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Supply chain design under uncertainty using sample average approximation and dual decomposition

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  • Schütz, Peter
  • Tomasgard, Asgeir
  • Ahmed, Shabbir
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    Abstract

    We present a supply chain design problem modeled as a sequence of splitting and combining processes. We formulate the problem as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to minimize the sum of investment costs and expected costs of operating the supply chain. In particular the model emphasizes the importance of operational flexibility when making strategic decisions. For that reason short-term uncertainty is considered as well as long-term uncertainty. The real-world case used to illustrate the model is from the Norwegian meat industry. We solve the problem by sample average approximation in combination with dual decomposition. Computational results are presented for different sample sizes and different levels of data aggregation in the second stage.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 199 (2009)
    Issue (Month): 2 (December)
    Pages: 409-419

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    Handle: RePEc:eee:ejores:v:199:y:2009:i:2:p:409-419

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Supply chain design Stochastic programming Sample average approximation Dual decomposition;

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    Cited by:
    1. Aydin, Nezir & Murat, Alper, 2013. "A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem," International Journal of Production Economics, Elsevier, vol. 145(1), pages 173-183.
    2. Farahani, Reza Zanjirani & Rezapour, Shabnam & Drezner, Tammy & Fallah, Samira, 2014. "Competitive supply chain network design: An overview of classifications, models, solution techniques and applications," Omega, Elsevier, vol. 45(C), pages 92-118.
    3. Pimentel, Bruno S. & Mateus, Geraldo R. & Almeida, Franklin A., 2013. "Stochastic capacity planning and dynamic network design," International Journal of Production Economics, Elsevier, vol. 145(1), pages 139-149.
    4. Tavella, Elena & Hjortso, Carsten Nico, 2012. "Enhancing the Design and Management of a Local Organic Food Supply Chain with Soft Systems Methodology," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association (IAMA), vol. 15(2).
    5. Baghalian, Atefeh & Rezapour, Shabnam & Farahani, Reza Zanjirani, 2013. "Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case," European Journal of Operational Research, Elsevier, vol. 227(1), pages 199-215.
    6. Sönmez, Erkut & Kekre, Sunder & Scheller-Wolf, Alan & Secomandi, Nicola, 2013. "Strategic analysis of technology and capacity investments in the liquefied natural gas industry," European Journal of Operational Research, Elsevier, vol. 226(1), pages 100-114.
    7. Kiya, Farhad & Davoudpour, Hamid, 2012. "Stochastic programming approach to re-designing a warehouse network under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 919-936.
    8. Lin, Cheng-Chang & Wu, Yi-Chen, 2013. "Optimal pricing for build-to-order supply chain design under price-dependent stochastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 31-49.
    9. Walther, Grit & Schatka, Anne & Spengler, Thomas S., 2012. "Design of regional production networks for second generation synthetic bio-fuel – A case study in Northern Germany," European Journal of Operational Research, Elsevier, vol. 218(1), pages 280-292.
    10. Michal Kaut & Kjetil Midthun & Adrian Werner & Asgeir Tomasgard & Lars Hellemo & Marte Fodstad, 2014. "Multi-horizon stochastic programming," Computational Management Science, Springer, vol. 11(1), pages 179-193, January.
    11. Meng, Qiang & Wang, Tingsong & Wang, Shuaian, 2012. "Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand," European Journal of Operational Research, Elsevier, vol. 223(1), pages 96-105.
    12. Egging, Ruud, 2013. "Benders Decomposition for multi-stage stochastic mixed complementarity problems – Applied to a global natural gas market model," European Journal of Operational Research, Elsevier, vol. 226(2), pages 341-353.
    13. Klibi, Walid & Martel, Alain, 2012. "Modeling approaches for the design of resilient supply networks under disruptions," International Journal of Production Economics, Elsevier, vol. 135(2), pages 882-898.
    14. Contreras, Ivan & Cordeau, Jean-François & Laporte, Gilbert, 2011. "Stochastic uncapacitated hub location," European Journal of Operational Research, Elsevier, vol. 212(3), pages 518-528, August.
    15. Schütz, Peter & Tomasgard, Asgeir, 2011. "The impact of flexibility on operational supply chain planning," International Journal of Production Economics, Elsevier, vol. 134(2), pages 300-311, December.
    16. Longinidis, Pantelis & Georgiadis, Michael C., 2014. "Integration of sale and leaseback in the optimal design of supply chain networks," Omega, Elsevier, vol. 47(C), pages 73-89.
    17. Lin, Cheng-Chang & Wang, Tsai-Hsin, 2011. "Build-to-order supply chain network design under supply and demand uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1162-1176, September.

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