Abstract
The workforce planning is a difficult optimization problem. It is important real life problem which helps organizations to determine workforce which they need. A workforce planning problem is very complex and needs special algorithms to be solved using reasonable computational resources. The problem consists to select set of employers from a set of available workers and to assign this staff to the tasks to be performed. The objective is to minimize the costs associated to the human resources needed to fulfil the work requirements. A good workforce planing is important for an organization to accomplish its objectives. 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) method which is a stochastic method for solving combinatorial optimization problems. On this paper we focus on influence of the parameters on ACO algorithm performance.
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Acknowledgements
Work presented here is partially supported by the Bulgarian National Scientific Fund under the grants DFNI-DN 12/5 “Efficient Stochastic Methods and Algorithms for Large Scale Problems” and DFNI-DN 02/10 “New Instruments for Data Mining and their Modeling”.
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Fidanova, S., Roeva, O., Luque, G. (2019). Ant Colony Optimization Algorithm for Workforce Planning: Influence of the Algorithm Parameters. In: Georgiev, K., Todorov, M., Georgiev, I. (eds) Advanced Computing in Industrial Mathematics. BGSIAM 2017. Studies in Computational Intelligence, vol 793. Springer, Cham. https://doi.org/10.1007/978-3-319-97277-0_10
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DOI: https://doi.org/10.1007/978-3-319-97277-0_10
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