Ant Colony Optimization for Workforce Planning with Hybridization

• Autorzy: Fidanova Stefka , Ganzha Maria

Identyfikatory

DOI

10.15439/2023F9586

• Języki publikacji: EN

Abstrakty

EN

Production organization plays a key role in the success of any enterprise. Workforce planning and assignment is an important element of the production organization. Optimizing workforce planning can improve the overall organization of production. The main goal is to minimize the assignment cost of the workers who will perform the planned work. The problem is known to be NP-hard, therefore we will apply methods from the field of artificial intelligence. The problem is to select workers to be assigned to perform the jobs. This is a difficult optimization problem with very strict constraints. For this reason, most of the existing methods hardly find feasible solutions. We propose Ant Colony Optimization Algorithm with hybridization, combination with local search procedures. We compare and analyze their performance.

Słowa kluczowe

EN

workforce planning; ant colony optimization; metaheuristics; hybrid method; local search

PL

planowanie siły roboczej; optymalizacja kolonii mrówek; metaheurystyka; metoda hybrydowa; wyszukiwanie lokalne

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 955--959
• Opis fizyczny: Bibliogr. 11 poz., wz., tab.

Twórcy

autor

Fidanova Stefka

• Institute of Information and Communication Technology Bulgarian Academy of Sciences Acad. G. Bonchev Str., bl. 25A, Sofia, Bulgaria

autor

Ganzha Maria

• System Research Institute Polish Academy of Sciences Warsaw, Poland

Bibliografia

• 1. Alba E., Luque G., Luna F., Parallel Metaheuristics for Workforce Planning, J. Mathematical Modelling and Algorithms, Vol. 6(3), Springer, 2007, 509-528.
• 2. Bonabeau E., Dorigo M. and Theraulaz G., Swarm Intelligence: From Natural to Artificial Systems, New York,Oxford University Press, 1999.
• 3. Dorigo M, Stutzle T., Ant Colony Optimization, MIT Press, 2004.
• 4. Easton F., Service completion estimates for cross-trained workforce schedules under uncertain attendance and demand, Production and Operational Management 23(4), 2014, 660–675.
• 5. Fidanova S., Roeva O., Paprzycki M., Gepner P., InterCriteria Analysis of ACO Start Startegies, Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, 2016, 547-550.
• 6. Fidanova S., Luquq G., Roeva O., Paprzycki M., Gepner P., Ant Colony Optimization Algorithm for Workforce Planning, FedCSIS’2017, IEEE Xplorer, IEEE catalog number CFP1585N-ART, 2017, 415-419.
• 7. Roeva O., Fidanova S., Luque G., Paprzycki M., Gepner P., Hybrid Ant Colony Optimization Algorithm for Workforce Planning, Annals of Computer Science and Information Systems, Vol. 15, 2018, pp. 233-236. ISSN: 2300-5963, http://dx.doi.org/http://dx.doi.org/10.15439/2018F47.
• 8. Fidanova S., Luque G., New Local Search Procedure for Workforce Planning Problem, CYBERNETICS AND INFORMATION TECHNOLO- GIES, Vol. 206, 2020, 40-48, http://dx.doi.org/10.2478/cait-2020-0059
• 9. Glover F., Kochenberger G., Laguna M., Wubbena, T. Selection and assignment of a skilled workforce to meet job requirements in a fixed planning period. In:MAEB’04, 2004, 636–641.
• 10. Grzybowska K., Kovács, G., Sustainable Supply Chain - Supporting Tools, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Vol. 2, 2014, 1321–1329.
• 11. Soukour A., Devendeville L., Lucet C., Moukrim A., A Memetic algorithm for staff scheduling problem in airport security service, Expert Systems with Applications, Vol. 40(18), 2013, 7504–7512.

Uwagi

1. The work is supported by National Scientific Fund of Bulgaria under the grant DFNI KP-06-N52/5. and the 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 scientific computing”.

2. Thematic Tracks Short Papers

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).