A hybrid particle swarm optimisation algorithm for multi-resource constrained flexible job shop scheduling problem with transportation

In the flexible job processing environment, there exists an insufficient optimisation of spatial and transportation resources. To effectively solve the multi-resource constrained flexible job shop scheduling problem with transportation, it is imperative to consider factors such as the capacity of transportation equipment, limitations of the transportation equipment temporary storage area and job temporary storage area. This article focuses on the coordinated scheduling of processing and transportation tasks, aiming to minimise the makespan and the total equipment running time. A hybrid particle swarm optimisation algorithm (NHPSO) is designed, incorporating genetic algorithm's (GA) crossover and mutation functions to preserve particles' genetic information while enhancing global search capabilities. The simulated annealing (SA) mechanism is also included to boost early-to-mid-stage optimisation ability and broaden solution search range. A neighbourhood search strategy based on the critical chain concept is developed in three evolutionary directions to enhance algorithm effectiveness without getting stuck at local optimums. Finally, the convergence ability and effectiveness of the proposed algorithm are verified by experiments. [Submitted: 4 November 2023; Accepted: 20 July 2024]