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《Systems Engineering-theory & Practice》 2004-02
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Modified Adaptive Genetic Algorithms for Solving Job-shop Scheduling Problems

WANG Wan-liang~1, WU Qi-di~2, SONG Yi~1 WT(1. College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014; 2. College of Electron and Information Engineering, Tongji University, Shanghai 200092)  
Job-shop scheduling problem(JSP) is one of the most difficulty combinatorial optimization problems. It is widely applied to productive management of enterprise. It is one of the most important links on CIMS. This paper proposed improved adaptive genetic algorithms for solving job-shop scheduling problems according to the idea that the best individual on current generation should be kept to next generation, but the best individual should be crossed and mutated by some probability. The software package for these modified adaptive genetic algorithms are programmed and applied to solving job-shop scheduling problems. These modified methods increase the convergence rate. Especially, the crossover probability and mutation probability are given automatically in the search process. It is important in the engineering.
【Fund】: 国家自然科学基金(60374056);; 国家863计划项目(2002AA412610);; 浙江省科技计划项目(012047)
【CateGory Index】: O224
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