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Article: An AIS-based hybrid algorithm for static job shop scheduling problem

TitleAn AIS-based hybrid algorithm for static job shop scheduling problem
Authors
KeywordsArtificial immune systems (AIS)
Particle swarm optimization (PSO)
Job shop scheduling problem (JSSP)
Clonal selection
Immune network
Memory cells
Issue Date2014
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0956-5515
Citation
Journal of Intelligent Manufacturing, 2014, v. 25 n. 3, p. 489-503 How to Cite?
AbstractA static job shop scheduling problem (JSSP) is a class of JSSP which is a combinatorial optimization problem with the assumption of no disruptions and previously known knowledge about the jobs and machines. A new hybrid algorithm based on artificial immune systems (AIS) and particle swarm optimization (PSO) theory is proposed for this problem with the objective of makespan minimization. AIS is a metaheuristics inspired by the human immune system. Its two theories, namely, clonal selection and immune network theory, are integrated with PSO in this research. The clonal selection theory builds up the framework of the algorithm which consists of selection, cloning, hypermutation, memory cells extraction and receptor editing processes. Immune network theory increases the diversity of antibody set which represents the solution repertoire. To improve the antibody hypermutation process to accelerate the search procedure, a modified version of PSO is inserted. This proposed algorithm is tested on 25 benchmark problems of different sizes. The results demonstrate the effectiveness of the PSO algorithm and the specific memory cells extraction process which is one of the key features of AIS theory. By comparing with other popular approaches reported in existing literatures, this algorithm shows great competitiveness and potential, especially for small size problems in terms of computation time.
Persistent Identifierhttp://hdl.handle.net/10722/164135
ISSN
2019 Impact Factor: 4.311
2015 SCImago Journal Rankings: 1.397
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQiu, X-
dc.contributor.authorLau, HYK-
dc.date.accessioned2012-09-20T07:55:47Z-
dc.date.available2012-09-20T07:55:47Z-
dc.date.issued2014-
dc.identifier.citationJournal of Intelligent Manufacturing, 2014, v. 25 n. 3, p. 489-503-
dc.identifier.issn0956-5515-
dc.identifier.urihttp://hdl.handle.net/10722/164135-
dc.description.abstractA static job shop scheduling problem (JSSP) is a class of JSSP which is a combinatorial optimization problem with the assumption of no disruptions and previously known knowledge about the jobs and machines. A new hybrid algorithm based on artificial immune systems (AIS) and particle swarm optimization (PSO) theory is proposed for this problem with the objective of makespan minimization. AIS is a metaheuristics inspired by the human immune system. Its two theories, namely, clonal selection and immune network theory, are integrated with PSO in this research. The clonal selection theory builds up the framework of the algorithm which consists of selection, cloning, hypermutation, memory cells extraction and receptor editing processes. Immune network theory increases the diversity of antibody set which represents the solution repertoire. To improve the antibody hypermutation process to accelerate the search procedure, a modified version of PSO is inserted. This proposed algorithm is tested on 25 benchmark problems of different sizes. The results demonstrate the effectiveness of the PSO algorithm and the specific memory cells extraction process which is one of the key features of AIS theory. By comparing with other popular approaches reported in existing literatures, this algorithm shows great competitiveness and potential, especially for small size problems in terms of computation time.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0956-5515-
dc.relation.ispartofJournal of Intelligent Manufacturing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial immune systems (AIS)-
dc.subjectParticle swarm optimization (PSO)-
dc.subjectJob shop scheduling problem (JSSP)-
dc.subjectClonal selection-
dc.subjectImmune network-
dc.subjectMemory cells-
dc.titleAn AIS-based hybrid algorithm for static job shop scheduling problem-
dc.typeArticle-
dc.identifier.emailLau, HYK: hyklau@hkucc.hku.hk-
dc.identifier.authorityLau, HYK=rp00137-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s10845-012-0701-2-
dc.identifier.scopuseid_2-s2.0-84901472747-
dc.identifier.hkuros209413-
dc.identifier.hkuros245533-
dc.identifier.volume25-
dc.identifier.issue3-
dc.identifier.spage489-
dc.identifier.epage503-
dc.identifier.isiWOS:000336223900008-
dc.publisher.placeUnited States-

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