A Multi-Objective Grey Wolf Optimizer for Heterogeneous Hybrid Flow Shop Scheduling in Mass Customization

Against the backdrop of mass customization, research interest in hybrid flow shop scheduling for standard and customized part production has been on the rise. However, most extant studies focus on single-shop scheduling optimization, and the inter-shop coordination mechanism for heterogeneous multi-shop systems remains underexplored. This paper investigates a heterogeneous hybrid flow shop scheduling problem featuring a distributed flow shop for standardized parts and a flexible job shop for customized parts, with the dual objectives of minimizing makespan and total cost. For this problem with the core complexity of heterogeneous cross-shop production reliance and conflicting dual-objective optimization, we propose a multi-objective grey wolf optimizer (MOGWO) combined with problem-specific local search strategies. Computational experiments on a set of test instances are carried out to evaluate the MOGWO’s performance, which is further compared with four classic multi-objective evolutionary algorithms of analogous algorithmic frameworks. Experimental results confirm that the proposed algorithm achieves superior solution quality and convergence efficiency for the multi-objective heterogeneous hybrid flow shop scheduling problem under study.