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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: A bi-objective optimization model for designing a closed loop supply chain (CLSC) network under uncertainty in which the total costs and the maximum waiting times in the queue of products are considered to minimize.

115 citations

Journal ArticleDOI
TL;DR: A two-stage stochastic programming model is developed that incorporates the hybrid allocation policy and achieves high levels of accessibility and equity simultaneously and is illustrated on a case study based on real-world data from the 2011 Van earthquake in Turkey.
Abstract: In this study, we introduce a distribution network design problem that determines the locations and capacities of the relief distribution points in the last mile network, while considering demand-and network-related uncertainties in the post-disaster environment. The problem addresses the critical concerns of relief organizations in designing last mile networks, which are providing accessible and equitable service to beneficiaries. We focus on two types of supply allocation policies and propose a hybrid version considering their different implications on equity and accessibility. Then, we develop a two-stage stochastic programming model that incorporates the hybrid allocation policy and achieves high levels of accessibility and equity simultaneously. We devise a branch-and-cut algorithm based on Benders decomposition to solve large problem instances in reasonable times and conduct a numerical study to demonstrate the computational effectiveness of the solution method. We also illustrate the application of our model on a case study based on real-world data from the 2011 Van earthquake in Turkey.

115 citations

Journal ArticleDOI
TL;DR: Applicability of the involved regularity conditions to nondifferentiable cases, and in particular to stochastic programming with recourse, is discussed, and an expansion in terms of a parametrized mathematical programming problem, depending on a single random vector is given.
Abstract: Asymptotic behavior of optimal solutions xI‚n of a sequence of stochastic programming problems is studied. Variational and generalized equations approaches are discussed. An expansion of xI‚n in terms of a parametrized mathematical programming problem, depending on a single random vector, is given. When optimal solutions of the parametrized program are directionally differentiable, this expansion leads to a close form expression for the asymptotic distribution of xI‚n. Applicability of the involved regularity conditions to nondifferentiable cases, and in particular to stochastic programming with recourse, is discussed.

114 citations

Journal ArticleDOI
TL;DR: In this paper, a shortest path stochastic dynamic programming (SP-SDP) is proposed to solve the optimal control problem associated with the design of the power management system.
Abstract: When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP-SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite-horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time-invariant causal state-feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite-horizon case, and for the same level of complexity as a discounted future-cost controller, the shortest-path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.

114 citations

Journal ArticleDOI
TL;DR: In this paper, a mixed possibilistic-stochastic programming approach is proposed to construct the crisp counterpart of the original HLP model and a simulated annealing (SA) and an imperialist competitive algorithm (ICA) with a new solution representation are developed to solve real-sized instances whose performances are compared with a proposed lower bound.
Abstract: This paper addresses a novel sustainable hub location problem (SHLP) in which two new environmental-based cost functions accounting for air and noise pollution of vehicles are incorporated. To cope with uncertain data incorporated in the model, a mixed possibilistic–stochastic programming approach is proposed to construct the crisp counterpart. A simulated annealing (SA) and an imperialist competitive algorithm (ICA) with a new solution representation are developed to solve real-sized instances whose performances are compared with a proposed lower bound. Finally, some computational experiments are provided to demonstrate the effectiveness of the proposed model and solution approaches.

114 citations


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