Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
Papers published on a yearly basis
Papers
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TL;DR: The linear programming (LP) model of deterministic supply-chain planning is extended to take demand uncertainty and cash flows into account for the medium term to manage the risks pertaining to unmet demand, excess inventory, and cash liquidity when demand is uncertain.
Abstract: We extend the linear programming (LP) model of deterministic supply-chain planning to take demand uncertainty and cash flows into account for the medium term. The resulting stochastic LP model is similar to that of Asset-Liability Management (ALM), for which the literature using stochastic programming is extensive. As such, we survey various modeling and solution choices developed in the ALM literature and discuss their applicability to supply chain planning. This survey can be a basis for making modeling/solution choices in research and in practice to manage the risks pertaining to unmet demand, excess inventory and cash liquidity when demand is uncertain.
104 citations
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TL;DR: This work will demonstrate the applicability of progressive hedging-based method for solving large scale stochastic network optimization problems with equilibrium constraints for pre-disaster transportation network protection against uncertain future disasters.
Abstract: This research focuses on pre-disaster transportation network protection against uncertain future disasters. Given limited resources, the goal of the central planner is to choose the best set of network components to protect while allowing the network users to follow their own best-perceived routes in any resultant network configuration. This problem is formulated as a two-stage stochastic programming problem with equilibrium constraints, where the objective is to minimize the total expected physical and social losses caused by potential disasters. Developing efficient solution methods for such a problem can be challenging. In this work, we will demonstrate the applicability of progressive hedging-based method for solving large scale stochastic network optimization problems with equilibrium constraints. In the proposed solution procedure, we solve each modified scenario sub-problem as a mathematical program with complementary constraints and then gradually aggregate scenario-dependent solutions to the final optimal solution.
104 citations
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TL;DR: Different novel robust flexible programming and robust mixed possibilistic-flexible programming models are proposed and a real-life problem is employed to show the efficiency and practicability of the propounded models against the traditional fuzzy programming approach.
103 citations
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TL;DR: A variant of Karmarkar's algorithm for block-angular structuredlinear programs, such as stochastic linear programs, is presented, which gives a worst-case bound on the order of the running time that can be an order of magnitude better than that of Karma's standard algorithm.
Abstract: We present a variant of Karmarkar's algorithm for block-angular structured linear programs, such as stochastic linear programs. By computing the projection efficiently, we give a worst-case bound on the order of the running time that can be an order of magnitude better than that of Karmarkar's standard algorithm. Further implications for approximations and very large-scale problems are given.
103 citations
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TL;DR: A new algorithm for the stochastic unit commitment problem which is based on column generation approach is proposed which continues adding schedules from the dual solution of the restricted linear master program until the algorithm cannot generate new schedules.
103 citations