Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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IBM1
TL;DR: A mixed-integer program in which expected value of the unmet demand is minimized subject to capacity and budget constraints is formulated in which a heuristic strengthens the linear programming relaxation of the formulation with cutting planes and performs limited enumeration.
Abstract: We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated probabilities, our aim is to identify a set of tools that is a good compromise for all these scenarios. More precisely, we formulate a mixed-integer program in which expected value of the unmet demand is minimized subject to capacity and budget constraints. This is a dicult two-stage stochastic mixed-integer program which can not be solved to optimality in a reasonable amount of time. We instead propose a heuristic that can produce near-optimal solutions. Our heuristic strengthens the linear programming relaxation of the formulation with cutting planes and performs limited enumeration. Analyses of the results in some real-life situations are also presented.
119 citations
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05 Dec 2003
TL;DR: In this article, the authors present an easily readable, up-to-date treatment of asset and wealth management in the presence of liabilities and other portfolio complexities using discrete-time, multi-period stochastic programming.
Abstract: All individuals and institutions face asset/liability management problems on a continuous basis. In this Research Foundation monograph, the author presents an easily readable, up-to-date treatment of asset and wealth management in the presence of liabilities and other portfolio complexities. The approach discussed and recommended is discrete-time, multiperiod stochastic programming. For most practical purposes, such models provide a superior alternative to other approaches, such as mean-variance, simulation, control theory, and continuous-time finance.
119 citations
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TL;DR: New theoretical convergence results on the cross-entropy (CE) method for discrete optimization are presented, and it is shown that a popular implementation of the method converges, and finds an optimal solution with probability arbitrarily close to 1.
119 citations
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TL;DR: In this article, a stochastic tactical planning model for the production and distribution of fresh agricultural products is presented, which incorporates the uncertainties encountered in the fresh produce industry when developing growing and distribution plans due to the variability of weather and demand.
119 citations
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TL;DR: In this article, a stochastic programming approach for assessing the statistical distribution of marginal water values in multipurpose multireservoir systems where hydropower generation and irrigation crop production are the main economic activities depending on water is presented.
Abstract: The International Conference on Water and the Environment held in Dublin in 1992 emphasized the need to consider water as an economic good. Since water markets are usually absent or ineffective, the value of water cannot be directly derived from market activities but must rather be assessed through shadow prices. Economists have developed various valuation techniques to determine the economic value of water, especially to handle allocation issues involving environmental water uses. Most of the nonmarket valuation studies reported in the literature focus on long-run policy problems, such as permanent (re) allocations of water, and assume that the water availability is given. When dealing with short-run allocation problems, water managers are facing complex spatial and temporal trade-offs and must therefore be able to track site and time changes in water values across different hydrologic conditions, especially in arid and semiarid areas where the availability of water is a limiting and stochastic factor. This paper presents a stochastic programming approach for assessing the statistical distribution of marginal water values in multipurpose multireservoir systems where hydropower generation and irrigation crop production are the main economic activities depending on water. In the absence of a water market, the Lagrange multipliers correspond to shadow prices, and the marginal water values are the Lagrange multipliers associated with the mass balance equations of the reservoirs. The methodology is illustrated with a cascade of hydroelectric-irrigation reservoirs in the Euphrates river basin in Turkey and Syria.
119 citations