A stochastic programming approach for sawmill production planning

This paper investigates a sawmill production planning problem where the non-homogeneous characteristics of logs result in random process yields. A two-stage stochastic Linear Programming (LP) approach is proposed to address this problem. The random yields are modelled as scenarios with discrete probability distributions. The solution methodology is based on the sample average approximation method. Confidence intervals are constructed for the optimality gap of several candidate solutions, based on Common Random Number (CRN) streams. A computational study including a prototype sawmill is presented to highlight the significance of using the stochastic model instead of the mean-value deterministic model, which is the traditional production planning tool in sawmills.

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