The Research And Application Of Hybrid Particle Swarm Optimization Algorithm In Mechanical Shop Scheduling

In the face of unexpected epidemic and other emergency situations,how to shorten the production and processing time of enterprises,reduce scheduling costs,efficient processing of products has become an urgent problem for enterprises to solve.When the demand scale is huge and the mechanical capacity is fixed,the workshop scheduling becomes one of the factors affecting the production efficiency.In recent decades,many algorithms have emerged to solve shop scheduling.Particle swarm optimization(PSO)and tabu search algorithm have been studied and improved by scholars due to their advantages of simplicity and efficiency.Particle swarm optimization has the characteristics of less parameters required,fast convergence speed and easy implementation,but the particle updates its position according to the current optimal and global optimal in the iteration process,and will fall into the local optimal.Tabu search algorithm keeps the historical optimal solution in tabu table to seek other possible optimal solutions to jump out of the local optimal solution,but only the higher quality initial solutions can fully utilize the advantages of the tabu search algorithm.To solve these problems,this paper proposes an improved hybrid particle swarm optimization algorithm(IHPSO)for solving JSP problems.The specific improvements are as follows:(1)A particle swarm optimization algorithm with three files(TAPSO)is used.The external files are used to save elite solutions,solutions with a high fitness improvement rate,and their excellent samples generated by crossover and mutation.And the TAPSO was applied to the job shop scheduling problem.(2)In view of the problem that the out-of-bounds particles of the above algorithm will frequently search for the best in the upper or lower bounds in the optimization process,the boundary symmetric mapping method is adopted in this paper to avoid invalid search and improve the optimization speed.Decreasing weights are used to balance the global and local search capabilities in particle position updates.(3)Taking the global optimal solution of the improved TAPSO algorithm as the initial solution of tabu search algorithm,a hybrid particle swarm optimization model for workshop scheduling was proposed.First of all,this paper uses the test function of single-objective optimization problem to test the performance of IHPSO in solving the optimal value of continuous function,and then compares and analyzes the standard test set and relevant literature for the workshop scheduling problem.The results prove the feasibility and effectiveness of IHPSO algorithm in solving JSP problems.Finally,taking a machining workshop as the actual background,the management and scheduling system of the workshop is developed,and good application results are achieved,which also shows the practical significance of the algorithm.