Skip to main content
SpringerLink
Log in

InterCriteria Analysis of Different Hybrid Ant Colony Optimization Algorithms for Workforce Planning

  • Chapter
  • First Online:
Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 838))

Abstract

Every organization and factory optimize their production process with a help of workforce planing. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is very complex and needs exponential number of calculations, therefore special algorithms are developed to be solved. The problem is to select employers and to assign them to the jobs to be performed. This problem has very strong constraints and it is difficult to find feasible solutions. The objective is to fulfil the requirements and to minimize the assignment cost. We propose a hybrid Ant Colony Optimization (ACO) algorithm to solve the workforce problem, which is a combination between ACO and an appropriate local search procedure. In this investigation InterCriteria Analysis (ICrA) is applied over numerical results obtained from ACO algorithms with the suggested different variants of local search procedures. Based on ICrA the ACO hybrid algorithms performance is examined and compared.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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 

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

    Article  Google Scholar 

  3. 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 

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

    Article  Google Scholar 

  5. Hu, K., Zhang, X., Gen, M., Jo, J.: A new model for single machine scheduling with uncertain processing time. J. Intell. Manuf. (Springer) 28(3), 717–725 (2015)

    Article  Google Scholar 

  6. Li, R., Liu, G.: An uncertain goal programming model for machine scheduling problem. J. Intell Manuf. (Springer) 28(3), 689–694 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Zhou, C., Tang, W., Zhao, R.: An uncertain search model for recruitment problem with enterprise performance. J. Intell. Manuf. (Springer) 28(3), 295–704 (2014). https://doi.org/10.1007/s10845-014-0997-1

    Article  Google Scholar 

  10. 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 

  11. Albayrak, G., Özdemir, \(\dot{\text{l}}\).: A state of art review on metaheuristic methods in time-cost trade-off problems. Int. J. Struct. Civil Eng. Res. 6(1), 30–34 (2017)

    Google Scholar 

  12. Mucherino, A., Fidanova, S., Ganzha, M.: Introducing the environment in ant colony optimization, recent advances in computational optimization, studies in computational. Intelligence 655, 147–158 (2016)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  16. Alba, E., Luque, G., Luna, F.: Parallel metaheuristics for workforce planning. J. Math. Model. Algorithms (Springer) 6(3), 509–528 (2007)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  18. 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 

  19. 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 

  20. Grzybowska, K., Kovcs, 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 

  21. Fidanova, S., Luquq, 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 

  22. Atanassov, K., Mavrov, D., Atanassova, V.: Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues in IFSs and GNs 11, 1–8 (2014)

    Google Scholar 

  23. Traneva, V., Atanassova, V., Tranev, S.: Index matrices as a decision-making tool for job appointment. In: G. Nikolov et al. (eds.) NMA 2018, LNCS , vol. 11189, pp. 1–9. Springer Nature Switzerland AG (2019)

    Google Scholar 

  24. Traneva, V., Tranev, S., Atanassova, V.: An intuitionistic fuzzy approach to the hungarian algorithm. In: Nikolov G. et al. (eds.) NMA 2018, LNCS, vol. 11189, pp. 1–9. Springer Nature Switzerland AG (2019)

    Google Scholar 

  25. Atanassov, K.T., Vassilev, P.: On the intuitionistic fuzzy sets of n-th type. In: Gaweda A., Kacprzyk J., Rutkowski L., Yen G. (eds.) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol. 738, pp. 265–274. Springer, Cham (2018)

    Google Scholar 

  26. Vassilev, P., Ribagin, S.: A note on intuitionistic fuzzy modal-like operators generated by power mean. In: Kacprzyk J., Szmidt E., Zadrony S., Atanassov K., Krawczak M. (eds.) Advances in Fuzzy Logic and Technology 2017. EUSFLAT 2017, IWIFSGN 2017. Advances in Intelligent Systems and Computing, vol. 643, pp. 470–475. Springer, Cham (2018)

    Google Scholar 

  27. Marinov, E., Vassilev, P., Atanassov, K.: On separability of intuitionistic fuzzy sets. In: Novel Developments in Uncertainty Representation and Processing, Advances in Intelligent Systems and Computing, vol. 401, pp. 111–123. Springer, Cham (2106)

    Google Scholar 

  28. Vassilev, P.: A note on new distances between intuitionistic fuzzy sets. Notes Intuit. Fuzzy Sets 21(5), 11–15 (2015)

