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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

Puni tekst: engleski, pdf (169 KB) str. 211-217 preuzimanja: 243* citiraj
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, br. 3, 2002, str. 211-217. https://doi.org/10.2498/cit.2002.03.09. Citirano 17.07.2019.
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, br. 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), str. 211-217. doi: 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 [pristupljeno 17.07.2019.];10(3):211-217. doi: 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, br. 3, str. 211-217, 2002. [Online]. doi: https://doi.org/10.2498/cit.2002.03.09

Sažetak
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

Posjeta: 344 *