Research On Flexible Job Shop Scheduling Problem Based On Improved Discrete Particle Swarm Algorithm

As the manufacturing industry continues to develop,the flexible job shop production method with a variety of categories and small batches has gradually become the mainstream approach.However,the scheduling problem associated with the flexible job shop is a complex combinatorial optimization problem.Traditional solution methods often suffer from slow solution speeds and are unable to solve large-scale problems.In light of these challenges,this study aims to address the problem by proposing an improved discrete particle swarm algorithm.The main focus of this algorithm will be as follows:(1)This paper proposes a novel hybrid discrete particle swarm simulated annealing algorithm for solving the flexible job shop scheduling problem with the objective of minimizing order completion time.which utilizes a nonlinear adaptive weight to improve the balance between global and local search performance of the discrete particle swarm algorithm.Additionally,the algorithm incorporates the principles of simulated annealing to design two neighborhood structures for local exploration of certain particles,enabling the algorithm to escape from local optima.To validate the performance of the proposed algorithm,several test cases are solved and compared with other algorithms.The results confirm the performance of the hybrid algorithm.(2)This paper presents an improved discrete particle swarm algorithm for solving a multi-objective flexible job shop scheduling problem with the optimization objectives of order completion time,maximum machine load,and minimum total machine load.To address this complex problem,several enhancements are proposed,including integrating the Pareto theory into the algorithm,redesigning the population initialization method and decoding strategy,and introducing a new update strategy for individual optimal and global optimal particles.Moreover,to further improve the search performance,a variable neighborhood search strategy is integrated,and three neighborhood structures are designed for local exploration.Experimental results demonstrate the effectiveness of the proposed algorithm in solving multi-objective problems.(3)we also investigate the rescheduling problem under dynamic events such as machine failure and order expediting,and propose a solution for the flexible job shop problem under perturbation events.Our algorithm is designed to handle these dynamic events and ensure efficient rescheduling.The feasibility and effectiveness of the proposed algorithm are validated through several sets of experiments.