E.Wright, Gyana R. Parija, and Robert Entriken; Chapter 4: SQG: Software for Solving Stochastic Programming Problems with Stochastic Quasi-Gradient Methods, Alexei A. Gaivoronski; Chapter 5: Computational Grids for Stochastic Programming, Jeff Linderoth and Stephen J.Wright; Chapter 6: Building and Solving Stochastic Linear Programming Models with SLP-IOR, Peter Kall and János Mayer; Chapter 7: Stochastic Programming from Modeling Languages, Emmanuel Fragnière and Jacek Gondzio; Chapter 8: A Stochastic Programming Integrated Environment (SPInE), P. Valente, G. Mitra, and C. A. Poojari; Chapter 9: Stochastic Modelling and Optimization Using Stochastics , M. A. H. ! Dempster, J. E. Scott, and G.W. P. Thompson; Chapter 10: An Integrated Modelling Environment for Stochastic Programming, Horand I. Gassmann and David M. Gay; Part II: Stochastic Programming Applications; Chapter 11: Introduction to Stochastic Programming Applications Horand I. Gassmann, Sandra L. Schwartz, SteinW.Wallace, and William T. Ziemba Chapter 12: Fleet Management, Warren B. Powell and Huseyin Topaloglu; Chapter 13: Modeling Production Planning and Scheduling under Uncertainty, A. Alonso-Ayuso, L. F. Escudero, and M. T. Ortuño; Chapter 14: A Supply Chain Optimization Model for the Norwegian Meat Cooperative, A. Tomasgard and E. Høeg; Chapter 15: Melt Control: Charge Optimization via Stochastic Programming, Jitka Dupaˇcová and Pavel Popela; Chapter 16: A Stochastic Programming Model for Network Resource Utilization in the Presence of Multiclass Demand Uncertainty, Julia L. Higle and Suvrajeet Sen; Chapter 17: Stochastic Optimization and Yacht Racing, A. B. Philpott; Chapter 18: Stochastic Approximation, Momentum, and Nash Play, H. Berglann and S. D. Flåm; Chapter 19: Stochastic Optimization for Lake Eutrophication Management, Alan J. King, László Somlyódy, and Roger J
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