Since greening scheduling is arousing increasing attention from many manufacturing enterprises, this paper focuses on a flexible job shop greening scheduling problem with crane transportation (FJSGSP-CT). Distinguished from the traditional scheduling model which merely concentrates on machining processes, FJSGSP-CT takes the comprehensive effect of machining and crane transportation processes into consideration. Due to the NP-hard nature of the problem, an efficient hybrid algorithm, particle filter and Levy flight-based decomposed multi-objective evolution hybridized with particle swarm (PLMEAPS), is developed to find feasible solutions. The proposed PLMEAPS benefits from the synergy of decomposed multi-objective evolutionary algorithm (MOEA/D) and particle swarm optimization (PSO). Particle filter and Levy flights are then creatively fused into the framework of PLMEAPS to enhance the computational performance of the algorithm. The introduction of particle filter enriches the diversity of the population and makes it possible to predict the near optimal solutions at each iteration, and the combination of Levy flights has beneficial effect on escaping from local optimum and accelerating convergence speed. The performance of the proposed PLMEAPS is evaluated by comparing with two other high-performing intelligent optimization algorithms, the multi-objective genetic local search (MOGLS) and the multi-objective grey wolf optimizer (MOGWO). The computational results reveal that the proposed PLMEAPS outperforms the other two algorithms both in solutions’ quality and convergence rate when solving FJSGSP-CT.

由于绿化调度引起了许多制造企业的越来越多的关注，因此本文着重讨论了起重机运输中灵活的车间车间绿化调度问题（FJSGSP-CT）。与仅专注于加工过程的传统调度模型不同，FJSGSP-CT考虑了加工和起重机运输过程的综合影响。由于问题的NP难性，开发了一种有效的混合算法，粒子滤波和基于Levy Flight的分解的多目标进化与粒子群混合（PLMEAPS），以找到可行的解决方案。提出的PLMEAPS受益于分解的多目标进化算法（MOEA / D）和粒子群优化（PSO）的协同作用。然后，将粒子过滤器和Levy Flight创造性地融合到PLMEAPS的框架中，以增强算法的计算性能。粒子滤波器的引入丰富了种群的多样性，并使得可以在每次迭代中预测接近最优的解决方案，并且征费飞行的组合对于逃避局部最优和加快收敛速度​​具有有益的作用。通过与其他两种高性能智能优化算法（多目标遗传局部搜索（MOGLS）和多目标灰狼优化器（MOGWO））进行比较，对所提出的PLMEAPS的性能进行了评估。计算结果表明，所提出的PLMEAPS在求解FJSGSP-CT时，在质量和收敛速度上均优于其他两种算法。