Scheduling in flexible manufacturing systems with a new genetic algorithm

While scheduling in traditional manufacturing systems, such as flow and job shops, has been extensively studied, there is a noticeable lack of research on scheduling for the newer flexible manufacturing systems. This paper explores scheduling within flexible manufacturing systems that incorporate both machine and routing flexibilities. 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 by leveraging the optimal schedule's characteristics. Lastly, we conduct computational experiments to demonstrate our algorithm's effectiveness. The results indicate that the proposed algorithm can achieve high-quality solutions within a reasonable computational timeframe.

Keywords:

scheduling , flexible manufacturing systems , mathematical model , genetic algorithm