An Improved Genetic Algorithm for Dual-Resource Constrained Flexible Job Shop Scheduling
In this paper, a dual-resource constrained job shop scheduling problem was studied. According to the information processing mechanism of an immune system in biotic science, a new immune Genetic Algorithm for flexible job shop scheduling through combining immune algorithm with genetic algorithm was proposed. The algorithm can effectively avoid the premature convergence problem caused by the high selective pressure. Moreover, it improves the ability of searching an optimum solution and increases the convergent speed. The operation-based encoding and an active schedule decoding method were employed, and several kinds of crossover operations were adopted in order to keep individual diversity and to improve the level of adaptability of the individual diversity hi the population. This new algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. Compared with the solutions suggested by other researchers, the simulations show that the developed algorithm can search for better solution on make-span and that it is available and efficient.
Genetic Algorithm Dual-Resource Flexible Job Shop Scheduling lmmune
Cao Xianzhou Yang Zhenhe
Henan University of Technology, Zhengzhou, Henan, 450007, China Hunan Provincial Cereal and Oil Scientific Research and Design Institute, Changsha, Hunan, 410008, C
国际会议
深圳
英文
42-45
2011-03-28（万方平台首次上网日期，不代表论文的发表时间）