Production planning with uncertainty in the quality of raw materials: a case in sawmills

Abstract

Motivated by sawmill production planning, this paper investigates multi-period, multi-product (MPMP) production planning in a manufacturing environment with non-homogeneous raw materials, and consequently random process yields. A two-stage stochastic program with recourse is proposed to address the problem. The random yields are modelled as scenarios with stationary probability distributions during the planning horizon. The solution methodology is based on the sample average approximation (SAA) scheme. The stochastic sawmill production planning model is validated through the Monte Carlo simulation. The computational results for a real medium capacity sawmill highlight the significance of using the stochastic model as a viable tool for production planning instead of the mean-value deterministic model, which is a traditional production planning tool in many sawmills.

Keywords:

• planning
• production
• stochastic programming
• Monte Carlo simulation
• sawmill

Acknowledgements

The authors would like to acknowledge the financial support provided by the Forest E-business Research Consortium (FOR@C) of Université Laval, and would like to thank specially, Dr. Jonathan Gaudreault, Philippe Marier, and Sébastien Lemieux, for their technical support.