A beam-search-based constructive heuristic for the PFSP to minimise total flowtime

In this paper we present a beam-search-based constructive heuristic to solve the permutation flowshop scheduling problem with total flowtime minimisation as objective. This well-known problem is NP-hard, and several heuristics have been developed in the literature. The proposed algorithm is inspired...

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Bibliographic Details
Published in:Computers & operations research Vol. 81; pp. 167 - 177
Main Authors: Fernandez-Viagas, Victor, Framinan, Jose M.
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.05.2017
Pergamon Press Inc
Subjects:
Algorithms
Beam search
Flowshop
Flowtime
Heuristic
Heuristics
Job shops
PFSP
Scheduling
Scheduling algorithms
Studies
Total completion time
Heuristics
PFSP
Beam search
Scheduling
Flowshop
Flowtime
Total completion time
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary:In this paper we present a beam-search-based constructive heuristic to solve the permutation flowshop scheduling problem with total flowtime minimisation as objective. This well-known problem is NP-hard, and several heuristics have been developed in the literature. The proposed algorithm is inspired in the logic of the beam search, although it remains a fast constructive heuristic. The results obtained by the proposed algorithm outperform those obtained by other constructive heuristics in the literature for the problem, thus modifying substantially the state-of-the-art of efficient approximate procedures for the problem. In addition, the proposed algorithm even outperforms two of the best metaheuristics for many instances of the problem, using much lesser computation effort. The excellent performance of the proposal is also proved by the fact that the new heuristic found new best upper bounds for 35 of the 120 instances in Taillard’s benchmark.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2016.12.020