Energy-Efficient Lot-Streaming Scheduling Method of Multi-Resource Constrained Flexible Job Shop
针对多资源约束柔性作业车间的节能批量流调度问题，提出一种基于知识的分批方法和改进多目标进化算法的混合优化方法，在基准问题和机床厂实例中验证了其降低能耗、完工时间和加工成本的有效性。
To cope with the problems of high-computational complexity and multiple locally optimal solutions induced by the coupling of multiple subproblems, conflicting objectives, and integration of resource constraints of the energy-efficient lot-streaming scheduling of multi-resource constrained flexible job shop (γ-shop for short), an energy-efficient lot-streaming scheduling optimization approach based on the knowledge-based lot-splitting method (KLSM) and the improved multiobjective evolutionary algorithm (IMOEA) is presented. First, a flexible job shop lot-splitting scheduling model with the optimization objectives of total energy consumption, makespan, and total processing cost is formulated. Second, a hybrid approach of the KLSM and the IMOEA is designed to solve the model The solution space of the problem is fully explored based on the moth-flame operator. Co-evolutionary operators are performed to promote information interaction among populations, hence both the population diversity and the convergence effect of the algorithm are improved. Moreover, a post-adjustment strategy based on adjacent processes is developed to reduce unnecessary fixture changes. Finally, extended experiments between some KLSM-based well-known and novel algorithms, including the proposed IMOEA, MOEA/D, NSGA-II, MOPSO, SGECF, SCEA, and SLMEA are conducted in benchmark problems and a real-world case of machine tool plant. The results show that the proposed method outperforms its competitors on co-optimization of lot-splitting, machine allocation, operation sequencing, and fixture assignment of the γ-shop scheduling, which can effectively reduce total energy consumption, makespan, and total processing cost.