Sawmill scheduling: an application-oriented model

Csaba Kebelei; Mate Hegyhati

doi:10.14513/actatechjaur.00743

Authors

Csaba Kebelei University of Sopron, Bajcsy-Zs. u. 4, H9400 Sopron, Hungary https://orcid.org/0009-0007-1132-638X
Mate Hegyhati University of Sopron, Bajcsy-Zs. u. 4, H9400 Sopron, Hungary https://orcid.org/0000-0003-3689-9866

DOI:

https://doi.org/10.14513/actatechjaur.00743

Keywords:

sawmill, scheduling, optimization, timber industry

Abstract

Sawmills play a key role in the primary sector of the wood industry producing lumber from tree logs through a multi-stage process including inspections, classification, sawing, drying, etc. The sawing step can become a bottleneck due to the high investment costs the necessary equipment present. Thus, their schedule can be of significant economic importance, resulting in several studies over the years. While most of the approaches in the literature consider a simple model of production and focus on the stochastic nature of real-life problems, present work details a more in-depth model to better tackle practical considerations of less automated and smaller sawmills. The proposed Mixed-Integer Linear Programming model addresses volatile labour availability and differences between the two most dominant sawing technologies. The efficiency of the model is tested on randomly generated instances. The proposed approach can provide the optimal solution within reasonable time for short-term instances.

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References

A. Santos, A. Carvalho, A. P. Barbosa-Póvoa, A. Marques, and P. Amorim, “Assessment and optimization of sustainable forest wood supply chains – A systematic literature review,” Forest Policy and Economics, vol. 105. 2019. https://doi.org/10.1016/j.forpol.2019.05.026 DOI: https://doi.org/10.1016/j.forpol.2019.05.026

M. A. Hoogstra-Klein and K. Meijboom, “A qualitative exploration of the wood product supply chain – investigating the possibilities and desirability of an increased demand orientation,” For Policy Econ, vol. 133, 2021. https://doi.org/10.1016/j.forpol.2021.102606 DOI: https://doi.org/10.1016/j.forpol.2021.102606

N. Kazemi, N. M. Modak, and K. Govindan, “A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis,” International Journal of Production Research, vol. 57, no. 15–16. 2019. https://doi.org/10.1080/00207543.2018.1471244 DOI: https://doi.org/10.1080/00207543.2018.1471244

S. Koch, S. König, and G. Wäscher, “Integer linear programming for a cutting problem in the wood-processing industry: A case study,” International Transactions in Operational Research, vol. 16, no. 6, 2009. https://doi.org/10.1111/j.1475-3995.2009.00704.x DOI: https://doi.org/10.1111/j.1475-3995.2009.00704.x

S. Maturana, E. Pizani, and J. Vera, “Scheduling production for a sawmill: A comparison of a mathematical model versus a heuristic,” Comput Ind Eng, vol. 59, no. 4, 2010. https://doi.org/10.1016/j.cie.2010.07.016 DOI: https://doi.org/10.1016/j.cie.2010.07.016

N. Vanzetti, D. Broz, G. Corsano, and J. M. Montagna, “An optimization approach for multiperiod production planning in a sawmill,” For Policy Econ, vol. 97, 2018. https://doi.org/10.1016/j.forpol.2018.09.001 DOI: https://doi.org/10.1016/j.forpol.2018.09.001

D. Broz, N. Vanzetti, G. Corsano, and J. M. Montagna, “Goal programming application for the decision support in the daily production planning of sawmills,” For Policy Econ, vol. 102, 2019. https://doi.org/10.1016/j.forpol.2019.02.004 DOI: https://doi.org/10.1016/j.forpol.2019.02.004

P. P. Alvarez and J. R. Vera, “Application of Robust Optimization to the Sawmill Planning Problem,” Ann Oper Res, vol. 219, no. 1, 2014. https://doi.org/10.1007/s10479-011-1002-4 DOI: https://doi.org/10.1007/s10479-011-1002-4

M. Varas, S. Maturana, R. Pascual, I. Vargas, and J. Vera, “Scheduling production for a sawmill: A robust optimization approach,” Int J Prod Econ, vol. 150, 2014. https://doi.org/10.1016/j.ijpe.2013.11.028 DOI: https://doi.org/10.1016/j.ijpe.2013.11.028

M. Kazemi Zanjani, D. Ait-Kadi, and M. Nourelfath, “Robust production planning in a manufacturing environment with random yield: A case in sawmill production planning,” Eur J Oper Res, vol. 201, no. 3, 2010. https://doi.org/10.1016/j.ejor.2009.03.041 DOI: https://doi.org/10.1016/j.ejor.2009.03.041

M. Morin et al., “Machine learning-based models of sawmills for better wood allocation planning,” Int J Prod Econ, vol. 222, 2020. https://doi.org/10.1016/j.ijpe.2019.09.029 DOI: https://doi.org/10.1016/j.ijpe.2019.09.029

V. Nasir, J. Cool, and F. Sassani, “Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection,” International Journal of Advanced Manufacturing Technology, vol. 102, no. 9–12, 2019. https://doi.org/10.1007/s00170-019-03526-3 DOI: https://doi.org/10.1007/s00170-019-03526-3

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