Job shop scheduling problem with alternative machines using genetic algorithms

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• 作者：I. A. Chaudhry (1) imran_chaudhry@yahoo.com
• 关键词：Key words alternative machine &#8211 ; genetic algorithm (GA) &#8211 ; job shop &#8211 ; scheduling &#8211 ; spreadsheet
• 刊名：Journal of Central South University of Technology
• 出版年：2012
• 出版时间：May 2012
• 年：2012
• 卷：19
• 期：5
• 页码：1322-1333
• 全文大小：434.0 KB
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• 作者单位：1. National University of Sciences and Technology, Islamabad, Pakistan
• 刊物类别：Engineering
• 刊物主题：Engineering, general
  Metallic Materials
  Chinese Library of Science
  
• 出版者：Central South University, co-published with Springer
• ISSN：1993-0666

文摘

The classical job shop scheduling problem (JSP) is the most popular machine scheduling model in practice and is known as NP-hard. The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed. However, JSP with alternative machines for various operations is an extension of the classical JSP, which allows an operation to be processed by any machine from a given set of machines. Since this problem requires an additional decision of machine allocation during scheduling, it is much more complex than JSP. We present a domain independent genetic algorithm (GA) approach for the job shop scheduling problem with alternative machines. The GA is implemented in a spreadsheet environment. The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures. The result shows that the proposed GA is competitive with the existing approaches. A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.