Analysis of modification of the evolutionary algorithm for sequencing production tasks

• Autorzy: Ciepliński Piotr , Golak Sławomir , Wieczorek Tadeusz

Identyfikatory

DOI

10.7494/cmms.2022.3.0783

• Języki publikacji: EN

Abstrakty

EN

Evolutionary algorithms are one of the heuristic techniques used to solve task sequencing problems. An important example of such a problem is the issue of sequencing production tasks. The combinatorial optimization of task sequences allows the minimization of the cost or time of a set of production tasks by reducing the components of these values which are present in the transitions between tasks. This paper aims to analyze the influence of the production nature expressed by a set of production task parameters and a definition of the task transition cost on the effectiveness of the modification of the evolutionary algorithm based on new directed stochastic mutation operators. The research carried out included the influence of the space dimension of the task parameters, the number of levels of the value of the cost function, and a definition of this function. The results obtained allow us to assess the effectiveness of the directed mutation in task sequencing for productions of various natures.

Słowa kluczowe

EN

evolutionary algorithm; task sequencing; mutation operator

PL

algorytm ewolucyjny; operator mutacji

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2022
• Tom: Vol. 22, No. 3
• Strony: 157--166
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Ciepliński Piotr

• Department of Industrial Computer Science, Faculty of Materials Engineering, Silesian University of Technology, Katowice, Poland

• https://orcid.org/0000-0001-9056-0367

autor

Golak Sławomir

• Department of Industrial Computer Science, Faculty of Materials Engineering, Silesian University of Technology, Katowice, Poland

• https://orcid.org/0000-0002-9325-3407

autor

Wieczorek Tadeusz

• Department of Industrial Computer Science, Faculty of Materials Engineering, Silesian University of Technology, Katowice, Poland

• https://orcid.org/0000-0002-9310-9798

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).