IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v116y2008i1p129-138.html
   My bibliography  Save this article

A multi-objective stochastic programming approach for supply chain design considering risk

Author

Listed:
  • Azaron, A.
  • Brown, K.N.
  • Tarim, S.A.
  • Modarres, M.

Abstract

In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all considered as the uncertain parameters. To develop a robust model, two additional objective functions are added into the traditional comprehensive supply chain design problem. So, our multi-objective model includes (i) the minimization of the sum of current investment costs and the expected future processing, transportation, shortage and capacity expansion costs, (ii) the minimization of the variance of the total cost and (iii) the minimization of the financial risk or the probability of not meeting a certain budget. The ideas of unreliable suppliers and capacity expansion, after the realization of uncertain parameters, are also incorporated into the model. Finally, we use the goal attainment technique to obtain the Pareto-optimal solutions that can be used for decision-making.

Suggested Citation

  • Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    • Handle: RePEc:eee:proeco:v:116:y:2008:i:1:p:129-138
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(08)00237-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Goh, Mark & Lim, Joseph Y.S. & Meng, Fanwen, 2007. "A stochastic model for risk management in global supply chain networks," European Journal of Operational Research, Elsevier, vol. 182(1), pages 164-173, October.
    3. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    4. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    5. Azaron, Amir & Katagiri, Hideki & Kato, Kosuke & Sakawa, Masatoshi, 2006. "Modelling complex assemblies as a queueing network for lead time control," European Journal of Operational Research, Elsevier, vol. 174(1), pages 150-168, October.
    6. Amir Azaron & Hideki Katagiri & Masatoshi Sakawa, 2007. "Time-cost trade-off via optimal control theory in Markov PERT networks," Annals of Operations Research, Springer, vol. 150(1), pages 47-64, March.
    7. Timpe, Christian H. & Kallrath, Josef, 2000. "Optimal planning in large multi-site production networks," European Journal of Operational Research, Elsevier, vol. 126(2), pages 422-435, October.
    8. Christian van Delft & Jean-Philippe Vial, 2004. "A practical implementation of stochastic programming: an application to the evaluation of option contracts in supply chains," Post-Print hal-00471409, HAL.
    9. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.
    2. 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.
    3. Vahid Nooraie, S. & Parast, Mahour Mellat, 2016. "Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 8-21.
    4. Zarrinpoor, Naeme & Fallahnezhad, Mohammad Saber & Pishvaee, Mir Saman, 2018. "The design of a reliable and robust hierarchical health service network using an accelerated Benders decomposition algorithm," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1013-1032.
    5. Govindan, Kannan & Fattahi, Mohammad, 2017. "Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 680-699.
    6. Ouhimmou, Mustapha & Nourelfath, Mustapha & Bouchard, Mathieu & Bricha, Naji, 2019. "Design of robust distribution network under demand uncertainty: A case study in the pulp and paper," International Journal of Production Economics, Elsevier, vol. 218(C), pages 96-105.
    7. Huang, Edward & Goetschalckx, Marc, 2014. "Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk," European Journal of Operational Research, Elsevier, vol. 237(2), pages 508-518.
    8. Huang, Edward & Liu, Irene & Lin, James T., 2018. "Robust model for the assignment of outgoing flights on airport baggage unloading areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 110-125.
    9. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    10. Li, Yuanzhe, 2019. "Modeling Bioenergy Supply Chains: Feedstocks Pretreatment, Integrated System Design Under Uncertainty," Institute of Transportation Studies, Working Paper Series qt1539g5sj, Institute of Transportation Studies, UC Davis.
    11. Julian Englberger & Frank Herrmann & Michael Manitz, 2016. "Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6192-6215, October.
    12. Sarmadi, Kamran & Amiri-Aref, Mehdi & Dong, Jing-Xin & Hicks, Christian, 2020. "Integrated strategic and operational planning of dry port container networks in a stochastic environment," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 132-164.
    13. Shishebori, Davood & Yousefi Babadi, Abolghasem, 2015. "Robust and reliable medical services network design under uncertain environment and system disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 268-288.
    14. Tsai, Jung-Fa, 2007. "An optimization approach for supply chain management models with quantity discount policy," European Journal of Operational Research, Elsevier, vol. 177(2), pages 982-994, March.
    15. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    16. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    17. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    18. Xu, Y. & Huang, G.H. & Qin, X.S. & Cao, M.F., 2009. "SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 53(6), pages 352-363.
    19. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    20. Aalaei, Amin & Davoudpour, Hamid, 2017. "A robust optimization model for cellular manufacturing system into supply chain management," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 667-679.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:116:y:2008:i:1:p:129-138. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.