Title: A stochastic programming approach for sawmill production planning

Authors: Masoumeh Kazemi Zanjani; Daoud Ait-Kadi; Mustapha Nourelfath

Addresses: Department of Mechanical and Industrial Engineering, Concordia University, 1515 St. Catherine West, EV4.243, Montreal, QC, H3G 1M8, Canada ' Department of Mechanical Engineering, Pavillon Adrien-Pouliot (Office 1314E), 1065, avenue de la Médecine, Université Laval, Québec (QC), G1V 0A6, Canada ' Department of Mechanical Engineering, Pavillon Adrien-Pouliot (Office 3344), 1065, avenue de la Médecine, Université Laval, Québec (QC), G1V 0A6, Canada

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.

Keywords: production planning; random yield; sawmills; stochastic linear programming; sample average approximation; discrete probability; stochastic modelling.

DOI: 10.1504/IJMOR.2013.050604

International Journal of Mathematics in Operational Research, 2013 Vol.5 No.1, pp.1 - 18

Published online: 31 Mar 2014 *

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