A Novel Genetic Algorithm for Optimizing Scheduling in Flexible Manufacturing Systems: Design, Implementation, and Performance Evaluation

While scheduling in traditional manufacturing systems, such as flow and job shops, has been extensively studied, there is a significant gap in research related to scheduling in newer flexible manufacturing systems. This paper explores scheduling within flexible manufacturing systems that incorporate both machine and routing flexibility. Initially, we propose two mathematical models in the form of mixed-integer linear programs to address this issue. The first model focuses on positioning, while the second addresses sequencing. These models are capable of optimally solving small-scale problems. In the subsequent phase, acknowledging the NP-hard nature of the problem, we develop an efficient genetic algorithm designed for large-scale scenarios, leveraging the characteristics of the optimal schedule. Finally, we conduct computational experiments to demonstrate the effectiveness of our algorithm. The results indicate that the proposed algorithm can achieve high-quality solutions within a reasonable computational timeframe. This research specifically focuses on optimizing scheduling in flexible manufacturing systems and presents a novel approach to the design of scheduling algorithms.

کلیدواژه ها:

scheduling ، flexible manufacturing systems ، mathematical model ، genetic algorithm