A Cauchy-mutation-based Self-adapted Multi-objective Equilibrium Optimizer for Hybrid Flowshop Scheduling Problem with Shared-track Transporting Robots

Hybrid flowshop, as a flexible production mode, has been widely applied in various manufacturing scenarios. However, research related to hybrid flowshop scheduling often overlooks the scheduling of workpiece transportation within the workshop, focusing instead solely on the sequencing of workpiece processing and machine selection decisions. To enrich research in this area, this paper investigates a hybrid flow shop scheduling problem that includes shared-track transporting robots and considers conflicts in the paths between robots. To effectively address this problem, a Cauchy-mutation-based self-adapted multi-objective equilibrium optimizer (CSMEO) algorithm is proposed. Encoding and decoding are tailored to the specific characteristics of the problem to ensure the feasibility of the obtained scheduling solutions. Numerical experimental results indicate that the performance of CSMEO surpasses that of comparative algorithms in solving the problem presented in this paper.