A Hybrid Genetic Algorithm for Optimization Problems in Flowshop Scheduling
Numerous real-world problems relating to flow shop scheduling are complex. The main problem is that the solution space is very large and therefore the set of feasible solutions cannot be enumerated one by one. Current approaches to solve these problems are metaheuristics techniques, which fall in two categories; population-based search and trajectory-based search. Because of their complexity, recent research has turned to genetic algorithms to address such problems. This paper gives an overall view for the problems in production scheduling where considerable emphasis is put on genetic algorithms and the evaluation of trade-off solutions. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore an integer programming model is developed by using a hybrid genetic algorithm for the problem which belongs to NP-hard class. Experimental results of a flow shop scheduling problem indicate that the hybrid genetic algorithm outperforms the other methods.
Hybrid Genetic Algorithm Optimization Flowshop Scheduling Mathematical Programming
Wu Jingjing Xu Kelin Kong Qinghua Jiang Wenxian
School of Mechanical Engineering, Tongji University, Shanghai P.R.China, 200092;Zhangzhou Institute School of Mechanical Engineering, Tongji University, Shanghai P.R.China, 200092 Zhangzhou Post Office, Zhangzhou P.R.China, 363000
国际会议
天津
英文
2007-10-20（万方平台首次上网日期，不代表论文的发表时间）