Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Ant Colony Optimization Algorithm for Workforce Planning: Influence of the Evaporation Parameter

, , ,

DOI: http://dx.doi.org/10.15439/2019F300

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 177181 ()

Full text

Abstract. 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.

References

  1. Alba E., Luque G., Luna F., Parallel Metaheuristics for Workforce Planning, J. Mathematical Modelling and Algorithms, Vol. 6(3), Springer, 2007, 509-528.https://doi.org/10.1007/s10852-007-9058-5
  2. Albayrak G., zdemir ., A state of art review on metaheuristic methods in time-cost trade-off problems, International Journal of Structural and Civil Engineering Research, Vol. 6(1), 2017, 30-34. https://doi.org/10.18178/ijscer.6.1.30-34
  3. Bonabeau E., Dorigo M. and Theraulaz G., Swarm Intelligence: From Natural to Artificial Systems, New York,Oxford University Press, 1999.
  4. Campbell G., A two-stage stochastic program for scheduling and allocating cross-trained workers, J. Operational Research Society 62(6), 2011, 1038-1047. https://doi.org/10.1057/jors.2010.16
  5. Dorigo M, Stutzle T., Ant Colony Optimization, MIT Press, 2004.
  6. Easton F., Service completion estimates for cross-trained workforce schedules under uncertain attendance and demand, Production and Operational Management 23(4), 2014, 660-675. https://doi.org/10.1111/poms.12174
  7. 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. https://doi.org/10.1007/9783−319−99648−64
  8. 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. https://doi.org/10.15439/2017F63
  9. Roeva O., Fidanova S., Luque G., Paprzycki M., Gepner P., Hybrid Ant Colony Optimization Algorithm for Workforce Planning, FedCSIS’2018, IEEE Xplorer, 2018, 233-236. https://doi.org/10.15439/2018F47
  10. 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:MAEB04, 2004, 636-641.
  11. Grzybowska K., Kovcs, G., Sustainable Supply Chain - Supporting Tools, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Vol. 2, 2014, 1321-1329. https://doi.org/10.15439/2014F75
  12. Hewitt M., Chacosky A., Grasman S., Thomas B., Integer programming techniques for solving non-linear workforce planning models with learning, European J of Operational Research 242(3),2015, 942-950. https://doi.org/10.1016/j.ejor.2014.10.060
  13. Hu K., Zhang X., Gen M., Jo J., A new model for single machine scheduling with uncertain processing time, J Intelligent Manufacturing, Vol 28(3), Springer, 2015, 717-725. https://doi.org/10.1007/s10845-015-1033-9
  14. Li G., Jiang H., He T., A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem, Omega, Vol. 50, Elsevier, 2015, 1-17. https://doi.org/10.1016/j.omega.2014.07.003
  15. Li R., Liu G., An uncertain goal programming model for machine scheduling problem. J. Inteligent Manufacturing, Vol. 28(3), Springer, 2014, 689-694. https://doi.org/10.1007/s10845-014-0982-8
  16. Mucherino A., Fidanova S., Ganzha M., Introducing the environment in ant colony optimization, Recent Advances in Computational Optimization, Studies in Computational Intelligence, Vol. 655, 2016, 147-158. https://doi.org/10.1007/9783−319−40132−49
  17. Ning Y., Liu J., Yan L., Uncertain aggregate production planning, Soft Computing, Vol. 17(4), Springer, 2013, 617-624. https://doi.org/10.1007/s00500-012-0931-4
  18. Othman M., Bhuiyan N., Gouw G., Integrating workers’ differences into workforce planning, Computers and Industrial Engineering, Vol. 63(4), 2012, 1096-1106. https://doi.org/10.1016/j.cie.2012.06.015
  19. Parisio A, Jones CN., A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand, Omega, Vol. 53, Elsevier, 2015, 97-103. https://doi.org/10.1016/j.omega.2015.01.003
  20. Roeva O., Atanassova V., Cuckoo search algorithm for model parameter identification, Int. J. Bioautomation, Vol. 20(4), 2016, 483-492.
  21. 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. https://doi.org/10.1016/j.eswa.2013.06.073
  22. Tilahun S.L., Ngnotchouye J.M.T., Firefly algorithm for discrete optimization problems: A survey, Journal of Civil Engineering, Vol. 21(2), 2017, 535-545. https://doi.org/10.1007/s12205-017-1501-1
  23. Toimil D., Gmes A., Review of metaheuristics applied to heat exchanger network design, International Transactions in Operational Research, Vol. 24(1-2), 2017, 7-26. https://doi.org/10.1111/itor.12296
  24. Yang G., Tang W., Zhao R., An uncertain workforce planning problem with job satisfaction, Int. J. Machine Learning and Cybernetics, Springer, 2016. https://doi.org/10.1007/s13042-016-0539-6 http://rd.springer.com/article/10.1007/s13042-016-0539-6
  25. Zhou C., Tang W., Zhao R., An uncertain search model for recruitment problem with enterprise performance, J Intelligent Manufacturing, Vol. 28(3), Springer, 2014, 295-704. http://dx.doi.org/10.1007/s10845-014-0997-1