Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F63

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 415–419 (2017)

Full text

Abstract. The workforce planning helps organizations to optimize the production process with aim to minimize the assigning costs. A workforce planning problem is very complex and needs special algorithms to be solved. The problem is to select set of employers from a set of available workers and to assign this staff to the jobs to be performed. Each job requires a time to be completed. For efficiency, a worker must performs a minimum number of hours of any assigned job. There is a maximum number of jobs that can be assigned and a maximum number of workers that can be assigned. There is a set of jobs that shows the jobs on which the worker is qualified. The objective is to minimize the costs associated to the human resources needed to fulfill the work requirements. On this work we propose a variant of Ant Colony Optimization (ACO) algorithm to solve workforce optimization problem. The algorithm is tested on a set of 20 test problems. Achieved solutions are compared with other methods, as scatter search and genetic algorithm. Obtained results show that ACO algorithm performs better than other two algorithms.

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.
2. Bonabeau E., Dorigo M. and Theraulaz G., Swarm Intelligence: From Natural to Artificial Systems, New York,Oxford University Press, 1999.
3. Campbell G., A two-stage stochastic program for scheduling and allocating cross-trained workers, J. Operational Research Society 62(6), 2011, 10381047.
4. Dorigo M, Stutzle T., Ant Colony Optimization, MIT Press, 2004.
5. Easton F., Service completion estimates for cross-trained workforce schedules under uncertain attendance and demand, Production and Operational Management 23(4), 2014, 660675.
6. 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.
7. 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, 636641.
8. Grzybowska K., Kovcs, G., Sustainable Supply Chain - Supporting Tools, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Vol. 2, 2014, 13211329.
9. 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, 942950.
10. 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.
11. 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, 117.
12. Li R., Liu G., An uncertain goal programming model for machine scheduling problem. J. Inteligent Manufacturing, Vol. 28(3), Springer, 2014, 689-694.
13. Ning Y., Liu J., Yan L., Uncertain aggregate production planning, Soft Computing, Vol. 17(4), Springer, 2013, 617624.
14. Othman M., Bhuiyan N., Gouw G., Integrating workers’ differences into workforce planning, Computers and Industrial Engineering, Vol. 63(4), 2012, 10961106.
15. 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.
16. 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, 75047512.
17. Yang G., Tang W., Zhao R., An uncertain workforce planning problem with job satisfaction, Int. J. Machine Learning and Cybernetics, Springer, 2016. http://dx.doi.org/10.1007/s13042-016-0539-6 http://rd.springer.com/article/10.1007/s13042-016-0539-6
18. 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