Rollout Algorithms for Combinatorial Optimization

Authors:
Dimitri P. Bertsekas;John N. Tsitsiklis;Cynara Wu
Affiliations:
Department of Electrical Engineering and Computer Science, M.I.T., Cambridge, MA 02139;Department of Electrical Engineering and Computer Science, M.I.T., Cambridge, MA 02139;Department of Electrical Engineering and Computer Science, M.I.T., Cambridge, MA 02139
Venue:
Journal of Heuristics
Year:
1997

Citing 4
Cited 20

A user's guide to tabu search

Annals of Operations Research - Special issue on Tabu search
Learning to act using real-time dynamic programming

Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Neuro-Dynamic Programming

Neuro-Dynamic Programming

Rollout Algorithms for Stochastic Scheduling Problems

Journal of Heuristics
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

Machine Learning
Meta-heuristics: The State of the Art

ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
A cooperative parallel rollout algorithm for the sequential ordering problem

Parallel Computing - Special issue: Parallel computing in logistics
Analysis of a Rollout Approach to Sequencing Problems with Stochastic Routing Applications

Journal of Heuristics
Parallelization Strategies for Rollout Algorithms

Computational Optimization and Applications
Approximate Policy Optimization and Adaptive Control in Regression Models

Computational Economics
The Dynamic Assignment Problem

Transportation Science
Augmenting priority rule heuristics with justification and rollout to solve the resource-constrained project scheduling problem

Computers and Operations Research
Stochastic rollout and justification to solve the resource-constrained project scheduling problem

Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Scheduling trains as a blocking parallel-machine job shop scheduling problem

Computers and Operations Research
Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands

Operations Research
A survey on metaheuristics for stochastic combinatorial optimization

Natural Computing: an international journal
Heuristic search in infinite state spaces guided by Lyapunov analysis

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Solutions to real-world instances of PSPACE-complete stacking

ESA'07 Proceedings of the 15th annual European conference on Algorithms
Rollout strategy-based probabilistic causal model approach for the multiple fault diagnosis

Robotics and Computer-Integrated Manufacturing
Opportunistic Fair Scheduling in Wireless Networks: An Approximate Dynamic Programming Approach

Mobile Networks and Applications
Hybrid metaheuristics in combinatorial optimization: A survey

Applied Soft Computing
Pilot, rollout and monte carlo tree search methods for job shop scheduling

LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Rollout Policies for Dynamic Solutions to the Multivehicle Routing Problem with Stochastic Demand and Duration Limits

Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying theeffectiveness of any given heuristic algorithm through sequentialapplication. In particular, we embed the problem within a dynamicprogramming framework, and we introduce several types of rolloutalgorithms, which are related to notions of policy iteration. Weprovide conditions guaranteeing that the rollout algorithmimproves the performance of the original heuristic algorithm. Themethod is illustrated in the context of a machine maintenance andrepair problem.