Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost
This article presents two combinatorial genetic algorithms (GA), unequal earliness tardiness-GA (UET-GA) and job-dependent earliness tardiness-GA (JDET-GA) for the single-machine scheduling problem to minimize earliness tardiness (ET) cost. The sequence of jobs produced in basic UET and JDET as a ch...
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| Format: | Article |
| Language: | English |
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Universitat Politècnica de València
2023-11-01
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| Series: | International Journal of Production Management and Engineering |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/IJPME/article/view/19277 |
| _version_ | 1826905484298289152 |
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| author | Prasad Bari Prasad Karande Vaidehi Bag |
| author_facet | Prasad Bari Prasad Karande Vaidehi Bag |
| author_sort | Prasad Bari |
| collection | DOAJ |
| description | This article presents two combinatorial genetic algorithms (GA), unequal earliness tardiness-GA (UET-GA) and job-dependent earliness tardiness-GA (JDET-GA) for the single-machine scheduling problem to minimize earliness tardiness (ET) cost. The sequence of jobs produced in basic UET and JDET as a chromosome is added to the random population of GA. The best sequence from each epoch is also injected as a population member in the subsequent epoch. The proposed improvement seeks to achieve convergence in less time to search for an optimal solution. Although the GA has been implemented very successfully on many different types of optimization problems, it has been learnt that the algorithm has a search ability difficulty that makes computations NP-hard for types of optimization problems, such as permutation-based optimization problems. The use of a plain random population initialization results in this flaw. To reinforce the random population initialization, the proposed enhancement is utilized to obtain convergence and find a promising solution. The cost is further significantly lowered offering the due date as a decision variable with JDET-GA. Multiple tests were run on well-known single-machine benchmark examples to demonstrate the efficacy of the proposed methodology, and the results are displayed by comparing them with the fundamental UET and JDET approaches with a notable improvement in cost reduction. |
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| format | Article |
| id | doaj.art-703bcff7162a4f809f1cf9f2dbbc7942 |
| institution | Directory Open Access Journal |
| issn | 2340-4876 |
| language | English |
| last_indexed | 2025-02-17T08:29:40Z |
| publishDate | 2023-11-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | International Journal of Production Management and Engineering |
| spelling | doaj.art-703bcff7162a4f809f1cf9f2dbbc79422025-01-02T22:27:24ZengUniversitat Politècnica de ValènciaInternational Journal of Production Management and Engineering2340-48762023-11-01121193010.4995/ijpme.2024.1927718469Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness costPrasad Bari0https://orcid.org/0000-0002-6257-8196Prasad Karande1https://orcid.org/0000-0002-2840-6120Vaidehi Bag2Fr. C. Rodrigues Institute of TechnologyVeermata Jijabai Technological InstituteFr. C. Rodrigues Institute of TechnologyThis article presents two combinatorial genetic algorithms (GA), unequal earliness tardiness-GA (UET-GA) and job-dependent earliness tardiness-GA (JDET-GA) for the single-machine scheduling problem to minimize earliness tardiness (ET) cost. The sequence of jobs produced in basic UET and JDET as a chromosome is added to the random population of GA. The best sequence from each epoch is also injected as a population member in the subsequent epoch. The proposed improvement seeks to achieve convergence in less time to search for an optimal solution. Although the GA has been implemented very successfully on many different types of optimization problems, it has been learnt that the algorithm has a search ability difficulty that makes computations NP-hard for types of optimization problems, such as permutation-based optimization problems. The use of a plain random population initialization results in this flaw. To reinforce the random population initialization, the proposed enhancement is utilized to obtain convergence and find a promising solution. The cost is further significantly lowered offering the due date as a decision variable with JDET-GA. Multiple tests were run on well-known single-machine benchmark examples to demonstrate the efficacy of the proposed methodology, and the results are displayed by comparing them with the fundamental UET and JDET approaches with a notable improvement in cost reduction.https://polipapers.upv.es/index.php/IJPME/article/view/19277earlinesstardinesscostcommon due dategenetic algorithm |
| spellingShingle | Prasad Bari Prasad Karande Vaidehi Bag Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost International Journal of Production Management and Engineering earliness tardiness cost common due date genetic algorithm |
| title | Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost |
| title_full | Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost |
| title_fullStr | Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost |
| title_full_unstemmed | Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost |
| title_short | Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost |
| title_sort | hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost |
| topic | earliness tardiness cost common due date genetic algorithm |
| url | https://polipapers.upv.es/index.php/IJPME/article/view/19277 |
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