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Article

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Title

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

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ SzD ] doctoral school student

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

Operations Research and Decisions

Journal year: 2022 | Journal volume: vol. 32 | Journal number: no. 4

Article type

scientific article

Publication language

english

Keywords
EN
  • mathematical optimisation
  • multi-criteria optimisation
  • scheduling
  • job-shop problem
  • MCDA
Abstract

EN The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimi- sation 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 réal- ité (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.

Date of online publication

08.02.2023

Pages (from - to)

57 - 74

DOI

10.37190/ord220404

URL

https://ord.pwr.edu.pl/assets/papers_archive/ord2022vol32no4_4.pdf

License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

70

Impact Factor

0,4

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