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Original scientific paper
https://doi.org/10.2498/cit.2002.03.09

A Cluster-based Evolutionary Algorithm for the Single Machine Total Weighted Tardiness-scheduling Problem

István Borgulya

Fulltext: english, pdf (169 KB) pages 211-217 downloads: 320* cite
APA 6th Edition
Borgulya, I. (2002). A Cluster-based Evolutionary Algorithm for the Single Machine Total Weighted Tardiness-scheduling Problem. Journal of computing and information technology, 10 (3), 211-217. https://doi.org/10.2498/cit.2002.03.09
MLA 8th Edition
Borgulya, István. "A Cluster-based Evolutionary Algorithm for the Single Machine Total Weighted Tardiness-scheduling Problem." Journal of computing and information technology, vol. 10, no. 3, 2002, pp. 211-217. https://doi.org/10.2498/cit.2002.03.09. Accessed 9 Jul. 2020.
Chicago 17th Edition
Borgulya, István. "A Cluster-based Evolutionary Algorithm for the Single Machine Total Weighted Tardiness-scheduling Problem." Journal of computing and information technology 10, no. 3 (2002): 211-217. https://doi.org/10.2498/cit.2002.03.09
Harvard
Borgulya, I. (2002). 'A Cluster-based Evolutionary Algorithm for the Single Machine Total Weighted Tardiness-scheduling Problem', Journal of computing and information technology, 10(3), pp. 211-217. https://doi.org/10.2498/cit.2002.03.09
Vancouver
Borgulya I. A Cluster-based Evolutionary Algorithm for the Single Machine Total Weighted Tardiness-scheduling Problem. Journal of computing and information technology [Internet]. 2002 [cited 2020 July 09];10(3):211-217. https://doi.org/10.2498/cit.2002.03.09
IEEE
I. Borgulya, "A Cluster-based Evolutionary Algorithm for the Single Machine Total Weighted Tardiness-scheduling Problem", Journal of computing and information technology, vol.10, no. 3, pp. 211-217, 2002. [Online]. https://doi.org/10.2498/cit.2002.03.09

Abstracts
In this paper a new evolutionary algorithm is described for the single machine total weighted tardiness problem. The operation of this method can be divided in three stages: a cluster forming and two local search stages. In the first stage it approaches some locally optimal solutions by grouping based on similarity. In the second stage it improves the accuracy of the approximation of the solutions with a local search procedure while periodically generating new solutions. In the third stage the algorithm continues the application of the local search procedure. We tested our algorithm on all the benchmark problems of ORLIB. The algorithm managed to find, within an acceptable time limit, the best-known solution for the problems, or found solutions within 1% of the best-known solutions in 99 % of the tasks.

Hrčak ID: 44781

URI
https://hrcak.srce.hr/44781

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