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Logistics and Supply Chain Management

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Facility Location Under Uncertainty

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 356))

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Abstract

This chapter focuses on the role of facility location under uncertainty in logistics and supply chain management. Several prototype models are discussed, gathering relevant elements of practical relevance, including multiple facility echelons, multiple products or families of products, and time-dependent decisions. This comes along with two relevant aspects: resilience and sustainability. The first requires careful anticipation of potential disruptions in the underlying network and the development of mechanisms for efficiently and effectively overcoming them. The second is aligned with the vision of a progressively more circular economy, ensuring that reverse logistics operations complement the forward ones, thus contributing to a better utilization of the available resources while preserving the environment. Financial risk and supply chain risk analytics are concepts also discussed in this chapter. Humanitarian supply chains call for specific types of decisions, objective functions, and constraints. These are also discussed. The chapter also covers particular families of problems, such as those resulting from gathering the elements discussed in the previous chapters.

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References

  1. Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research,220, 67–78.

    Article  Google Scholar 

  2. Ang, E., Iancu, D. A., & Swinney, R. (2017). Disruption risk and optimal sourcing in multitier supply networks. Management Science,63, 2397–2771.

    Article  Google Scholar 

  3. Basole, R. C., & Bellamy, M. A. (2014). Supply network structure, visibility, and risk diffusion: A computational approach. Decision Sciences,45, 753–789.

    Article  Google Scholar 

  4. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research,52, 35–53.

    Article  Google Scholar 

  5. Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production,260, 120842.

    Article  Google Scholar 

  6. Caunhye, A. M., Zhang, Y., Li, M., & Nie, X. (2016). A location-routing model for prepositioning and distributing emergency supplies. Transportation Research Part E: Logistics and Transportation Review,90, 161–176.

    Article  Google Scholar 

  7. Chavez, M. M. M., Costa, Y., & Sarache, W. (2021). A three-objective stochastic location-inventory-routing model for agricultural waste-based biofuel supply chain. Computers & Industrial Engineering,162, 107759.

    Article  Google Scholar 

  8. Church, R. L., & Drezner, Z. (2022). Review of obnoxious facilities location problems. Computers & Operations Research,138, 105468.

    Article  Google Scholar 

  9. Cordeau, J.-F., Klibi, W., & Nickel, S. (2021). Logistics network design. In T. G. Crainic, M. Gendreau, & B. Gendron (Eds.), Network design with applications to transportation and logistics (Ch. 19, pp. 599–625). Springer.

    Google Scholar 

  10. Correia, I., Melo, T., & Saldanha-da-Gama, F. (2013). Comparing classical performance measures for a multi-period, two-echelon supply chain network design problem with sizing decisions. Computers & Industrial Engineering,64, 366–380.

    Article  Google Scholar 

  11. Demange, M., Gabrel, V., Haddad, M. A., & Murat, C. (2020). A robust p-center problem under pressure to locate shelters in wildfire context. EURO Journal on Computational Optimization,8, 103–139.

    Article  Google Scholar 

  12. Dolgui, A., & Ivanov, D. (2021). Ripple effect and supply chain disruption management: New trends and research directions. International Journal of Production Research,59, 102–109.

    Article  Google Scholar 

  13. Dönmez, Z., Kara, B. Y., Karsu, Ö., & Saldanha-da-Gama, F. (2021). Humanitarian facility location under uncertainty: Critical review and future prospects. Omega,102, 102393.

    Article  Google Scholar 

  14. Dunke, F., Heckmann, I., Nickel, S., & Saldanha-da-Gama, F. (2018). Time traps in supply chains: Is optimal still good enough? European Journal of Operational Research,264, 813–829.

    Article  Google Scholar 

  15. Escudero, L. F., & Monge, J. F. (2023). On risk management of multistage multiscale FLP under uncertainty. In H. A. Eiselt & V. Marianov (Eds.), Uncertainty in facility location models (pp. 355–390). Springer Nature Switzerland AG.

    Google Scholar 

  16. Escudero, L. F., & Salmeron, J. (2005). On a fix-and-relax framework for a class of project scheduling problems. Annals of Operations Research,140, 163–188.

    Article  Google Scholar 

  17. Fazayeli, S., Eydi, A., & Kamalabadi, I. N. (2018). Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm. Computers & Industrial Engineering,119, 233–246.

    Article  Google Scholar 

  18. Fernandes, L. J., & Saldanha-da-Gama, F. (2008). Contingency planning – a literature review. In Proceedings of SCMCC-08 Supply Chain Management and Competitiveness.

