An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines

In this article, an effective shuffled frog-leaping algorithm (SFLA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines (HFSP-IPM). First, some novel heuristic decoding rules for both job order decision and machine assignment are proposed. Then, three hybri...

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Vydáno v:Engineering optimization Ročník 45; číslo 12; s. 1409 - 1430
Hlavní autoři: Xu, Ye, Wang, Ling, Wang, Shengyao, Liu, Min
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 01.12.2013
Taylor & Francis Ltd
Témata:
Algorithms
Decoding
decoding method
Design of experiments
Exploration
Heuristic
hybrid flow-shop scheduling problem
identical parallel machines
Operators
Optimization
Optimization algorithms
Production scheduling
Scheduling
Searching
shuffled frog-leaping algorithm
Taguchi methods
ISSN:0305-215X, 1029-0273
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Shrnutí:In this article, an effective shuffled frog-leaping algorithm (SFLA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines (HFSP-IPM). First, some novel heuristic decoding rules for both job order decision and machine assignment are proposed. Then, three hybrid decoding schemes are designed to decode job order sequences to schedules. A special bi-level crossover and multiple local search operators are incorporated in the searching framework of the SFLA to enrich the memetic searching behaviour and to balance the exploration and exploitation capabilities. Meanwhile, some theoretical analysis for the local search operators is provided for guiding the local search. The parameter setting of the algorithm is also investigated based on the Taguchi method of design of experiments. Finally, numerical testing based on well-known benchmarks and comparisons with some existing algorithms are carried out to demonstrate the effectiveness of the proposed algorithm.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2012.737784