Scheduling in flexible manufacturing systems (FMS) is acknowledged as a computationally hard problem, as it involves complex production schedules, flexible job parts, and automated guided vehicle (AGV) routings. Consequently, several efficient metaheuristics are proposed to obtain a near-optimal solution. However, each heuristic requires specific parameter calibration, further complicating the problem. Furthermore, efficient scheduling in FMS must address multiple conflicting objectives simultaneously, leading to another set of operational challenges. In this paper, two recently developed, simple yet efficient parameter-less algorithms, namely Jaya and teaching-learning-based optimization (TLBO), are proposed to solve the scheduling problem in FMS. The multi-objective function is used to minimize the machine’s idle time and penalties for jobs that do not meet their due dates. The jobs are categorized in flexible manufacturing cells (FMCs) and processed in a loop layout FMS. First, the external FMC sequence is optimized, followed by the internal job sequence. The rank-priority rule is applied in algorithms with encoding and decoding mechanisms to obtain the optimal production sequences. The effectiveness of the proposed approach is compared with the public benchmarks. Computational results show that TLBO and Jaya outperform most of the popular and efficient heuristics in the literature.
Mishra et al. (Wed,) studied this question.