期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 152, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113360
关键词
Flow shop group scheduling; Job-related blocking time; Transportation time; Co-evolutionary genetic algorithm
类别
资金
- National Natural Science Foundation of China [71701016, 71471015]
- Beijing Natural Science Foundation [9174038]
- Humanity and Social Science Youth Foundation of Ministry of Education of China [17YJC630143]
- Fundamental Research Funds for Central Universities [FRF-BD-18-009A]
This study investigates a new two-machine flow shop group scheduling problem with job-related blocking and transportation times, which is derived from the realistic pipe-making process of steel pipe products in the modern steel manufacturing industry. In contrast to the traditional blocking constraint, the attributes of jobs, not the quantity of jobs in the buffer area, are used to determine the need for a blocking feature. The objective is to minimize the makespan. We present a mixed integer linear programming model and prove that the problem is strongly NP-hard. As the problem is a joint decision of two sub-problems, namely group scheduling and job scheduling within each group, a co-evolutionary genetic algorithm (CGA) is proposed to solve it. In the proposed CGA, the two sub-problems are synergistically evolved via a co-evolutionary framework. A block-mining-based artificial chromosome construction strategy is designed to speed up the convergence process. Computational experiments based on actual production data are carried out. The results indicate that the proposed CGA is effective for the considered problem. (C) 2020 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据