Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm

Hsueh Chien Cheng; Tsung Che Chiang; Li Chen Fu

doi:10.1109/CEC.2008.4631005

Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm

Hsueh Chien Cheng, Tsung Che Chiang, Li Chen Fu

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.

Original language	English
Title of host publication	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Pages	1596-1602
Number of pages	7
DOIs	https://doi.org/10.1109/CEC.2008.4631005
Publication status	Published - 2008
Event	2008 IEEE Congress on Evolutionary Computation, CEC 2008 - Hong Kong, China Duration: 2008 Jun 1 → 2008 Jun 6

Publication series

Name	2008 IEEE Congress on Evolutionary Computation, CEC 2008

Other

Other	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Country/Territory	China
City	Hong Kong
Period	2008/06/01 → 2008/06/06

ASJC Scopus subject areas

Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1109/CEC.2008.4631005

Cite this

Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm. / Cheng, Hsueh Chien; Chiang, Tsung Che; Fu, Li Chen.

2008 IEEE Congress on Evolutionary Computation, CEC 2008. 2008. p. 1596-1602 4631005 (2008 IEEE Congress on Evolutionary Computation, CEC 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cheng, HC, Chiang, TC & Fu, LC 2008, Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm. in 2008 IEEE Congress on Evolutionary Computation, CEC 2008., 4631005, 2008 IEEE Congress on Evolutionary Computation, CEC 2008, pp. 1596-1602, 2008 IEEE Congress on Evolutionary Computation, CEC 2008, Hong Kong, China, 2008/06/01. https://doi.org/10.1109/CEC.2008.4631005

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abstract = "The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.",

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AB - The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.

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Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm

Abstract

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