Maximizing Dual Function by Genetic Algorithm – A New Approach for Optimal Manpower Planning

Abstract

We propose a new approach to tackle the manpower planning problem with multiple types of jobs in a long planning horizon, where dynamic demands for manpower must be fulfilled by allocating enough number of employees with qualified skills. We first apply Lagrangean relaxation to decompose the problem into a number of subproblems, each corresponding to one skill type, and then develop a coordination scheme based on a Genetic algorithm, which updates the Lagrangean multipliers to maximize the dual objective function. We report computational results, which demonstrate the effectiveness of our approach.