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Article: An AIS-based hybrid algorithm for static job shop scheduling problem
Title | An AIS-based hybrid algorithm for static job shop scheduling problem |
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Authors | |
Keywords | Artificial immune systems (AIS) Particle swarm optimization (PSO) Job shop scheduling problem (JSSP) Clonal selection Immune network Memory cells |
Issue Date | 2014 |
Publisher | Springer 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? |
Abstract | A 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 Identifier | http://hdl.handle.net/10722/164135 |
ISSN | 2023 Impact Factor: 5.9 2023 SCImago Journal Rankings: 2.071 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Qiu, X | - |
dc.contributor.author | Lau, HYK | - |
dc.date.accessioned | 2012-09-20T07:55:47Z | - |
dc.date.available | 2012-09-20T07:55:47Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Journal of Intelligent Manufacturing, 2014, v. 25 n. 3, p. 489-503 | - |
dc.identifier.issn | 0956-5515 | - |
dc.identifier.uri | http://hdl.handle.net/10722/164135 | - |
dc.description.abstract | A 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.language | eng | - |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0956-5515 | - |
dc.relation.ispartof | Journal of Intelligent Manufacturing | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Artificial immune systems (AIS) | - |
dc.subject | Particle swarm optimization (PSO) | - |
dc.subject | Job shop scheduling problem (JSSP) | - |
dc.subject | Clonal selection | - |
dc.subject | Immune network | - |
dc.subject | Memory cells | - |
dc.title | An AIS-based hybrid algorithm for static job shop scheduling problem | - |
dc.type | Article | - |
dc.identifier.email | Lau, HYK: hyklau@hkucc.hku.hk | - |
dc.identifier.authority | Lau, HYK=rp00137 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1007/s10845-012-0701-2 | - |
dc.identifier.scopus | eid_2-s2.0-84901472747 | - |
dc.identifier.hkuros | 209413 | - |
dc.identifier.hkuros | 245533 | - |
dc.identifier.volume | 25 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 489 | - |
dc.identifier.epage | 503 | - |
dc.identifier.isi | WOS:000336223900008 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0956-5515 | - |