Research on Improved Particle Swarm Optimization Algorithm in Flexible Job-Shop Scheduling Problem
In order to reduce the processing time of the flexible job-shop scheduling problem(FJSP),a mathematical model of the processing satisfaction of the FJSP is constructed through the prospect theory,an improved particle swarm adjusted by inertia weight power function is designed to reduce the processing time,the inertia weight of the algorithm can be adaptively adjusted by the power function,and the learning factor can be decreased or increased accordingly to balance the global exploration and local development capabilities of the algorithm.Selecting the classical examples of BRdata and Kacem,and comparing the PPSO algo-rithm with the artificial immune algorithm(AIA)and the integrated simulated annealing algorithm(ISA),the processing time of PPSOis shorter;In the example of BRdata,when the number of work pieces is J=25,the number of available machines is M=16,and the number of iterations is 600,compared with the AIA algorithm and the ISA algorithm,the processing time of the PPSO is shortened by 21.61%and 4.32%respectively.The verification results show that the PPSO algorithm is effective in reducing pro-cessing time in the flexible job-shop scheduling problem.
Improved Particle Swarm OptimizationFJSPProspect TheoryInertia Weight Power FunctionPro-cessing Time
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