Skip to main content
Springer Nature Link
Log in

Influence of the ACO Evaporation Parameter for Unstructured Workforce Planning Problem

  • Conference paper
  • First Online:
Large-Scale Scientific Computing (LSSC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13127))

Included in the following conference series:

  • 1047 Accesses

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 especially when the problem is unstructured. 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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alba E., Luque G., Luna F., Parallel metaheuristics for workforce planning. J. Math. Model. Algorithms 6(3), 509–528 (2007). https://doi.org/10.1007/s10852-007-9058-5

  2. Albayrak, G., Özdemir, İ: A state of art review on metaheuristic methods in time-cost trade-off problems. Int. J. Struct. Civ. Eng. Res. 6(1), 30–34 (2017)

    Article  Google Scholar 

  3. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    Book  Google Scholar 

  4. Campbell, G.: A two-stage stochastic program for scheduling and allocating cross-trained workers. J. Oper. Res. Soc. 62(6), 1038–1047 (2011)

    Article  Google Scholar 

  5. Dorigo M, Stutzle T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Google Scholar 

  6. Easton, F.: Service completion estimates for cross-trained workforce schedules under uncertain attendance and demand. Prod. Oper. Manag. 23(4), 660–675 (2014)

    Article  Google Scholar 

  7. Fidanova, S., Roeva, O., Paprzycki, M., Gepner, P.: InterCriteria analysis of ACO start startegies. In: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, pp. 547–550 (2016)

    Google Scholar 

  8. Fidanova, S., Luque, G., Roeva, O., Paprzycki, M., Gepner, P.: Ant colony optimization algorithm for workforce planning. In: FedCSIS 2017, IEEE Xplorer, IEEE Catalog Number CFP1585N-ART, pp. 415–419 (2017)

    Google Scholar 

  9. Roeva, O., Fidanova, S., Luque, G., Paprzycki, M., Gepner, P.: Hybrid ant colony optimization algorithm for workforce planning. In: FedCSIS 2018, IEEE Xplorer, pp. 233–236 (2018)

    Google Scholar 

  10. Fidanova, S., Luque, G., Roeva, O., Ganzha, M.: Ant colony optimization algorithm for workforce planning: influence of the evaporation parameter. In: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, Annals of Computer Science and Information Systems, pp. 181–185 (2019). ISSN: 2300-5963

    Google Scholar 

  11. 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 2004, pp. 636–641 (2004)

    Google Scholar 

  12. Grzybowska, K., Kovács, G.: Sustainable supply chain - supporting tools. In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, vol. 2, pp. 1321–1329 (2014)

    Google Scholar 

  13. Hewitt, M., Chacosky, A., Grasman, S., Thomas, B.: Integer programming techniques for solving non-linear workforce planning models with learning. Eur. J. Oper. Res. 242(3), 942–950 (2015)

    Article  MathSciNet  Google Scholar 

  14. Hu, K., Zhang, X., Gen, M., Jo, J.: A new model for single machine scheduling with uncertain processing time. J. Intell. Manuf. 28(3), 717–725 (2015). https://doi.org/10.1007/s10845-015-1033-9

  15. Li, G., Jiang, H., He, T.: A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem. Omega 50, 1–17 (2015)

    Google Scholar 

  16. Li, R., Liu, G.: An uncertain goal programming model for machine scheduling problem. J. Intell. Manuf. 28(3), 689–694 (2014). https://doi.org/10.1007/s10845-014-0982-8

  17. Mucherino, A., Fidanova, S., Ganzha, M.: Introducing the environment in ant colony optimization. In: Fidanova, S. (ed.) Recent Advances in Computational Optimization. SCI, vol. 655, pp. 147–158. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40132-4_9

    Chapter  Google Scholar 

  18. Ning Y., Liu J., Yan L., Uncertain aggregate production planning. Soft Comput. 17(4), 617–624 (2013)

    Google Scholar 

  19. Othman, M., Bhuiyan, N., Gouw, G.: Integrating workers’ differences into workforce planning. Comput. Ind. Eng. 63(4), 1096–1106 (2012)

    Article  Google Scholar 

  20. Parisio, A., Jones, C.N.: A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand. Omega 53, 97–103 (2015)

    Google Scholar 

  21. Roeva, O., Atanassova, V.: Cuckoo search algorithm for model parameter identification. Int. J. Bioautom. 20(4), 483–492 (2016)

    Google Scholar 

  22. Soukour, A., Devendeville, L., Lucet, C., Moukrim, A.: A Memetic algorithm for staff scheduling problem in airport security service. Expert Syst. Appl. 40(18), 7504–7512 (2013)

    Article  Google Scholar 

  23. Tilahun, S.L., Ngnotchouye, J.M.T.: Firefly algorithm for discrete optimization problems: a survey. J. Civ. Eng. 21(2), 535–545 (2017)

    Google Scholar 

  24. Toimil, D., Gómes, A.: Review of metaheuristics applied to heat exchanger network design. Int. Trans. Oper. Res. 24(1–2), 7–26 (2017)

    Article  MathSciNet  Google Scholar 

  25. Yang, G., Tang, W., Zhao, R.: An uncertain workforce planning problem with job satisfaction. Int. J. Mach. Learn. Cybern. 8(5), 1681–1693 (2016). https://doi.org/10.1007/s13042-016-0539-6. http://rd.springer.com/article/10.1007/s13042-016-0539-6

  26. Zhou, C., Tang, W., Zhao, R.: An uncertain search model for recruitment problem with enterprise performance. J. Intell. Manuf. 28(3), 695–704 (2014). https://doi.org/10.1007/s10845-014-0997-1

    Article  Google Scholar 

Download references

Acknowledgment

This work is partially supported by the Projects: KP-06-N22/1 “Theoretical Research and Applications of InterCriteria Analysis” and by the Bulgarian Scientific Fund by the grant DN 12/5.

Author information

Authors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

  2. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Olympia Roeva

Authors
  1. Stefka Fidanova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olympia Roeva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefka Fidanova .

Editor information

Editors and Affiliations

  1. IICT-BAS, Sofia, Bulgaria

    Ivan Lirkov

  2. IICT-BAS, Sofia, Bulgaria

    Svetozar Margenov

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fidanova, S., Roeva, O. (2022). Influence of the ACO Evaporation Parameter for Unstructured Workforce Planning Problem. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2021. Lecture Notes in Computer Science, vol 13127. Springer, Cham. https://doi.org/10.1007/978-3-030-97549-4_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-97549-4_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97548-7

  • Online ISBN: 978-3-030-97549-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics