A Genetic Algorithm and Tabu Search for Solving Flexible Job Shop Schedules

作者：Zhang, Guohui^*; Shi, Yang; Gao, Liang

来源：1st International Symposium on Computational Intelligence and Design, Wuhan, PEOPLES R CHINA, 2008-10-17 To 2008-10-18.

DOI：10.1109/ISCID.2008.202

• MULTIPURPOSE MACHINES

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. An improved genetic algorithm combined with local search is proposed to solve the FJSP with makespan criterion. To control the local search and convergence to the global optimal solution, time-varying crossover probability and time varying maximum step size of tabu search are introduced. Representative flexible job shop scheduling benchmark problems are solved in order to test the effectiveness and efficiency of the proposed algorithm. Computational results show that the proposed genetic algorithm is efficient and effective.

• 出版日期2008
• 单位数字制造装备与技术国家重点实验室; 华中科技大学