A multi-restart iterated local search algorithm for the permutation flow shop problem minimizing total flow time

A variety of metaheuristics have been developed to solve the permutation flow shop problem minimizing total flow time. Iterated local search (ILS) is a simple but powerful metaheuristic used to solve this problem. Fundamentally, ILS is a procedure that needs to be restarted from another solution whe...

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Bibliographic Details
Published in:Computers & operations research Vol. 40; no. 2; pp. 627 - 632
Main Authors: Dong, Xingye, Chen, Ping, Huang, Houkuan, Nowak, Maciek
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.02.2013
Elsevier
Pergamon Press Inc
Subjects:
Applied sciences
Benchmarking
Exact sciences and technology
Heuristic
Iterated local search
Job shops
Mathematical models
Metaheuristic
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Optimization algorithms
Permutation flow shop
Permutations
Production scheduling
Scheduling
Scheduling, sequencing
Searching
State of the art
Strategy
Studies
Total flow time
Total flow time
Metaheuristic
Scheduling
Permutation flow shop
Iterated local search
Local search
Local optimum
Search algorithm
Trapping
Heuristic method
Flow shop
Metamodel
Completion time
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary:A variety of metaheuristics have been developed to solve the permutation flow shop problem minimizing total flow time. Iterated local search (ILS) is a simple but powerful metaheuristic used to solve this problem. Fundamentally, ILS is a procedure that needs to be restarted from another solution when it is trapped in a local optimum. A new solution is often generated by only slightly perturbing the best known solution, narrowing the search space and leading to a stagnant state. In this paper, a strategy is proposed to allow the restart solution to be generated from a group of solutions drawn from local optima. This allows an extension of the search space, while maintaining the quality of the restart solution. A multi-restart ILS (MRSILS) is proposed, with the performance evaluated on a set of benchmark instances and compared with six state of the art metaheuristics. The results show that the easily implementable MRSILS is significantly better than five of the other metaheuristics and comparable to or slightly better than the remaining one.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2012.08.021