A Knowledge-Driven Bi-Population Optimization Algorithm for Distributed Heterogeneous No-Idle Flow Shop Scheduling problem with Batch Delivery and Setup Times

This paper investigates the distributed heterogeneous no-idle flow shop scheduling problem with the objective of minimizing the makespan and total energy consumption, while considering setup times and batch deliveries. Given the challenges of simultaneously optimizing these two objectives, a Knowledge-Driven Bipopulation Evolutionary Algorithm (KDBEA) is proposed to address this issue. First, the algorithm employs four arrays for encoding, which correspond to factory allocation, job sequencing, batch allocation, and speed allocation. Second, various types of evolutionary operators are designed and combined with adaptive strategies to guide the dual populations toward efficient evolution. Finally, a knowledge-guided local search strategy is implemented to enhance the algorithm’s exploratory capabilities. To verify the effectiveness of the proposed KDBEA, a large number of experiments were conducted and it was compared with three other advanced algorithms.The experimental results show that KDBEA is superior to its competitors.