A modified particle swarm optimization algorithm in a rolling horizon framework for the aggregate production planning problem: pharmaceutical industry case

This paper proposes a modified particle swarm optimization algorithm in a rolling horizon framework to solve the aggregate production planning under uncertainty problem. This latter is a medium-term capacity planning and is well known as NP-hard. The problem consists of determining production, inventory and workforce levels to satisfy demands during a planning horizon. As this demand constitutes a major source of uncertainties, a rolling horizon heuristic is implemented to ensure the generation of a near optimal planning after the arrival of new information. Real data from a pharmaceutical company is used to test and validate the proposed combined approach. Computational results reveal that this latter is effective in solving the problem in short computational time and allows to obtain reactive plans with significant total cost decrease.