文献类型：期刊文献

英文题名：Optimization of multi-stage production operations based on genetic algorithm

作者：Yu, Chanjuan[1]; Dong, Shaohua[1]; Yang, Zhongqing[1]; Sun, Jing[2]; Wang, Guotao[1]

第一作者：Yu, Chanjuan

机构：[1] School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; [2] Department of Industrial Engineering and Logistics, Beijing Union University, Beijing, 100020, China

第一机构：School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

年份：2016

卷号：17

期号：43

起止页码：18.1-18.8

外文期刊名：International Journal of Simulation: Systems, Science and Technology

收录：EI(收录号：20165103147821);Scopus(收录号：2-s2.0-85006172622)

语种：英文

外文关键词：Genetic algorithms - Optimization - Production control - Scheduling

摘要：This paper introduce a methodology, which is used to solve a multi-stage production planning problem having multiple objectives, which are conflicting in nature. The production process consists of multiple stages having one or more machines in each stage. Every processing stage produces work-in-process, semi-finished and finished items as an output, some becomes an input to the subsequent stage either fully or partially depending on the cycle times of the machines, the rest exports as products. Events of machine breakdowns and imbalances in input–output relations in one or more stages may affect both work-in-process (WIP) and final production targets. Here develop a multi stage production operation model based on genetic algorithm. Our analytic illustrate that multi stage is more complex than single stage production operation. The attributes of the cost of multi stage production include the production cost, the warehousing cost, the transportation cost, the contract penalty cost and the productivity cost of idle. The numerical results show that it can reduce overall cost, improve scheduling rationality and enhance the efficiency of production scheduling. The finding are of far-reaching importance in view of the multi stage production operation and guide the enterprise to find a quick and science way to Increase benefit. ? 2016, UK Simulation Society. All rights reserved.