Research on Intelligent Genetic Algorithms for Optimal Production Scheduling
| Autor: | Hao-Chin Chang, 張皓欽 |
|---|---|
| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 104 Enterprises exist in a competitive manufacturing environment. To reduce production costs and effectively use production capacity to improve competitiveness, a hybrid production system is necessary. The flexible job shop (FJS) is a hybrid production system, and the FJS problem (FJSP) has drawn considerable attention in the past few decades. A novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. Moreover, in contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. For solving the distributed and flexible job-shop scheduling problem (DFJSP), we use the Taguchi method to optimize the parameters of a genetic algorithm (GA). In this dissertation, we propose a refined genetic algorithm (GA) for resolving a fastener manufacturing plant scheduling problem. In contrast to previous studies, we aim to solve the encountered problem when using traditional GAs for solving distributed and flexible problems (DFJSP), such as the incorrect job assignment and diversity problems. When deals with DFJSP, several factors such as job routing and selection of jobs, machines, and manufacturing units, which all render encoding solutions into chromosomes of the GA that increasingly difficult, should be considered when addressing the scheduling problems in this environment. A unique design for the evolution process is necessary for avoiding above problems occurred; however, the diversity is often lost. In order to overcome this drawback, this study proposes an intuitive and easy-to-use encoding operator in which a probability concept that integrated into a real-parameter encoding method. The proposed refined GA was used to solve the scheduling problem of a real-world fastener manufacturing plant with several verifications of the results revealed that they are robust and outperforms the result of traditional scheduling method. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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