Maximizing Dual Function by Genetic Algorithm – A New Approach for Optimal Manpower Planning
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.
Unable to display preview. Download preview PDF.