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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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Journal ArticleDOI
TL;DR: This paper addresses a multi-stage and multi-period supply chain network design problem in which multiple commodities should be produced through different subsequent levels of manufacturing processes, formulated as a two-stage stochastic program under stoChastic and highly time-variable demands.

96 citations

Journal ArticleDOI
TL;DR: A novel two-stage scenario-based mixed fuzzy-stochastic programming model for integrated relief pre-positioning and procurement planning based on a quantity flexibility (QF) contract under a mixture of uncertain data is proposed.
Abstract: Humanitarian organizations typically pre-position relief items in strategic locations whose optimum levels are affected by the amounts of pre-disaster contractual agreements and post-disaster procurements. To account for these interrelationships, this paper proposes a novel two-stage scenario-based mixed fuzzy-stochastic programming model for integrated relief pre-positioning and procurement planning based on a quantity flexibility (QF) contract under a mixture of uncertain data. An effective multi-step solution method is also devised to solve the problem in real-sized instances. Applicability of the proposed model is examined through a real case study. Finally, a number of sensitivity analyses are conducted to provide helpful managerial insights.

96 citations

Journal ArticleDOI
TL;DR: The results indicate that FR is useful to derive operating rules for a long-term planning model, where imperfect and partial information is available, and ANFIS is beneficial in medium-term implicit stochastic optimization as it is able to extract important features of the system from the generated input-output set and represent those features as general operating rules.

96 citations

Book ChapterDOI
01 Sep 2010
TL;DR: This paper constructs a δ(1 + e)-approximation algorithm for the stochastic problem, which invokes the linear algorithm only a logarithmic number of times in the problem input, for any desired accuracy level e > 0.
Abstract: We consider optimization problems that can be formulated as minimizing the cost of a feasible solution wT x over an arbitrary combinatorial feasible set F ⊂ {0, 1}n. For these problems we describe a broad class of corresponding stochastic problems where the cost vector W has independent random components, unknown at the time of solution. A natural and important objective that incorporates risk in this stochastic setting is to look for a feasible solution whose stochastic cost has a small tail or a small convex combination of mean and standard deviation. Our models can be equivalently reformulated as nonconvex programs for which no efficient algorithms are known. In this paper, we make progress on these hard problems. Our results are several efficient general-purpose approximation schemes. They use as a black-box (exact or approximate) the solution to the underlying deterministic problem and thus immediately apply to arbitrary combinatorial problems. For example, from an available δ-approximation algorithm to the linear problem, we construct a δ(1 + e)-approximation algorithm for the stochastic problem, which invokes the linear algorithm only a logarithmic number of times in the problem input (and polynomial in 1/e), for any desired accuracy level e > 0. The algorithms are based on a geometric analysis of the curvature and approximability of the nonlinear level sets of the objective functions.

96 citations

Journal ArticleDOI
TL;DR: By means of a numerical study, it is demonstrated that simple state-dependent policies that prioritize less urgent jobs when the total number of jobs is large perform well, especially when jobs are time-critical.
Abstract: In the aftermath of mass-casualty events, key resources (such as ambulances and operating rooms) can be overwhelmed by the sudden jump in patient demand. To ration these resources, patients are assigned different priority levels, a process that is called triage. According to triage protocols in place, each patient's priority level is determined based on that patient's injuries only. However, recent work from the emergency medicine literature suggests that when determining priorities, resource limitations and the scale of the event should also be taken into account in order to do the greatest good for the greatest number. This article investigates how this can be done and what the potential benefits would be. We formulate the problem as a priority assignment problem in a clearing system with multiple classes of impatient jobs. Jobs are classified based on their lifetime (i.e., their tolerance for wait), service time, and reward distributions. Our objective is to maximize the expected total reward, e.g., the expected total number of survivors. Using sample-path methods and stochastic dynamic programming, we identify conditions under which the state information is not needed for prioritization decisions. In the absence of these conditions, we partially characterize the optimal policy, which is possibly state dependent, and we propose a number of heuristic policies. By means of a numerical study, we demonstrate that simple state-dependent policies that prioritize less urgent jobs when the total number of jobs is large perform well, especially when jobs are time-critical.

96 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532