Scheduling of multi-site production problem with the machine maintenance by genetic algorithms
Chan, F. T. S. and Chung, S. H. and Wong, T. C.; Xia, G.P and Deng, X.Q, eds. (2008) Scheduling of multi-site production problem with the machine maintenance by genetic algorithms. In: Proceedings of the 38th International Conference on Computers and Industrial Engineering. Publishing House Electronics Industry, pp. 2057-2061. ISBN 9787121074370
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The significances of Distributed Scheduling (DS) problems have been recognized by researchers in recent years. DS problems are much more complicated than classical scheduling problems because they involve not only the scheduling problems in a single factory, but also the problems in the upper level, which is how to allocate the jobs to suitable factories. In general, DS problems focus on solving two issues simultaneously: (i) allocation of jobs to suitable factories, and (ii) determination of the corresponding production schedules in each factory. Its objective is to maximize system efficiency by finding an optimal plan for a better collaboration among various processes. However, in many papers, machine maintenance has usually been ignored during the production scheduling. In reality, every machine requires maintenance and the maintenance policy applied will directly influence the machine's availability, and consequently the production scheduling. The objective of this paper is to exanimate the influence of the maintenance curves to the distributed scheduling through three sample experiments.
ORCID iDs
Chan, F. T. S., Chung, S. H. and Wong, T. C. ORCID: https://orcid.org/0000-0001-8942-1984; Xia, G.P and Deng, X.Q-
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Item type: Book Section ID code: 48238 Dates: DateEvent1 December 2008PublishedSubjects: Technology > Manufactures
Technology > Engineering (General). Civil engineering (General) > Engineering designDepartment: Faculty of Engineering > Design, Manufacture and Engineering Management Depositing user: Pure Administrator Date deposited: 22 May 2014 11:48 Last modified: 11 Nov 2024 14:55 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/48238