    Google Scholar 

  19. Fonseca, M. C., García-Sánchez, Á., Ortega-Mier, M., & Saldanha-da-Gama, F. (2010). A stochastic bi-objective location model for strategic reverse logistics. TOP,18, 158–184.

    Article  Google Scholar 

  20. Ghadge, A., Er, M., Ivanov, D., & Chaudhuri, A. (2022). Visualisation of ripple effect in supply chains under long-term, simultaneous disruptions: A system dynamics approach. International Journal of Production Research,60, 6173–6186.

    Article  Google Scholar 

  21. Hassanpour, S. T., Ke, G. Y., Zhao, J., & Tulett, D. M. (2023). Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows. Computers & Industrial Engineering,177, 109066.

    Article  Google Scholar 

  22. Heckmann, I., & Nickel, S. (2019). Location logistics in supply chain management. In G. Laporte, S. Nickel, & F. Saldanha-da-Gama (Eds.), Location Science (Ch. 16, 2nd ed., pp. 453–476). Springer.

    Google Scholar 

  23. Heckmann, I., Nickel, S., & Saldanha-da-Gama, F. (2023). Facility location and supply chain risk analytics. In H. A. Eiselt & V. Marianov (Eds.), Uncertainty in facility location models (pp. 155–181). Springer Nature Switzerland AG.

    Google Scholar 

  24. Heitsch, H., & Römisch, W. (2009). Scenario tree modeling for multistage stochastic programs. Mathematical Programming,118, 371–406.

    Article  Google Scholar 

  25. Holguín-Veras, J., Pérez, N., Jaller, M., van Wassenhove, L., & Aros-Vera, F. (2013). On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management,31, 262–280.

    Article  Google Scholar 

  26. Hosseini, S., & Ivanov, D. (2022). A new resilience measure for supply networks with the ripple effect considerations: A Bayesian network approach. Annals of Operations Research,319, 581–607.

    Article  Google Scholar 

  27. Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review,125, 285–307.

    Article  Google Scholar 

  28. Høyland, K., & Wallace, S. W. (2001). Generating scenario trees for multistage decision problems. Management Science,47, 295–307.

    Article  Google Scholar 

  29. Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research,55, 6158–6174.

    Article  Google Scholar 

  30. Jabbarzadeh, A., Fahimnia, B., & Sabouhi, F. (2018). Resilient and sustainable supply chain design: Sustainability analysis under disruption risks. International Journal of Production Research,56, 5945–5968.

    Article  Google Scholar 

  31. Javid, A. A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review,46, 582–597.

    Article  Google Scholar 

  32. Kamalahmadi, M., & Parast, M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics,171, 116–133.

    Article  Google Scholar 

  33. Kara, B., & Rancourt, M.-È. (2019). Location problems in humanitarian supply chains. In G. Laporte, S. Nickel, & F. Saldanha-da-Gama (Eds.), Location Science (Ch. 21, 2nd ed.). Springer.

    Google Scholar 

  34. Kaya, O., & Ozkok, D. (2020). A blood bank network design problem with integrated facility location, inventory and routing decisions. Networks and Spatial Economics,20, 757–783.

    Article  Google Scholar 

  35. Keutchayan, J., Ortmann, J., & Rei, W. (2021). Problem-driven scenario clustering in stochastic optimization. ar**v:2106.11717 [math.OC]. Available at: https://doi.org/10.48550/ar**v.2106.11717

  36. Kiya, F., & Davoudpour, H. (2012). Stochastic programming approach to re-designing a warehouse network under uncertainty. Transportation Research Part E: Logistics and Transportation Review,48, 919–936.

    Article  Google Scholar 

  37. Klibi, W., Lasalle, F., Martel, A., & Ichoua, S. (2010). The stochastic multiperiod location transportation problem. Transportation Science,44, 221–237.

    Article  Google Scholar 

  38. Klibi, W., Martel, A., & Guitouni, A. (2016). The impact of operations anticipations on the quality of stochastic location-allocation models. Omega,62, 19–33.

    Article  Google Scholar 

  39. Klose, A., & Drexl, A. (2005). Facility location models for distribution system design. European Journal of Operational Research,162, 4–29.

    Article  Google Scholar 

  40. Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research,291, 1117–1131.

    Article  Google Scholar 

  41. Li, Y., & Zobel, C. W. (2020). Exploring supply chain network resilience in the presence of the ripple effect. International Journal of Production Economics,228, 107693.

