Rollout Algorithms for Combinatorial Optimization

Dimitri P. Bertsekas; John N. Tsitsiklis; Cynara Wu

doi:10.1023/A:1009635226865

Rollout Algorithms for Combinatorial Optimization

Dimitri P. Bertsekas, John N. Tsitsiklis, Cynara Wu

Research output: Contribution to journal › Article › peer-review

171 Scopus citations

Abstract

We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying the effectiveness of any given heuristic algorithm through sequential application. In particular, we embed the problem within a dynamic programming framework, and we introduce several types of rollout algorithms, which are related to notions of policy iteration. We provide conditions guaranteeing that the rollout algorithm improves the performance of the original heuristic algorithm. The method is illustrated in the context of a machine maintenance and repair problem.

Original language	English (US)
Pages (from-to)	245-262
Number of pages	18
Journal	Journal of Heuristics
Volume	3
Issue number	3
DOIs	https://doi.org/10.1023/A:1009635226865
State	Published - 1997
Externally published	Yes

ASJC Scopus subject areas

Software
Information Systems
Computer Networks and Communications
Control and Optimization
Management Science and Operations Research
Artificial Intelligence

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abstract = "We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying the effectiveness of any given heuristic algorithm through sequential application. In particular, we embed the problem within a dynamic programming framework, and we introduce several types of rollout algorithms, which are related to notions of policy iteration. We provide conditions guaranteeing that the rollout algorithm improves the performance of the original heuristic algorithm. The method is illustrated in the context of a machine maintenance and repair problem.",

author = "Bertsekas, {Dimitri P.} and Tsitsiklis, {John N.} and Cynara Wu",

note = "Funding Information: ⁄Research supported by NSF under Grant DMI-9625489.",

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AU - Tsitsiklis, John N.

AU - Wu, Cynara

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N2 - We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying the effectiveness of any given heuristic algorithm through sequential application. In particular, we embed the problem within a dynamic programming framework, and we introduce several types of rollout algorithms, which are related to notions of policy iteration. We provide conditions guaranteeing that the rollout algorithm improves the performance of the original heuristic algorithm. The method is illustrated in the context of a machine maintenance and repair problem.

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Rollout Algorithms for Combinatorial Optimization

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