Ant colony optimization algorithm for workforce planning: influence of the evaporation parameter

• Autorzy: Fidanova Stefka , Luque Gabriel , Roeva Olympia , Ganzha Maria

Identyfikatory

DOI

10.15439/2019F300

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Optimization of the production process is important for every factory or organization. The better organization can be done by optimization of the workforce planing. The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done. The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence. The problem is to select employers and to assign them to the jobs to be performed. The constraints of this problem are very strong and for the algorithms is difficult to find feasible solutions. We apply Ant Colony Optimization Algorithm to solve the problem. We investigate the algorithm performance according evaporation parameter. The aim is to find the best parameter setting.

Słowa kluczowe

EN

workforce planning; ant colony optimization; metaheuristics; evaporation parameter

PL

planowanie siły roboczej; optymalizacja kolonii mrówek; metaheurystyka

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 177--181
• Opis fizyczny: Bibliogr. 25 poz., wz., rys.

Twórcy

autor

Fidanova Stefka

• IICT, BAS, Sofia, Bulgaria

autor

Luque Gabriel

• DLCS University of Málaga 29071 Málaga, Spain

autor

Roeva Olympia

• IBPhBME, BAS, Sofia, Bulgaria

autor

Ganzha Maria

• SRI, PAS, Warsaw, Poland

Bibliografia

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Uwagi

1. Work presented here is partially supported by the National Scientific Fund of Bulgaria under grant DFNI DN12/5 “Efficient Stochastic Methods and Algorithms for Large-Scale Problems”, Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program and co-financed by the European Union through the European structural and Investment funds, and by the Polish-Bulgarian collaborative grant “Practical aspects for sciantific computing”.

2. Track 1: Artificial Intelligence and Applications

3. Technical Session: 12th International Workshop on Computational Optimization

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).