    Google Scholar 

  29. Atanassova, V., Mavrov, D., Doukovska, L., Atanassov, K.: Discussion on the threshold values in the intercriteria decision making approach. Notes on Intuit. Fuzzy Sets 20(2), 94–99 (2014)

    Google Scholar 

  30. Atanassova, V., Doukovska, L., Atanassov, K., Mavrov, D.: Intercriteria decision making approach to EU member states competitiveness analysis. In: Shishkov, B. (ed.) Proceedings of the International Symposium on Business Modeling and Software Design—BMSD’14, pp. 289–294 (2014)

    Google Scholar 

  31. Atanassova, V. Doukovska, L., Karastoyanov, D., Capkovic, F.: InterCriteria decision making approach to EU member states competitiveness analysis: trend analysis. In: Angelov P. et al. (eds.) Intelligent Systems’2014, Advances in Intelligent Systems and Computing, vol. 322, pp. 107–115 (2014)

    Google Scholar 

  32. Roeva, O., Fidanova, S., Vassilev, P., Gepner, P.: InterCriteria analysis of a model parameters identification using genetic algorithm. In: Proceedings of the Federated Conference on Computer Science and Information Systems, vol. 5, pp. 501–506 (2015)

    Google Scholar 

  33. Todinova, S., Mavrov, D., Krumova, S., Marinov, P., Atanassova, V., Atanassov, K., Taneva, S.G.: Blood plasma thermograms dataset analysisby means of intercriteria and correlation analyses for the case of colorectal cancer. Int. J. Bioautomation 20(1), 115–124 (2016)

    Google Scholar 

  34. Vassilev, P., Todorova, L., Andonov, V.: An auxiliary technique for intercriteria analysis via a three dimensional index matrix. Notes on Intuit. Fuzzy Sets 21(2), 71–76 (2015)

    Google Scholar 

  35. Angelova, M., Roeva, O., Pencheva, T.: InterCriteria analysis of crossover and mutation rates relations in simple genetic algorithm. In: Proceedings of the Federated Conference on Computer Science and Information Systems, vol. 5, pp. 419–424 (2015)

    Google Scholar 

  36. Roeva, O., Fidanova, S., Paprzycki, M.: InterCriteria analysis of ACO and GA hybrid algorithms. Stud. Comput. Intell. 610, 107–126 (2016)

    MathSciNet  Google Scholar 

  37. 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, pp. 636–641 (2004)

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  40. Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, Berlin (2012)

    Book  Google Scholar 

  41. Atanassov, K.: Review and new results on intuitionistic fuzzy sets, mathematical foundations of artificial intelligence seminar, sofia, 1988. Preprint IM-MFAIS-1-88, Reprinted: Int. J. Bioautomation 20(S1), S7–S16 (2016)

    Google Scholar 

  42. Atanassov, K.: Intuitionistic Fuzzy Sets, VII ITKR Session, Sofia, 20–23 June 1983. Reprinted: Int. J. Bioautomation 20(S1), S1–S6 (2016)

    Article  MathSciNet  Google Scholar 

  43. Atanassov, K.: On index matrices, Part 1: standard cases. Adv. Stud. Contemp. Math. 20(2), 291–302 (2010)

    MathSciNet  MATH  Google Scholar 

  44. Ikonomov, N., Vassilev, P., Roeva, O.: ICrAData—software for intercriteria analysis. Int. J. Bioautomation 22(1), 1–10 (2018)

    Article  Google Scholar 

  45. Atanassov, K., Atanassova, V., Gluhchev, G.: InterCriteria analysis: ideas and problems. Notes Intuit. Fuzzy Sets 21(1), 81–88 (2015)

    MATH  Google Scholar 

Download references

Acknowledgements

Work presented here is partially supported by the National Science Fund of Bulgaria under grants DFNI-DN02/10 “New Instruments for Knowledge Discovery from Data and by the Polish-Bulgarian collaborative grant “Practical aspects for scientific computing”.

Author information

Authors and Affiliations

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

    Stefka Fidanova

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

    Olympia Roeva

  3. DLCS, University of Mlaga, 29071, Mlaga, Spain

    Gabriel Luque

  4. System Research Institute Polish Academy of Sciences, Warsaw and Management Academy, Warsaw, Poland

    Marcin Paprzycki

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

  3. Gabriel Luque
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcin Paprzycki
    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. Parallel Algorithms, Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Fidanova, S., Roeva, O., Luque, G., Paprzycki, M. (2020). InterCriteria Analysis of Different Hybrid Ant Colony Optimization Algorithms for Workforce Planning. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-030-22723-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation