论文标题
量身定制的NSGA-III实例化，用于灵活的车间日程安排
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling
论文作者
论文摘要
为多目标灵活的车间调度问题（FJSP）提出了定制的多目标进化算法（MOEA）。它使用智能初始化方法来丰富第一个生成的人群，并提出各种跨界运营商，以创造更好的后代多样性。尤其是，可以自动调整操作员概率的MIP-EGO配置器，该配置器可以调整算法参数。此外，还采用了不同的本地搜索策略来探索社区以寻求更好的解决方案。通常，算法增强策略可以与任何标准的EMO算法集成。在本文中，它已与NSGA-III结合使用，以求解基准的多目标FJSP，而NSGA-III的现成实现无法解决FJSP。实验结果显示出出色的性能，计算预算较少。
A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP). It uses smart initialization approaches to enrich the first generated population, and proposes various crossover operators to create a better diversity of offspring. Especially, the MIP-EGO configurator, which can tune algorithm parameters, is adopted to automatically tune operator probabilities. Furthermore, different local search strategies are employed to explore the neighborhood for better solutions. In general, the algorithm enhancement strategy can be integrated with any standard EMO algorithm. In this paper, it has been combined with NSGA-III to solve benchmark multi-objective FJSPs, whereas an off-the-shelf implementation of NSGA-III is not capable of solving the FJSP. The experimental results show excellent performance with less computing budget.
