scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
TL;DR: A large deviation-type approximation, referred to as “Bernstein approximation,” of the chance constrained problem is built that is convex and efficiently solvable and extended to the case of ambiguous chance constrained problems, where the random perturbations are independent with the collection of distributions known to belong to a given convex compact set.
Abstract: We consider a chance constrained problem, where one seeks to minimize a convex objective over solutions satisfying, with a given close to one probability, a system of randomly perturbed convex constraints. This problem may happen to be computationally intractable; our goal is to build its computationally tractable approximation, i.e., an efficiently solvable deterministic optimization program with the feasible set contained in the chance constrained problem. We construct a general class of such convex conservative approximations of the corresponding chance constrained problem. Moreover, under the assumptions that the constraints are affine in the perturbations and the entries in the perturbation vector are independent-of-each-other random variables, we build a large deviation-type approximation, referred to as “Bernstein approximation,” of the chance constrained problem. This approximation is convex and efficiently solvable. We propose a simulation-based scheme for bounding the optimal value in the chance constrained problem and report numerical experiments aimed at comparing the Bernstein and well-known scenario approximation approaches. Finally, we extend our construction to the case of ambiguous chance constrained problems, where the random perturbations are independent with the collection of distributions known to belong to a given convex compact set rather than to be known exactly, while the chance constraint should be satisfied for every distribution given by this set.

1,099 citations

Journal ArticleDOI
TL;DR: In this paper, a stochastic dynamic programming model is used to model the operating flexibility of a multinational corporation to shift production between two manufacturing plants located in different countries, the value of which is dependent upon the real exchange rate.
Abstract: The multinational corporation is a network of activities located in different countries. The value of this network derives from the opportunity to benefit from uncertainty through the coordination of subsidiaries which are geographically dispersed. We model this coordination as the operating flexibility to shift production between two manufacturing plants located in different countries. A stochastic dynamic programming model treats explicitly this flexibility as equivalent to owning an option, the value of which is dependent upon the real exchange rate. The model is extended to analyze hysteresis effects and within-country growth options. We show that the management of across-border coordination has led to changes in the heuristic rules used for performance evaluation and transfer pricing.

1,092 citations

Journal ArticleDOI
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.

1,044 citations

Journal ArticleDOI
01 Oct 1977

1,016 citations

Journal ArticleDOI
TL;DR: The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm, and a parallel procedure for decreasing computational costs is discussed.
Abstract: Dynamic programming is discussed as an approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, is numerically stable, and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the approach's efficacy, applying dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm. A parallel procedure for decreasing computational costs is discussed. >

1,014 citations


Network Information
Related Topics (5)
Optimization problem
96.4K papers, 2.1M citations
86% related
Scheduling (computing)
78.6K papers, 1.3M citations
85% related
Optimal control
68K papers, 1.2M citations
84% related
Supply chain
84.1K papers, 1.7M citations
83% related
Markov chain
51.9K papers, 1.3M citations
79% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532