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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01 Apr 2000TL;DR: This paper considers two related modelling approaches and solution techniques addressing the traditional supply chain network planning problem as a multi-period resource allocation model involving 0–1 discrete strategic decision variables and a two-stage integer stochastic programming representation and solution of the same problem.
Abstract: The traditional supply chain network planning problem is stated as a multi-period resource allocation model involving 0–1 discrete strategic decision variables. The MIP structure of this problem makes it fairly intractable for practical applications, which involve multiple products, factories, warehouses and distribution centres (DCs). The same problem formulated and studied under uncertainty makes it even more intractable. In this paper we consider two related modelling approaches and solution techniques addressing this issue. The first involves scenario analysis of solutions to “wait and see” models and the second involves a two-stage integer stochastic programming (ISP) representation and solution of the same problem. We show how the results from the former can be used in the solution of the latter model. We also give some computational results based on serial and parallel implementations of the algorithms.
155 citations
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TL;DR: A two-stage, stochastic programming approach is proposed for incorporating demand uncertainty in multisite midterm supply-chain planning problems and the challenge associated with the expectation evaluation of the inner optimization problem is resolved by obtaining its closed-form solution using linear programming (LP) duality.
Abstract: A two-stage, stochastic programming approach is proposed for incorporating demand uncertainty in multisite midterm supply-chain planning problems. In this bilevel decision-making framework, the production decisions are made “here-and-now” prior to the resolution of uncertainty, while the supply-chain decisions are postponed in a “wait-and-see” mode. The challenge associated with the expectation evaluation of the inner optimization problem is resolved by obtaining its closed-form solution using linear programming (LP) duality. At the expense of imposing the normality assumption for the stochastic product demands, the evaluation of the expected second-stage costs is achieved by analytical integration yielding an equivalent convex mixed-integer nonlinear problem (MINLP). Computational requirements for the proposed methodology are shown to be much smaller than those for Monte Carlo sampling. In addition, the cost savings achieved by modeling uncertainty at the planning stage are quantified on the basis of a r...
155 citations
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01 Jan 2000
TL;DR: A new model of multi-stage asset allocation problem using a new methodology for optimization under uncertainty — the Robust Counterpart approach is developed and illustrated by simulated numerical results.
Abstract: In the paper, we develop, discuss and illustrate by simulated numerical results a new model of multi-stage asset allocation problem. The model is given by a new methodology for optimization under uncertainty — the Robust Counterpart approach.
155 citations
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TL;DR: The proposed model, which is called Stochastic Varying Chromosome Length Genetic Algorithm with water Quality constraints (SVLGAQ), is applied to the Ghomrud Reservoir–River system in the central part of Iran and shows, the proposed model for reservoir operation and waste load allocation can reduce theSalinity of the allocated water demands as well as the salinity build-up in the reservoir.
154 citations
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TL;DR: Several heuristic and meta-heuristic methods for elective surgery planning when operating room capacity is shared by elective and emergency surgery are proposed and compared.
154 citations