Mixed Selection Strategy-Assisted Multi-Operator Approach for Job Shop Scheduling Problems

The Job Shop Scheduling Problem (JSP), known for its NP-hard combinatorial complexity, poses a significant challenge in manufacturing. Despite extensive algorithmic advancements targeting optimal or near-optimal solutions with feasible computational costs, JSP remains computationally demanding. Recently, multi-operator and multi-method approaches have garnered attention; however, the potential of communication strategies among perturbation mechanisms remains underexplored. To address this gap, this study introduces a Differential Evolution algorithm featuring three perturbation mechanisms that interact to enhance the evolutionary process in solving JSPs. The proposed communication strategy evaluates the diversity of schedules through a diversity-check mechanism while simultaneously considering solution quality and divergence. By strategically selecting operators to maintain a balance between exploration and exploitation, and incorporating a diversity-check mechanism to prevent premature convergence, this approach significantly enhances solution quality. Benchmark results demonstrate that this hybrid algorithm not only outperforms its constituent algorithms but also surpasses several established methods, including its popular variants and other algorithms. This superior performance underscores the algorithm’s effectiveness and robustness in tackling complex JSP scenarios.