A three-stage adaptive memetic algorithm for multi-objective optimization of flexible assembly job-shop scheduling problem

The flexible assembly job-shop scheduling problem (FAJSP) widely arises in the manufacturing industry. Various approaches have been designed in recent years to address this problem. However, existing methods have rarely considered assembly process constraints and task assembly wait time. For this reason, this paper proposes a three-stage adaptive memetic algorithm (TA-MA) to solve the FAJSP with process route constraints. Specifically, the proposed algorithm combines memetic algorithms and reinforcement learning. The optimization objectives are completion time, equipment load, and assembly operation waiting time. Moreover, a two-layer integer coding method is proposed to encode the problem, and a reinforcement learning method is introduced to assist the solution search of the memetic algorithm. Further, a three-stage search framework is designed to reasonably equilibrium TA-MA's exploration and mining capabilities as iterations advance. Finally, the effectiveness of the proposed algorithm is assessed through a series of experiments. The outcomes demonstrate that the proposed algorithm is effective and outperforms existing algorithms.