期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 51, 期 17, 页码 5100-5119出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2013.793425
关键词
intuitionistic fuzzy sets; job-shop scheduling problem; genetic algorithm; uncertain information
资金
- National Natural Science Foundation of China [61174022, 71271061]
- Chongqing Natural Science Foundation [2010BA2003]
- National High Technology Research and Development Program of China (863 Program) [2013AA013801]
- Science and Technology Planning Project of Guangdong Province, China [2010B010600034]
- Southwest University Scientific & Technological Innovation Fund for Postgraduates [ky2011011]
- Fundamental Research Funds for the Central Universities [XDJK2013D010]
The Job-Shop Scheduling Problem (JSP) is an important concern in advanced manufacturing systems. In real applications, uncertainties exist practically everywhere in the JSP, ranging from engineering design to product manufacturing, product operating conditions and maintenance. A variety of approaches have been proposed to handle the uncertain information. Among them, the Intuitionistic Fuzzy Sets (IFS) is a novel tool with the ability to handle vague information and is widely used in many fields. This paper develops a method to address the JSP under an uncertain environment based on IFSs. Another contribution of this paper is to put forward a generalised (or extended) IFS to process the additive operation and to compare the operation between two IFSs. The methodology is illustrated using a three-step procedure. First, a transformation is constructed to convert the uncertain information in the JSP into the corresponding IFS. Secondly, a novel addition operation between two IFSs is proposed that is suitable for the JSP. Then a novel comparison operation on two IFSs is presented. Finally, a procedure is constructed using the chromosome of an operation-based representation and a genetic algorithm. Two examples are used to demonstrate the efficiency of the proposed method. In addition, a comparison between the results of the proposed IFSJSP and other existing approaches demonstrates that IFSJSP significantly outperforms other existing methods.
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