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
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TL;DR: The numerous variants of the stochastic vehicle routing problem that have been studied in the literature are described and categorized.
119 citations
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TL;DR: Experimental results show how dimensionality issues, given by the large number of basins and realistic modeling of the stochastic inflows, can be mitigated by employing neural approximators for the value functions, and efficient discretizations of the state space, such as orthogonal arrays, Latin hypercube designs and low-discrepancy sequences.
118 citations
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TL;DR: In this article, a stochastic programming-robust optimization model is presented to balance the profit estimates and the tractability to various circumstances in the supply chain of a power plant.
118 citations
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TL;DR: A new method is presented in which the operating policy for a reservoir is determined by solving a stochastic dynamic programming model consisting of that reservoir and a two-dimensional representation of the rest of the system.
Abstract: We present a new method of determining an operating policy for a multireservoir system in which the operating policy for a reservoir is determined by solving a stochastic dynamic programming model consisting of that reservoir and a two-dimensional representation of the rest of the system The method is practical for systems with many reservoirs because the time required to determine an operating policy only increases quadratically with the number of reservoirs in the system and because the operating policy for a reservoir is a function of few variables We apply the method to examples of multireservoir systems with between 3 and 17 reservoirs and show that the operating policies determined are very close to optimal
118 citations
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TL;DR: A proposed solution algorithm is capable of providing high quality solutions even for large scale problem instances and integrates Monte Carlo sampling techniques with the L-shaped algorithm.
118 citations