A genetic algorithm for solving the economic lot scheduling problem in flow shops

Jia Yen Huang*, Ming-Jong Yao

*此作品的通信作者

研究成果: Article同行評審

14 引文 斯高帕斯(Scopus)

摘要

In this study, we propose a hybrid genetic algorithm (HGA) to solve the economic lot scheduling problem in flow shops. The proposed HGA utilizes a so-called Proc PLM heuristic that tests feasibility for the candidate solutions obtained in the evolutionary process of genetic algorithm. When a candidate solution is infeasible, we propose to use a binary search heuristic to 'fix' the candidate solution so as to obtain a feasible solution with the minimal objective value. To evaluate the performance of the proposed HGA, we randomly generate a total of 2100 instances from seven levels of utilization rate ranged from 0.45 to 0.80. We solve each of those 2100 instances by the proposed HGA and the other solution approaches in the literature. Our experiments show that the proposed HGA outperforms traditional methods for solving the economic lot scheduling problem in flow shops.

原文English
頁(從 - 到)3737-3761
頁數25
期刊International Journal of Production Research
46
發行號14
DOIs
出版狀態Published - 1 七月 2008

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