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Furniture supply chain tactical planning optimization using a time decomposition approach

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  • Ouhimmou, M.
  • D'Amours, S.
  • Beauregard, R.
  • Ait-Kadi, D.
  • Chauhan, S. Singh

Abstract

We study the supply chain tactical planning problem of an integrated furniture company located in the Province of Québec, Canada. The paper presents a mathematical model for tactical planning of a subset of the supply chain. The decisions concern procurement, inventory, outsourcing and demand allocation policies. The goal is to define manufacturing and logistics policies that will allow the furniture company to have a competitive level of service at minimum cost. We consider planning horizon of 1 year and the time periods are based on weeks. We assume that customer's demand is known and dynamic over the planning horizon. Supply chain planning is formulated as a large mixed integer programming model. We developed a heuristic using a time decomposition approach in order to obtain good solutions within reasonable time limit for large size problems. Computational results of the heuristic are reported. We also present the quantitative and qualitative results of the application of the mathematical model to a real industrial case.

Suggested Citation

  • Ouhimmou, M. & D'Amours, S. & Beauregard, R. & Ait-Kadi, D. & Chauhan, S. Singh, 2008. "Furniture supply chain tactical planning optimization using a time decomposition approach," European Journal of Operational Research, Elsevier, vol. 189(3), pages 952-970, September.
    • Handle: RePEc:eee:ejores:v:189:y:2008:i:3:p:952-970
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    3. Masoud Esmaeilikia & Behnam Fahimnia & Joeseph Sarkis & Kannan Govindan & Arun Kumar & John Mo, 2016. "Tactical supply chain planning models with inherent flexibility: definition and review," Annals of Operations Research, Springer, vol. 244(2), pages 407-427, September.
    4. Melega, Gislaine Mara & de Araujo, Silvio Alexandre & Jans, Raf, 2018. "Classification and literature review of integrated lot-sizing and cutting stock problems," European Journal of Operational Research, Elsevier, vol. 271(1), pages 1-19.
    5. Guajardo, Mario & Kylinger, Martin & Rönnqvist, Mikael, 2013. "Speciality oils supply chain optimization: From a decoupled to an integrated planning approach," European Journal of Operational Research, Elsevier, vol. 229(2), pages 540-551.
    6. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2020. "Tactical sales and operations planning: A holistic framework and a literature review of decision-making models," International Journal of Production Economics, Elsevier, vol. 228(C).
    7. Burcu B. Keskin & Halit Üster, 2012. "Production/distribution system design with inventory considerations," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(2), pages 172-195, March.
    8. Mustapha Ouhimmou & Sophie D'Amours & Robert Beauregard & Daoud Ait-Kadi & Satyaveer Singh Chauhan, 2009. "Optimization Helps Shermag Gain Competitive Edge," Interfaces, INFORMS, vol. 39(4), pages 329-345, August.
    9. Masoud Esmaeilikia & Behnam Fahimnia & Joeseph Sarkis & Kannan Govindan & Arun Kumar & John Mo, 2016. "A tactical supply chain planning model with multiple flexibility options: an empirical evaluation," Annals of Operations Research, Springer, vol. 244(2), pages 429-454, September.
    10. Scott, James A. & Ho, William & Dey, Prasanta K., 2012. "A review of multi-criteria decision-making methods for bioenergy systems," Energy, Elsevier, vol. 42(1), pages 146-156.
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    12. Gunasekaran, Angappa & Ngai, Eric W.T., 2012. "The future of operations management: An outlook and analysis," International Journal of Production Economics, Elsevier, vol. 135(2), pages 687-701.

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