Greedy-Based Non-Dominated Sorting Genetic Algorithm III for Optimizing Single-Machine Scheduling Problem With Interfering Jobs
| Autor: | Chen-Yang Cheng, Shih-Wei Lin, Pourya Pourhejazy, Kuo-Ching Ying, Shu-Fen Li, Ying-Chun Liu |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | IEEE Access, Vol 8, Pp 142543-142556 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2169-3536 30042496 |
| DOI: | 10.1109/ACCESS.2020.3014134 |
| Popis: | Given the importance of production planning and control in the design of flexible services and manufacturing systems, scheduling problems with interfering jobs are much-needed optimization tools to respond to heterogeneous and fluctuating market demands in a timely fashion. This study contributes to the scheduling literature developing an effective multi-objective (M-O) metaheuristic to solve the Single-machine Scheduling Problems with Interfering Jobs (SSP-IJs). Integrating a local search-based mechanism into the evolutionary search procedure, a Greedy-based non-dominated sorting genetic algorithm III (GNSGA-III) is proposed that effectively explores multi-objective solution environments. Various performance indicators within extensive numerical tests are used to compare the performance of the GNSGA-III with that of the best-performing benchmark algorithm in the literature developed to solve the SSP-IJs. Statistical tests verify that the developed multi-objective optimization algorithm is superior with respect to various performance indicators. Applications of the developed solution approach are worthwhile topics to help advance multi-objective optimization problems. |
| Databáze: | Directory of Open Access Journals |
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