A Bi-Learning Evolutionary Algorithm for Transportation-Constrained and Distributed Energy-Efficient Flexible Scheduling

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 article, a bi-learning evolutionary algorithm (BLEA) is proposed to solve the distributed energy-efficient flexible job shop problem with transportation constraints (DEFJSP-T). First, 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.