Bi-criteria optimization of unrelated parallel manufacturing systems with limited human resource capacity

Managing the human resource capacity involved in the manufacturing tasks may represents a strategic leverage for improving the production economies of a firm. In this paper a Pareto front-based multi-objective optimization for scheduling a set of different parts within a manufacturing system with M unrelated parallel machines and with a limited workforce capacity has been addressed. The multi-objective performance of the production system at the varying of the human resource capacity has been investigated by a bi-criteria heuristic algorithm optimizing makespan and total earliness. First, on the basis of a small sized class of problems, three different and well-known multi-objective evolutionary algorithms have been compared. Then, once the most performing optimization tool has been selected, an extensive computational analysis has been carried out in order to put in evidence the way the approximate Pareto optimal solutions are characterized by the human resource capacity. Numerical results confirm as the human resources strongly affects the bi-criteria performance of the unrelated parallel machines scheduling problem.