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.
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References
Alba, E., Luque, G., Luna, F.: Parallel metaheuristics for workforce planning. J. Math. Model. Algorithms 6(3), 509–528 (2007)
Angelova, M., Roeva, O., Pencheva, T.: InterCriteria analysis of crossover and mutation rates relations in simple genetic algorithm. In: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, vol. 5, pp. 419–424 (2015)
Atanassov, K.: Index Matrices: Towards an Augmented Matrix Calculus. Springer, Switzerland (2014)
Atanassov, K.: Intuitionistic fuzzy sets. VII ITKR session, Sofia, 20–23 June 1983. Int. J. Bioautom. 20(S1), S1–S6 (2016)
Atanassov, K.: Generalized index matrices. Comptes rendus de l’Academie bulgare des Sciences 40(11), 15–18 (1987)
Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, Berlin (2012)
Atanassov, K.: On index matrices, Part 1: standard cases. Adv. Stud. Contemp. Math. 20(2), 291–302 (2010)
Atanassov, K.: On index matrices, Part 2: intuitionistic fuzzy case. Proc. Jangjeon Math. Soc. 13(2), 121–126 (2010)
Atanassov, K.: On index matrices. Part 5: 3-dimensional index matrices. Adv. Stud. Contemp. Math. 24(4), 423–432 (2014)
Atanassov, K.: Review and new results on intuitionistic fuzzy sets, mathematical foundations of artificial intelligence seminar, Sofia, 1988, Preprint IM-MFAIS-1-88. Int. J. Bioautom. 20(S1), S7–S16 (2016)
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 on Intuitionistic Fuzzy Sets and Generalized Nets 11, 1–8 (2014)
Atanassov, K., Szmidt, E., Kacprzyk, J.: On intuitionistic fuzzy pairs. Notes Intuitionistic Fuzzy Sets 19(3), 1–13 (2013)
Atanassov, K., Atanassova, V., Gluhchev, G.: InterCriteria analysis: ideas and problems. Notes on Intuitionistic Fuzzy Sets 21(1), 81–88 (2015)
Atanassova, V.: Interpretation in the intuitionistic fuzzy triangle of the results, obtained by the InterCriteria analysis. In: Proceedings of the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), pp. 1369–1374 (2015)
Atanassova, V., Mavrov, D., Doukovska, L., Atanassov, K.: Discussion on the threshold values in the InterCriteria decision making approach. Notes on Intuitionistic Fuzzy Sets 20(2), 94–99 (2014)
Atanassova, V., Doukovska, L., Atanassov, K., Mavrov, D.: Intercriteria decision making approach to EU member states competitiveness analysis. In: Proceedings of the International Symposium on Business Modeling and Software Design - BMSD’14, pp. 289–294 (2014)
Atanassova, V., Doukovska, L., Karastoyanov, D., Capkovic, F.: InterCriteria decision making approach to EU member states competitiveness analysis: trend analysis. In: Intelligent Systems’2014, Advances in Intelligent Systems and Computing, vol. 322, pp. 107–115 (2014)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Campbell, G.: A two-stage stochastic program for scheduling and allocating cross-trained workers. J. Oper. Res. Soc. 62(6), 1038–1047 (2011)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Easton, F.: Service completion estimates for cross-trained workforce schedules under uncertain attendance and demand. Prod. Oper. Manage. 23(4), 660–675 (2014)
Fidanova, S., Roeva, O., Paprzycki, M.: InterCriteria analysis of ACO start strategies. In: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, vol. 8, pp. 547–550 (2016)
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)
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)
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)
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)
Hu, K., Zhang, X., Gen, M., Jo, J.: A new model for single machine scheduling with uncertain processing time. J. Intell. Manufact. 28(3), 717–725 (2015)
Ikonomov, N., Vassilev, P., Roeva, O.: ICrAData software for InterCriteria analysis. Int. J. Bioautom. 22(2) (2018) (in press)
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)
Li, R., Liu, G.: An uncertain goal programming model for machine scheduling problem. J. Intell. Manufact. 28(3), 689–694 (2014)
Ning, Y., Liu, J., Yan, L.: Uncertain aggregate production planning. Soft Comput. 17(4), 617–624 (2013)
Othman, M., Bhuiyan, N., Gouw, G.: Integrating workers’ differences into workforce planning. Comput. Ind. Eng. 63(4), 1096–1106 (2012)
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)
Roeva, O., Vassilev, P., Angelova, M., Su, J., Pencheva, T.: Comparison of different algorithms for InterCriteria relations calculation. In: 2016 IEEE 8th International Conference on Intelligent Systems, pp. 567–572 (2016)
Roeva, O., Fidanova, S., Paprzycki, M.: InterCriteria analysis of ACO and GA hybrid algorithms. Stud. Comput. Intell. 610, 107–126 (2016)
Roeva, O., Fidanova, S., Vassilev, P., Gepner, P.: InterCriteria analysis of a model parameters identification using genetic algorithm. Proceedings of the Federated Conference on Computer Science and Information Systems 5, 501–506 (2015)
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)
Todinova, S., Mavrov, D., Krumova, S., Marinov, P., Atanassova, V., Atanassov, K., Taneva, S.G.: Blood plasma thermograms dataset analysis by means of InterCriteria and correlation analyses for the case of colorectal cancer. Int. J. Bioautom. 20(1), 115–124 (2016)
Yang, G., Tang, W., Zhao, R.: An uncertain workforce planning problem with job satisfaction. Int. J. Mach. Learn. Cybern. (2016). https://doi.org/10.1007/s13042-016-0539-6
Zaharieva, B., Doukovska, L., Ribagin, S., Radeva, I.: InterCriteria decision making approach for Behterev’s disease analysis. Int. J. Bioautom. 22(2) (2018) (in press)
Zhou, C., Tang, W., Zhao, R.: An uncertain search model for recruitment problem with enterprise performance. J. Intell. Manufact. 28(3), 295–704 (2014)
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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Roeva, O., Fidanova, S., Luque, G., Paprzycki, M. (2019). Intercriteria Analysis of ACO Performance for Workforce Planning Problem. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-99648-6_4
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