Solving a Class of Job-Shop Scheduling Problem based on Improved BPSO Algorithm

Analyzing the special job shop scheduling problem of a large-scale machine shop, considering workers operational qualification and characteristics of discretely concurrent production, a novel mathematical model has been proposed to meet actual production. In addition, an improved binary particle swarm optimization (BPSO) algorithm has been developed for solving the problem of arranging m workers to process n structures, to optimize the minimum completion time of the jobs. In this improved BPSO, a new method of making initial particles has been presented for searching the optimum particle in the feasible dimensional problem space. Besides, importing memory base, modifying Sig function, and considering constraint condition have been used in the algorithm for making updated particles meet the constraint equation of the mathematical model. The research on the algorithm examples demonstrates that the improved BPSO algorithm is effective and can achieve good results. Moreover, the mathematical model has wide application in discrete manufacture.