Advertisement

Annals of Operations Research

, Volume 257, Issue 1–2, pp 15–44 | Cite as

Robust supply chain network design: an optimization model with real world application

  • Shiva Zokaee
  • Armin Jabbarzadeh
  • Behnam Fahimnia
  • Seyed Jafar Sadjadi
Article

Abstract

This paper presents a robust optimization model for the design of a supply chain facing uncertainty in demand, supply capacity and major cost data including transportation and shortage cost parameters. We first present a base model that aims to determine the strategic ‘location’ and tactical ‘allocation’ decisions for a deterministic four-tier supply chain. The model is then extended to incorporate uncertainty in key input parameters using a robust optimization approach that can overcome the limitations of scenario-based solution methods in a tractable way, i.e. without excessive changes in complexity of the underlying base deterministic model. The application of the approach is investigated in an actual case study where real data is utilized to design a bread supply chain network. Numerical results obtained from model implementation and sensitivity analysis experiments arrive at important managerial insights and practical implications.

Keywords

Supply chain design Location Allocation Uncertainty Robust optimization Case study 

References

  1. Alem, J. D., & Morabito, R. (2012). Production planning in furniture settings via robust optimization. Computers & Operations Research, 39, 139–150.CrossRefGoogle Scholar
  2. Aryanezhad, M. B., Jalali, S. G., & Jabbarzadeh, A. (2010). An integrated supply chain design model with random disruptions consideration. African Journal of Business Management, 4, 2393–2401.Google Scholar
  3. Assavapokeea, T., Realff, M. J., & Ammonsc, J. C. (2008a). Min–max regret robust optimization approach on interval data uncertainty. Journal of Optimization Theory and Applications, 137, 297–316.CrossRefGoogle Scholar
  4. Assavapokeea, T., Realff, M. J., Ammonsc, J. C., & Hongd, I. H. (2008b). Scenario relaxation algorithm for finite scenario-based min–max regret and min–max relative regret robust optimization. Computers & Operations Research, 35, 2093–2102.CrossRefGoogle Scholar
  5. 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, 116, 129–138.CrossRefGoogle Scholar
  6. Babazadeh, R., & Razmi, J. (2012). A robust stochastic programming approach for agile and responsive logistics under operational and disruption risks. International Journal of Logistics Systems and Management, 13(4), 458–482.CrossRefGoogle Scholar
  7. Baron, O., Milner, J., & Naseraldin, H. (2011). Facility location: A robust optimization approach. Production and Operations Management, 20(5), 772–785.CrossRefGoogle Scholar
  8. Bashiri, M., Badri, H., & Talebi, J. (2012). A new approach to tactical and strategic planning in production–distribution networks. Applied Mathematical Modeling, 36, 1703–1717.CrossRefGoogle Scholar
  9. Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming, Series B, 88, 411–424.CrossRefGoogle Scholar
  10. Ben-Tal, A., Goryashko, A., Guslitzer, E., & Nemirovski, A. (2004). Adjustable robust solutions of uncertain linear programs. Mathematical Programming, 99(2), 351–376.CrossRefGoogle Scholar
  11. Ben-Tal, A., Chung, B. D., Mandala, S. R., & Yao, T. (2011). Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Research Part B, 45(8), 1177–1189.CrossRefGoogle Scholar
  12. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.CrossRefGoogle Scholar
  13. Bertsimas, D., & Thiele, A. (2006). A robust optimization approach to inventory theory. Operations Research, 54(1), 150–168.CrossRefGoogle Scholar
  14. Bozorgi-Amiri, A., Jabalameli, M. S., & Mirzapour Al-e-Hashem, S. M. (2011). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum, pp. 1–29.Google Scholar
  15. Bozorgi-Amiri, A., Jabalameli, M. S., Alinaghian, M., & Heydari, M. (2012). A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology, 60(1), 357–371.CrossRefGoogle Scholar
  16. Chen, X., & Zhang, Y. (2009). Uncertain linear programs: Extended affinely adjustable robust counterparts. Operations Research, 57(6), 1469–1482.CrossRefGoogle Scholar
  17. Cordeau, J. F., Pasin, F., & Solomon, M. M. (2006). An integrated model for logistics network design. Annals of Operations Research, 144(1), 59–82.CrossRefGoogle Scholar
  18. Esmaeilikia, M., Fahimnia, B., Sarkis, J., Govindan, K., Kumar, A., & Mo, J. (2014a). A tactical supply chain planning model with multiple flexibility options: An empirical evaluation. Annals of Operations Research, 1–26.Google Scholar
  19. Esmaeilikia, M., Fahimnia, B., Sarkis, J., Govindan, K., Kumar, A., & Mo, J. (2014b). Tactical supply chain planning models with inherent flexibility: Definition and review. Annals of Operations Research, 1–21.Google Scholar
  20. Fahimnia, B., Farahani, R., & Sarkis, J. (2013). Integrated aggregate supply chain planning using Memetic Algorithm: A performance analysis case study. International Journal of Production Research, 51(18), 5354–5373.CrossRefGoogle Scholar
  21. Georgiadis, M. C., Tsiakis, P., Longinidis, P., & Sofioglou, M. K. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39, 254–272.CrossRefGoogle Scholar
  22. Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward-reverse logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling, 38(9), 2630–2647.CrossRefGoogle Scholar
  23. Jabbarzadeh, A., Jalali Naini, S.G., Davoudpour, H., Azad, N. (2012). Designing a supply chain network under the risk of disruption. Mathematical Problems in Engineering. doi: 10.1155/2012/234324.
  24. Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network Design for the supply of blood in disasters: A robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225–244.Google Scholar
  25. Jeong, K. Y., Hong, J. D., & Xie, Y. (2014). Design of emergency logistics networks, taking efficiency, risk and robustness into consideration. International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management, 17(1), 1–22.CrossRefGoogle Scholar
  26. Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 203, 283–293.CrossRefGoogle Scholar
  27. Lalmazloumian, M., Wong, K. Y., Govindan, K., & Kannan, D. (2013). A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Annals of Operations Research. doi: 10.1007/s10479-013-1421-5.
  28. Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management—a review. European Journal of Operational Research, 196, 401–412.CrossRefGoogle Scholar
  29. Mirzapour Al-e-hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 134, 28–42.CrossRefGoogle Scholar
  30. Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2), 264–281.CrossRefGoogle Scholar
  31. Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E, 49, 217–249.Google Scholar
  32. Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research, 37, 668–683.CrossRefGoogle Scholar
  33. Soyster, A. L. (1973). Convex programming with set-inclusive constrains and applications to inexact Linear programming. Operations Research Letters, 21(5), 1154–1157.CrossRefGoogle Scholar
  34. Tang, T. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103, 451–488.CrossRefGoogle Scholar
  35. Wang, B., & He, S. (2009). Robust optimization model and algorithm for logistics center location and allocation under uncertain environment. Journal of Transportation Systems Engineering and Information Technology, 9(2), 69–74.CrossRefGoogle Scholar
  36. Yu, Ch S, & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64, 385–397.CrossRefGoogle Scholar
  37. Zhang, Z. H., & Jiang, H. (2014). A robust counterpart approach to the bi-objective emergency medical service design problem. Applied Mathematical Modelling, 38(3), 1033–1040.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Shiva Zokaee
    • 1
  • Armin Jabbarzadeh
    • 1
  • Behnam Fahimnia
    • 2
  • Seyed Jafar Sadjadi
    • 1
  1. 1.Department of Industrial EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Institute of Transport and Logistics Studies (ITLS)The University of Sydney Business SchoolSydneyAustralia

Personalised recommendations