A robust optimization model for stochastic aggregate production planning

Abstract

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.

Keywords:

• Aggregate production planning
• robustness
• stochastic programming

Acknowledgements

We thank the anonymous referees for their helpful comments. The work described in this paper was supported by the Strategic Research Grant from City University of Hong Kong (Project Number 7001516).

STEPHEN C. H. LEUNG is a Lecturer in the Department of Management Sciences at City University of Hong Kong. He received his PhD in Operational Research and Management Science from City University of Hong Kong. Before joining the teaching profession, Dr Leung was a consultant in Wilbur Smith Associate Ltd. He had been involved in a variety of projects about transport modelling and traffic impact assessment. His current research interests are logistics management, operations management and multiple-criteria decision-making.

YUE WU is a Lecturer in the School of Management at the University of Southampton. She received her PhD in Control Theory and Control Engineering from Northeastern University in China. Her research interests are production planning and scheduling, computer-integrated manufacturing systems, intelligent computing and supply-chain management.