Journal ArticleDOI
A robust optimization model for stochastic logistic problems
Chian-Son Yu,Han-Lin Li +1 more
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TLDR
A stochastic management problem is reformulate as a highly e$cient robust optimization model capable of generating solutions that are progressively less sensitive to the data in the scenario set, and the method proposed herein to transform a robust model into a linear program only requires adding n#m variables.About:
This article is published in International Journal of Production Economics.The article was published on 2000-03-01. It has received 452 citations till now. The article focuses on the topics: Stochastic programming & Robust optimization.read more
Citations
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A stochastic programming approach for supply chain network design under uncertainty
TL;DR: This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale and integrates a recently proposed sampling strategy, the sample average approximation scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions.
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Facility Location Under Uncertainty: A Review
TL;DR: A review of the literature on stochastic and robust facility location models can be found in this article, where the authors illustrate both the rich variety of approaches for optimization under uncertainty and their application to facility location problems.
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The Design of Robust Value-Creating Supply Chain Networks: A Critical Review †
TL;DR: This paper argues for the assessment of SCN robustness as a necessary condition to ensure sustainable value creation and contributes to framing the foundations for a robust SCN design methodology.
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Robust possibilistic programming for socially responsible supply chain network design: A new approach
TL;DR: This paper addresses the problem of socially responsible supply chain network design under uncertain conditions by developing a bi-objective mathematical programming model, called robust possibilistic programming (RPP), and several varieties of RPP models are developed.
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Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case
TL;DR: A stochastic mathematical formulation for designing a network of multi-product supply chains comprising several capacitated production facilities, distribution centres and retailers in markets under uncertainty and incorporates the cut-set concept in reliability theory and also the robust optimisation concept is developed.
References
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Journal ArticleDOI
Robust Optimization of Large-Scale Systems
TL;DR: This paper characterize the desirable properties of a solution to models, when the problem data are described by a set of scenarios for their value, instead of using point estimates, and develops a general model formulation, called robust optimization RO, that explicitly incorporates the conflicting objectives of solution and model robustness.
Book
Operations Research: Applications and Algorithms
TL;DR: In this paper, the authors present a model-based approach to solving linear programming problems, which is based on the Gauss-Jordan method for solving systems of linear equations, and the Branch-and-Bound method for solving mixed integer programming problems.
Journal ArticleDOI
Linear Programming under Uncertainty
TL;DR: This article originally appeared in Management Science, April-July 1955, Volume 1, Numbers 3 and 4, pp. 197-206, published by The Institute of Management Sciences.
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On minimizing a convex function subject to linear inequalities
TL;DR: In this paper, the Simplex Method was extended to yield finite algorithms for minimizing either a convex quadratic function or the sum of the t largest of a set of linear functions and the solution of a generalization of the latter problem is indicated.
Journal ArticleDOI
A New Scenario Decomposition Method for Large-Scale Stochastic Optimization
TL;DR: A novel parallel decomposition algorithm for large, multistage stochastic optimization problems that decomposes the problem into subproblems that correspond to scenarios and has promise for solving Stochastic programs that lie outside current capabilities.