A stochastic programming approach for sawmill production planning. (1st January 2013)

• Record Type: Journal Article
• Authors: Kazemi Zanjani, Masoumeh; Ait-Kadi, Daoud; Nourelfath, Mustapha
• Is Part Of: International journal of mathematics in operational research. Volume 5:Number 1(2013)
• Page Start: 1
• Page End: 18
• Publication Date: 2013-01-01
• Subjects: production planning -- random yield -- sawmill -- stochastic programming -- sample average approximation; Operations research -- Mathematical models -- Periodicals; Operations research -- Mathematics -- Periodicals; Decision making -- Mathematical models -- Periodicals; 658.4033
• Languages: English
• ISSNs: 1757-5850

Abstract

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.