Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA

• Autorzy: Jurczak Marcin , Miebs Grzegorz , Bachorz Rafał A.

Identyfikatory

DOI

10.37190/ord220404

• Języki publikacji: EN

Abstrakty

EN

The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI), reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method.

Słowa kluczowe

EN

mathematical optimisation; multi-criteria optimisation; scheduling; job shop problem; MCDA

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2022
• Tom: Vol. 32, No. 4
• Strony: 57--74
• Opis fizyczny: Bibliogr. 68 poz., rys.

Twórcy

autor

Jurczak Marcin

• Department of Logistics, Poznań University of Economics and Business, Poznań, Poland

• https://orcid.org/0000-0002-0828-308X

autor

Miebs Grzegorz

• Advanced Analytics Team, PSI Poland, Poznań, Poland
• nstitute of Computing Science, Faculty of Computing, Poznan University of Technology, Poznań, Poland

• https://orcid.org/0000-0002-0828-308X

autor

Bachorz Rafał A.

• Advanced Analytics Team, PSI Poland, Poznań, Poland
• Institute of Medical Biology of Polish Academy of Sciences, Łódź, Poland

• https://orcid.org/0000-0002-6940-9432

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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).