A Multi-objective Production Scheduling Case Study Solved by Genetic Algorithms
The scheduling problem associated with the manufacturing process is very complex, since this involves several constraints and objectives. These constraints are related to production capacity, the quantity of available molds, demand satisfaction and other operational constraints. The main objectives are related to the way to maximize the utilization of manufacturing resources and minimize mold movements. We developed a deterministic crowding genetic algorithm for this multi-objective problem. The algorithm has proved to be a powerful and flexible tool to solve large-scale instances of this real and complex scheduling problem. A genetic optimization procedure is also proposed to search for schedules using genetic algorithms for this multi-objective problem, such that makespan is minimized. One set of real production data were collected to validate the proposed method. Experimental results indicate that the genetically optimized schedules not only improve the operation performance but also minimize the scheduling risks.
production scheduling genetic algorithm multi-objective optimization application.
WU Jingjing KONG Qinghua XU Kelin JIANG Wenxian
Department of Mechanical Engineering Tongji University Shanghai 200092, China;Zhangzhou Institute of Zhangzhou Institute of Technology Zhangzhou, Fujian Province 363000, China
国际会议
武汉
英文
554-559
2007-07-25（万方平台首次上网日期，不代表论文的发表时间）