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.
Highlights
T HE workforce planning is an important industrial decision making problem
The structured problems are enumerated from S01 to S10 and unstructured problems are enumerated from U 01 to U 10
In this article we propose Ant Colony Optimization (ACO) algorithm for solving workforce planning problem
Summary
T HE workforce planning is an important industrial decision making problem It is a hard optimization problem, which includes multiple level of complexity. This problem contains two decision sets: selection and assignment. In the work [9] workforce planning models that contain non-linear models of human learning are reformulated as mixed integer programs. For the more complex non-linear workforce planning problems, the convex methods are not applicable. On this case is applied some heuristic method including genetic algorithm [1], [11], memetic algorithm [16], scatter search [1].
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