Multi-level job scheduling in a flexible job shop environment.
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| Title: | Multi-level job scheduling in a flexible job shop environment. |
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| Authors: | Na, Hongbum1 (AUTHOR), Park, Jinwoo1 (AUTHOR) autofact@snu.ac.kr |
| Source: | International Journal of Production Research. Jul2014, Vol. 52 Issue 13, p3877-3887. 11p. 3 Black and White Photographs, 3 Diagrams, 3 Charts. |
| Subjects: | Job shops, Production scheduling, Manufacturing cells, Production planning, Genetic algorithms, Material requirements planning, Materials management, Flexible manufacturing systems |
| Abstract: | This study deals with a scheduling problem with multi-level job structures in a flexible job shop environment. This scheduling process arises after the part production plans are created by the MRP (material requirement planning) system, therefore the total tardiness measure is considered as an objective function in order to complete the parts by the set due dates. MILP (mixed integer linear programming) model is introduced to mathematically represent the target problem. Owing to the high complexity of the target problem, GA (genetic algorithm) is proposed to solve the problem and additional methods, such as priority rules and local search rules, are applied to improve the performance of GA. Computational examples are shown and the results are discussed in comparison with the results of IBM ILOG CPLEX and IBM ILOG CP Optimizer. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study deals with a scheduling problem with multi-level job structures in a flexible job shop environment. This scheduling process arises after the part production plans are created by the MRP (material requirement planning) system, therefore the total tardiness measure is considered as an objective function in order to complete the parts by the set due dates. MILP (mixed integer linear programming) model is introduced to mathematically represent the target problem. Owing to the high complexity of the target problem, GA (genetic algorithm) is proposed to solve the problem and additional methods, such as priority rules and local search rules, are applied to improve the performance of GA. Computational examples are shown and the results are discussed in comparison with the results of IBM ILOG CPLEX and IBM ILOG CP Optimizer. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00207543 |
| DOI: | 10.1080/00207543.2013.848487 |
