A Supply Chain Design of Perishable Products Under Uncertainty

  • Himanshu Shrivastava
  • Pankaj Dutta
  • Mohan Krishnamoorthy
  • Pravin Suryawanshi
Conference paper

Abstract

Most supply chain (SC) studies often consider conventional products and assign little importance to the product perishability. In addition, most SC models in the literature assume that transportation routes are disruption-free. However, in reality, transportation routes are subject to various sorts of disruptions. In this chapter, we develop a stochastic mathematical model for a perishable product under conditions of route disruption and demand uncertainty. We investigate optimal facility location and distribution strategies that minimise the total cost of the SC. We propose two policies for decision-making under uncertainty. The first one is the risk-neutral policy in which the expected cost of the SC is minimised. The second policy is the risk-averse policy. The risk-averse policy is proposed through conditional value-at-risk (CVaR) approach in which the worst-case cost is minimised. The effectiveness of our model is demonstrated through an illustrative example. We observe that a resilient SC and a disruption-free SC have different designs. Finally, the effect of disruption uncertainties is presented through a statistical analysis.

Keywords

Conditional value-at-risk (CVaR) Distribution planning Network design Perishable products Resilient supply chains Uncertainty 

References

  1. 1.
    F. Aqlan, S. Lam Sarah, Supply chain risk modelling and mitigation. Int. J. Prod. Res. 53(18), 5640–5656 (2015)CrossRefGoogle Scholar
  2. 2.
    L.V. Snyder, Z. Atan, P. Peng, Y. Rong, A.J. Schmitt, B. Sinsoysal, OR/MS models for supply chain disruptions: a review. IIE Trans. 48(2), 89–109 (2016)CrossRefGoogle Scholar
  3. 3.
    G. Bhatia, C. Lane, A. Wain, Building resilience in supply chains, in An Initiative of the Risk Response Network in collaboration with Accenture. In World Economic Forum, Geneva, Switzerland (2013)Google Scholar
  4. 4.
    A. Baghalian, S. Rezapour, Z. Farahani Reza, Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur. J. Oper. Res. 227(1), 199–215 (2013)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    N. Azad, H. Davoudpour, K.D. Saharidis Georgios, M. Shiripour, A new model to mitigating random disruption risks of facility and transportation in supply chain network design. Int. J. Adv. Manuf. Technol. 70(9–12), 1757–1774 (2014)CrossRefGoogle Scholar
  6. 6.
    H. Shrivastava, P. Dutta, M. Krishnamoorthy, P. Suryawanshi, Designing a resilient supply chain network for perishable products with random disruptions, in Proceedings of The International MultiConference of Engineers and Computer Scientists 2017. Lecture Notes in Engineering and Computer Science. 15–17 March 2017, Hong Kong (2017), pp. 870–875Google Scholar
  7. 7.
    S. Negi, N. Anand, Issues and challenges in the supply chain of fruits & vegetables sector in India: a review. Int. J. Manag. Value Suppl. Chains 6(2), 47–62 (2015)CrossRefGoogle Scholar
  8. 8.
    V.R. Reddy, S.K. Singh, V. Anbumozhi, Food supply chain disruption due to natural disasters: entities, risks, and strategies for resilience. ERIA Discussion Paper 18 (2016)Google Scholar
  9. 9.
    Y. Merzifonluoglu, Risk averse supply portfolio selection with supply, demand and spot market volatility. Omega 57, 40–53 (2015)CrossRefGoogle Scholar
  10. 10.
    A. Madadi, M.E. Kurz, K.M. Taaffe, J.L. Sharp, S.J. Mason, Supply network design: risk-averse or risk-neutral? Comput. Ind. Eng. 78, 55–65 (2014)CrossRefGoogle Scholar
  11. 11.
    T. Sawik, Selection of resilient supply portfolio under disruption risks. Omega 41(2), 259–269 (2013)CrossRefGoogle Scholar
  12. 12.
    R.T. Rockafellar, S. Uryasev, Optimization of conditional value-at-risk. J. Risk 2, 21–42 (2000)CrossRefGoogle Scholar
  13. 13.
    M.C. Georgiadis, P. Tsiakis, P. Longinidis, M.K. Sofioglou, Optimal design of supply chain networks under uncertain transient demand variations. Omega 39(3), 254–272 (2011)CrossRefGoogle Scholar
  14. 14.
    M. Khouja, The single-period (news-vendor) problem: literature review and suggestions for future research. Omega 27(5), 537–553 (1999)CrossRefGoogle Scholar
  15. 15.
    P. Dutta, D. Chakraborty, Incorporating one-way substitution policy into the newsboy problem with imprecise customer demand. Eur. J. Oper. Res. 200(1), 99–110 (2010)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    R. Karuppiah, M. Martin, I.E. Grossmann, A simple heuristic for reducing the number of scenarios in two-stage stochastic programming. Comput. Chem. Eng. 34(8), 1246–1255 (2010)CrossRefGoogle Scholar
  17. 17.
    N.S. Sadghiani, S.A. Torabi, N. Sahebjamnia, Retail supply chain network design under operational and disruption risks. Trans. Res. Part E: Logistics Trans. Rev. 75, 95–114 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Himanshu Shrivastava
    • 1
  • Pankaj Dutta
    • 2
  • Mohan Krishnamoorthy
    • 3
    • 4
  • Pravin Suryawanshi
    • 2
  1. 1.IITB-Monash Research Academy, IIT BombayPowai, MumbaiIndia
  2. 2.Shailesh J. Mehta School of ManagementIndian Institute of Technology BombayPowai, MumbaiIndia
  3. 3.Department of Mechanical and Aerospace EngineeringMonash UniversityMelbourneAustralia
  4. 4.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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