Sawmill Production Planning Under Uncertainty: Modelling and Solution Approaches

Masoumeh Kazemi Zanjani, Mustapha Nourelfath and Daoud Ait-Kadi
Additional contact information
Masoumeh Kazemi Zanjani: Department of Mechanical and Industrial Engineering, Concordia University, 1515 St. Catherine St. West, EV4.243, Montreal(QC), H3H 1M8, Canada
Mustapha Nourelfath: Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Department of Mechanical Engineering, Université Laval, Québec, Canada
Daoud Ait-Kadi: Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Department of Mechanical Engineering, Université Laval, Québec, Canada

Chapter 13 in Stochastic Programming:Applications in Finance, Energy, Planning and Logistics, 2013, pp 347-395 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractWe investigate the operational level production planning problem in the sawing units of sawmills under the uncertainty in the quality of materials and demand. In order to widen the application of the production planning model in different types of sawmills, we also take into account setup constraints. We propose two-stage stochastic programming, robust optimization, and multistage stochastic programming models to formulate different aspects of this problem. As demand and yield own different uncertain natures, they are modeled separately and then integrated. Demand uncertainty is considered as a dynamic stochastic data process during the planning horizon which is modeled as a scenario tree. The uncertain yield is modeled as scenarios with a stationary probability distribution during the planning horizon. Yield scenarios are then integrated into each node of demand scenario tree, constituting a hybrid scenario tree. We also propose two solution strategies to find good solutions with an acceptable gap to the optimal solution, while taking into account setup constraints in the production planning model. The first strategy is based on the progressive hedging algorithm (PHA), while the second strategy is a successive approximation heuristic which solves the problem by considering only a subset of scenarios which is updated at each iteration. We conduct a case study with respect to a realistic scale prototype sawmill. Computational experiments highlights the effectiveness of the proposed tools for production planning under uncertainty in the sawing units of sawmills.

Keywords: Stochastic Programming; Optimization with Scenarios; Finance; Energy; Production and Logistics Applications (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.worldscientific.com/doi/pdf/10.1142/9789814407519_0013 (application/pdf)
https://www.worldscientific.com/doi/abs/10.1142/9789814407519_0013 (text/html)
Ebook Access is available upon purchase.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9789814407519_0013

Ordering information: This item can be ordered from

Access Statistics for this chapter

More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().