Research on Distributed Hybrid Flow Workshop Scheduling Using a Multi-Objective Hybrid Genetic Algorithm

With the rapid advancement of intelligent and distributed manufacturing, the Distributed Hybrid Flow Shop Scheduling Problem (DHFSP) has emerged as a key area of research in industrial scheduling, focusing on multi-factory collaboration, multi-objective optimization, and complex constraints. This article introduces a Multi-Objective Hybrid Genetic Algorithm (MOHGA) that employs a non-dominated sorting strategy along with a local search mechanism to create a distributed scheduling model aimed at minimizing total completion time, maximum delay, and energy consumption. Simulation tests and algorithm comparisons demonstrate that MOHGA excels in convergence, diversity, and solution quality. Experimental results indicate that MOHGA achieves an 18.7% reduction in total energy consumption and a 12.3% improvement in the Hypervolume index compared to MOPSO with 100 workpieces, offering an effective solution for distributed scheduling.