Research On Flexible Workshop Scheduling Based On Collaborative Mechanism Group Optimization Algorith

Under the trend of intelligent industrialization,industrial informatization,and intelligence have become the future development trend.Traditional production methods are unable to cope with the changeable market environment,resulting in the development of traditional manufacturing towards intelligent manufacturing.As part of intelligent manufacturing,the optimization of workshop scheduling schemes plays a crucial role.The flexible workshop scheduling problem is the most common scheduling model in workshop production,due to its high flexibility and suitability for the current multivariety,small-batch production mode.However,it is an NP-hard problem with a complex model,leading to extensive attention and research by scholars.This thesis aims to solve the single-objective flexible workshop scheduling problem and the multi-objective flexible workshop scheduling problem using an improved collaborative mechanism optimization algorithm.Firstly,the improved collaborative particle swarm algorithm is employed to address the single-objective flexible workshop scheduling problem with the optimization goal of maximizing completion time.To overcome the limitations of the PSO algorithm in solving the flexible workshop scheduling problem,such as reduced search ability in large calculation cases,susceptibility to local optimization,and poor algorithm effectiveness due to machine load imbalance,a learning co-evolution algorithm based on GA and PSO is proposed.This algorithm enhances diversity by sharing optimal individuals from both GA and PSO algorithms,thus avoiding local optima.Additionally,a variable neighborhood search method is designed to improve the local search ability of the algorithm.The chromosomal structure of the optimal individual in each generation is analyzed to influence the population’s next generation,thereby enhancing the quality of newly generated chromosomes.Experimental results comparing the improved particle swarm coevolution algorithm with other intelligent optimization algorithms,including the MK study in the benchmark,demonstrate its strong search ability in small cases and good global search ability with increased computing power.Furthermore,the multi-objective flexible workshop scheduling problem is addressed by considering optimization goals such as maximum completion time,machine energy consumption,and total machine load.An optimal initialization method is provided based on the three objectives.To improve the algorithm’s search ability,strategies such as non-uniform mutation,elite strategy,Levy flight strategy,and golden section are adopted.By solving multi-objective test functions,conducting standard and real studies,and comparing the proposed algorithm with commonly used multi-objective algorithms,the effectiveness of the collaborative particle swarm algorithm for solving the multi-objective flexible job shop scheduling problem is demonstrated.Finally,the multi-objective collaborative particle swarm algorithm is utilized to optimize real workshop data,considering both minimum completion time and singleobjective optimization,as well as the maximum completion time,machine energy consumption,and total machine load for multi-objective optimization.The solution provides a feasible production scheme for the workshop,offering effective decisionmaking references for decision-makers.