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Intercriteria Analysis of ACO Performance for Workforce Planning Problem

  • Olympia Roeva
  • Stefka FidanovaEmail author
  • Gabriel Luque
  • Marcin Paprzycki
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 795)

Abstract

The workforce planning helps organizations to optimize the production process with the aim to minimize the assigning costs. The problem is to select a set of employees from a set of available workers and to assign this staff to the jobs to be performed. A workforce planning problem is very complex and requires special algorithms to be solved. The complexity of this problem does not allow the application of exact methods for instances of realistic size. Therefore, we will apply Ant Colony Optimization (ACO) algorithm, which is a stochastic method for solving combinatorial optimization problems. The ACO algorithm is tested on a set of 20 workforce planning problem instances. The obtained solutions are compared with other methods, as scatter search and genetic algorithm. The results show that ACO algorithm performs better than other the two algorithms. Further, we focus on the influence of the number of ants and the number of iterations on ACO algorithm performance. The tests are done on 16 different problem instances – ten structured and six unstructured problems. The results from ACO optimization procedures are discussed. In order to evaluate the influence of considered ACO parameters additional investigation is done. InterCriteria Analysis is performed on the ACO results for the regarded 16 problems. The results show that for the considered here workforce planning problem the best performance is achieved by the ACO algorithm with five ants in population.

Keywords

Workforce planning Ant colony optimization Metaheuristics InterCriteria analysis 

Notes

Acknowledgements

Work presented here is partially supported by the National Scientific Fund of Bulgaria under grants DFNI-DN 02/10 “New Instruments for Knowledge Discovery from Data, and their Modelling” and DFNI-DN 12/5 “Efficient Stochastic Methods and Algorithms for Large Scale Problems”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Olympia Roeva
    • 1
  • Stefka Fidanova
    • 2
    Email author
  • Gabriel Luque
    • 3
  • Marcin Paprzycki
    • 4
  1. 1.Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Institute of Information and Communication TechnologyBulgarian Academy of SciencesSofiaBulgaria
  3. 3.Department of Languages and Computer ScienceUniversity of MlagaMlagaSpain
  4. 4.System Research InstitutePolish Academy of Sciences, Warsaw and Management AcademyWarsawPoland

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