An innovative genetic algorithm-based master schedule to optimize job shop scheduling problem
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Abstract
The rapid development of manufacturing sectors in the contemporary Industry 4.0 era has prompted enterprises to adopt increasingly systematic, streamlined, and efficient organizational structures. In this scenario, production scheduling has arisen as an essential function that contributes to the effective allocation of organizational resources in the production process, while shortening production time and satisfying customer deadlines. Consequently, the objective of this study is to develop an advanced method to optimize the production scheduling problem. Accordingly, this study proposes an innovative genetic algorithm-based master schedule (GA-MS) focused on minimizing makespan. The proposed method is implemented in various benchmark datasets on job shop scheduling problems. Compared with some benchmark methods such as priority rules-based approaches, branch and bound algorithm (B&B), and shifting bottleneck algorithm (SB), the proposed GA-MS shows an outstanding performance concerning the tested datasets. Its application in practical manufacturing factories is highly recommended.
References
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