Hybrid Multi-Objective Artificial Bee Colony for Flexible Assembly Job Shop with Learning Effect - Du, Zhaosheng; Li, Junqing; Li, Jiake (Mathematics (Basel), 2025)
The flexible job shop scheduling problem is a typical and complex combinatorial optimization problem. In recent years, the assembly problem in job shop scheduling problems has been widely studied. However, most of the studies ignore the learning effect of workers, which may lead to higher costs than necessary. This paper considers a flexible assembly job scheduling problem with learning effect (FAJSPLE) and proposes a hybrid multi-objective artificial bee colony (HMABC) algorithm to solve the problem. Firstly, a mixed integer linear programming model is developed where the maximum completion time (makespan), total energy consumption and total cost are optimized simultaneously. Secondly, a critical path-based mutation strategy was designed to dynamically adjust the level of workers according to the characteristics of the critical path. Finally, the local search capability is enhanced by combining the simulated annealing algorithm (SA), and four search operators with different neighborhood structures are designed. By comparative analysis on different scales instances, the proposed algorithm reduces 55.8 and 958.99 on average over the comparison algorithms for the GD and IGD metrics, respectively; for the C-metric, the proposed algorithm improves 0.036 on average over the comparison algorithms.