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

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

3 Zitate (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.

OriginalspracheEnglisch
Titel31st European Modeling and Simulation Symposium, EMSS 2019
Redakteure/-innenMichael Affenzeller, Agostino G. Bruzzone, Francesco Longo, Guilherme Pereira
Herausgeber (Verlag)DIME UNIVERSITY OF GENOA
Seiten254-261
Seitenumfang8
ISBN (elektronisch)9788885741263
PublikationsstatusVeröffentlicht - 2019
Veranstaltung31st European Modeling and Simulation Symposium, EMSS 2019 - Lisbon, Portugal
Dauer: 18 Sep 201920 Sep 2019

Publikationsreihe

Name31st European Modeling and Simulation Symposium, EMSS 2019

Konferenz

Konferenz31st European Modeling and Simulation Symposium, EMSS 2019
Land/GebietPortugal
OrtLisbon
Zeitraum18.09.201920.09.2019

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