Genetic algorithm based multi-objective scheduling in a flow shop with batch processing machines
In this paper, the problem of minimizing makespan and the total tardiness in a flow shop with batch processing machines (BPM) is considered and an efficient genetic algorithm (GA) is presented, in which job permutation is the only optimization object and the solution of problem can be directly obtained using the permutation. To obtain a set of nondominated solutions, a rank and the weighted objective based binary tournament selection and an external archive updating strategy are also adopted. The proposed GA is finally tested and the computational results show its promising performance on multi-objective scheduling of flow shop with BPM.
Deming Lei Qiongfang Zhang Wen Cheng Tao Wang Xiuping Guo
School of Automation, Wuhan University of Technology University of Springfield Wuhan City, Hubei Pro Department of management science and engineering School of economics and management Southwest iaoto
国际会议
The 8th World Congress on Intelligent Control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)
济南
英文
694-699
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）