New Local Search Procedure for Workforce Planning Problem

Abstract

Optimization of workforce planning is important for any production area. This leads to an improvement in production process. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is to select and assign employers to the jobs to be performed. The constraints are very strong, coming both from the specifics of the production process and from the legislation. Sometimes it is difficult to find feasible solutions. The complexity of the problem is very high and the needed number of calculations is exponential, therefore only specially developed algorithms can be applied. The objective is to minimize the assignment cost, while respecting all requirements. We propose a new hybrid metaheuristic algorithm to solve the workforce-planning problem, which is a combination between Ant Colony Optimization (ACO) and suitable local search procedure.

Ant Colony Optimization for Workforce Planning with Hybridization

“…In our previous work [6] is shown that our ACO algorithm without hybridization outperforms Genetic algorithm and Scatter search from [1]. The stopping criteria is achieving the best found solution for the same test instance from [7], [8]. We apply same parameter settings for all variants of hybridization of ACO algorithm and they are fixed after several experiments.…”

“…The procedure is applied once, regardless of whether the new solution is feasible or not [7]; • The workers to be removed are randomly selected. The procedure is repeated until a valid solution is constructed [8]; • The most expensive workers are removed. The procedure is applied once, regardless of whether the new solution is feasible or not.…”