An improved genetic algorithm with dynamic neighborhood search for job shop scheduling problem.
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| Titel: | An improved genetic algorithm with dynamic neighborhood search for job shop scheduling problem. |
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| Autoren: | Hu K; School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China., Wang L; School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China., Cai J; School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China.; AnHui Key Laboratory of Detection Technology and Energy Saving Devices, AnHui Polytechnic University, Wuhu 241000, China., Cheng L; School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China. |
| Quelle: | Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2023 Sep 11; Vol. 20 (9), pp. 17407-17427. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: AIMS Press Country of Publication: United States NLM ID: 101197794 Publication Model: Print Cited Medium: Internet ISSN: 1551-0018 (Electronic) Linking ISSN: 15471063 NLM ISO Abbreviation: Math Biosci Eng Subsets: PubMed not MEDLINE; MEDLINE |
| Imprint Name(s): | Publication: Springfield, MO : AIMS Press Original Publication: Springfield, MO, USA : [S.l.] : American Institute of Mathematical Sciences; Beihang University |
| Abstract: | The job shop scheduling problem (JSP) has consistently garnered significant attention. This paper introduces an improved genetic algorithm (IGA) with dynamic neighborhood search to tackle job shop scheduling problems with the objective of minimization the makespan. An inserted operation based on idle time is introduced during the decoding phase. An improved POX crossover operator is presented. A novel mutation operation is designed for searching neighborhood solutions. A new genetic recombination strategy based on a dynamic gene bank is provided. The elite retention strategy is presented. Several benchmarks are used to evaluate the algorithm's performance, and the computational results demonstrate that IGA delivers promising and competitive outcomes for the considered JSP. |
| Contributed Indexing: | Keywords: dynamic gene bank; elite retention; idle time; improved POX; improved genetic algorithm; job shop scheduling problem; neighborhood searching |
| Entry Date(s): | Date Created: 20231103 Latest Revision: 20231103 |
| Update Code: | 20250114 |
| DOI: | 10.3934/mbe.2023774 |
| PMID: | 37920060 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | The job shop scheduling problem (JSP) has consistently garnered significant attention. This paper introduces an improved genetic algorithm (IGA) with dynamic neighborhood search to tackle job shop scheduling problems with the objective of minimization the makespan. An inserted operation based on idle time is introduced during the decoding phase. An improved POX crossover operator is presented. A novel mutation operation is designed for searching neighborhood solutions. A new genetic recombination strategy based on a dynamic gene bank is provided. The elite retention strategy is presented. Several benchmarks are used to evaluate the algorithm's performance, and the computational results demonstrate that IGA delivers promising and competitive outcomes for the considered JSP. |
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| ISSN: | 1551-0018 |
| DOI: | 10.3934/mbe.2023774 |