A new solution encoding for simulation-based multi-objective workforce qualification optimization

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

Solutions for combinatorial problems can be represented by simple encodings, e.g. vectors of binary or integer values or permutations. For such encodings, various specialized operators have been proposed and implemented. In workforce qualification optimization, qualification matrices can for example be encoded in the form of binary vectors. Though simple, this encoding is rather general and existing operators might not work too well considering the genotype is a binary vector, whereas the phenotype is a qualification matrix. Therefore, a new solution encoding that assigns a number of workers to qualification groups is implemented. By conducting experiments with NSGA-II and the newly developed encoding, we show that having an appropriate mapping between genotype and phenotype, as well as more specialized genetic operators, helps the overall multiobjective search process. Solutions found using the specialized encoding mostly dominate the ones found using a binary vector encoding.

Original languageEnglish
Title of host publication31st European Modeling and Simulation Symposium, EMSS 2019
EditorsMichael Affenzeller, Agostino G. Bruzzone, Francesco Longo, Guilherme Pereira
PublisherDIME UNIVERSITY OF GENOA
Pages254-261
Number of pages8
ISBN (Electronic)9788885741263
Publication statusPublished - 2019
Event31st European Modeling and Simulation Symposium, EMSS 2019 - Lisbon, Portugal
Duration: 18 Sep 201920 Sep 2019

Publication series

Name31st European Modeling and Simulation Symposium, EMSS 2019

Conference

Conference31st European Modeling and Simulation Symposium, EMSS 2019
CountryPortugal
CityLisbon
Period18.09.201920.09.2019

Keywords

  • Encoding
  • Multiobjective optimization
  • NSGA-II
  • Simulation
  • Workforce qualification

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