Improved Particle Swarm Optimization to Solve the Integration Problem of Production Planning and Flexible Job Shop Scheduling
In order to solve the problem of incompatibility between the production plan formulated by the machining enterprise and the workshop scheduling scheme,the production planning and flexible operation workshop scheduling integration model with the objec-tive function of minimizing the maximum completion time and minimizing the processing cost was established.An improved particle swarm optimization(IPSO)algorithm was proposed as a global optimization algorithm.Based on the traditional particle swarm optimiza-tion(PSO),the genetic operator crossover method was introduced to improve the population evolution,and the random boundary varia-tion was designed to improve the population diversity and avoid local optimization.The power function was use for the learning factor and inertia weight to dynamically change to enhance their search ability and converge faster.Finally,through production examples,the feasi-bility of IPSO in solving the integration problem of production planning and workshop scheduling was verified.At the same time,the PSO,grey wolf optimizer(GWO)and genetic algorithm(GA)were used as comparative algorithms,and experiments were carried out on 15 basic examples of Brandimarte.The results obtained are better than other algorithms,which proves the effectiveness and superiority of IPSO in solving flexible job shop scheduling problems.
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