A Bi-Learning Evolutionary Algorithm for Transportation-Constrained and Distributed Energy-Efficient Flexible Scheduling
提出双学习进化算法，结合统计学习与进化学习，解决带运输约束的分布式节能柔性作业车间调度问题，实验表明该算法在效率和效果上优于对比方法。
With the rise of globalization and environmental concerns, distributed scheduling and energy-efficient scheduling have become crucial topics in the informational manufacturing system. Additionally, the growing consideration about realistic constraints, such as transportation time and finite transportation resources, has made the scheduling problem increasingly complex. Facing these challenges, special mechanisms are required to improve the efficiency of solving algorithms. In this paper, a bi-learning evolutionary algorithm (BLEA) is proposed to solve the distributed energy-efficient flexible job shop problem with transportation constraints (DEFJSP-T). Firstly, we integrate statistical learning (SL) and evolutionary learning (EL) in the framework, while decomposition and Pareto dominance methods are employed in different stages to handle conflicting objectives. During the SL stage, probability models are established to statistically search for advantageous substructures on each weight vector, and an update mechanism is devised to improve the exploration. In the EL stage, the genetic operators are introduced and an improved local search that takes into account the problem properties is proposed to realize sufficient exploitation. Finally, according to the performance of the SL, a novel switching mechanism between SL and EL is designed to ensure the rational allocation of computing resources. Extensive experiments are conducted to test the performances of the BLEA. The statistical comparison shows that the BLEA is superior in solving the DEFJSP-T in terms of efficiency and effectiveness.