    Article  Google Scholar 

  42. MahmoumGonbadi, A., Genovese, A., & Sgalambro, A. (2021). Closed-loop supply chain design for the transition towards a circular economy: A systematic literature review of methods, applications and current gaps. Journal of Cleaner Production,323, 129101.

    Article  Google Scholar 

  43. Marín, A., & Pelegrín, B. (1998). The return plant location problem: Modelling and resolution. European Journal of Operational Research,104, 375–392.

    Article  Google Scholar 

  44. Melo, M. T., Nickel, S., & Saldanha da Gama, F. (2006). Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Computers & Operations Research,33, 181–208.

    Article  Google Scholar 

  45. 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.

    Article  Google Scholar 

  46. Mohamed, I. B., Klibi, W., Sadykov, R., Şen, H., & Vanderbeck, F. (2023). The two-echelon stochastic multi-period capacitated location-routing problem. European Journal of Operational Research,306, 645–667.

    Article  Google Scholar 

  47. Mohammadi, S., Darestani, S. A., Vahdani, B., & Alinezhad, A. (2020). A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns. Computers & Industrial Engineering,148, 106734.

    Article  Google Scholar 

  48. Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V. R., & Javanmard, S. (2014). Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Computers & Industrial Engineering,76, 204–221.

    Article  Google Scholar 

  49. Nickel, S., & Saldanha-da-Gama, F. (2019). Multi-period facility location. In G. Laporte, S. Nickel, & F. Saldanha-da-Gama (Eds.), Location Science (Ch. 11, 2nd ed., pp. 303–326). Springer.

    Google Scholar 

  50. Nickel, S., Saldanha-da-Gama, F., & Ziegler, H.-P. (2010). Stochastic programming approaches for risk aware supply chain network design problems. Technical Report, Fraunhofer ITWM, Kaiserslautern, Germany. Fraunhofer ITWM, Report 181. Available at: https://kluedo.ub.rptu.de/frontdoor/deliver/index/docId/2204/file/bericht_181.pdf

  51. Nickel, S., Saldanha-da-Gama, F., & Ziegler, H.-P. (2012). A multi-stage stochastic supply network design problem with financial decisions and risk management. Omega,40, 511–524.

    Article  Google Scholar 

  52. Paul, J. A., & Wang, X. (2019). Robust location-allocation network design for earthquake preparedness. Transportation Research Part B: Methodological,119, 139–155.

    Article  Google Scholar 

  53. Pires Ribeiro, J., & Barbosa-Póvoa, A. (2018). Supply chain resilience: Definitions and quantitative modelling approaches–A literature review. Computers & Industrial Engineering,115, 109–122.

    Article  Google Scholar 

  54. Rabbani, M., Heidari, R., & Yazdanparast, R. (2019). A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation. European Journal of Operational Research,272, 945–961.

    Article  Google Scholar 

  55. Saldanha-da-Gama, F. (2022). Facility location in logistics and transportation: An enduring relationship. Transportation Research Part E: Logistics and Transportation Review,160, 102903.

    Article  Google Scholar 

  56. Sazvar, Z., Tafakkori, K., Oladzad, N., & Nayeri, S. (2021). A capacity planning approach for sustainable-resilient supply chain network design under uncertainty: A case study of vaccine supply chain. Computers & Industrial Engineering,159, 107406.

    Article  Google Scholar 

  57. Shang, X., Zhang, G., Jia, B., & Almanaseer, M. (2022). The healthcare supply location-inventory-routing problem: A robust approach. Transportation Research Part E: Logistics and Transportation Review,158, 102588.

    Article  Google Scholar 

  58. Shapiro, A. (2021). Tutorial on risk neutral, distributionally robust and risk averse multistage stochastic programming. European Journal of Operational Research,288, 1–13.

    Article  Google Scholar 

  59. Shapiro, J. F. (2004). Challenges of strategic supply chain planning and modeling. Computers & Chemical Engineering, 28:855–861. FOCAPO 2003 Special Issue.

    Google Scholar 

  60. Shen, S. (2013). Optimizing designs and operations of a single network or multiple interdependent infrastructures under stochastic arc disruption. Computers & Operations Research,40, 2677–2688.

    Article  Google Scholar 

  61. Shen, Z.-J.M., & Qi, L. (2007). Incorporating inventory and routing costs in strategic location models. European Journal of Operational Research,179, 372–389.

    Article  Google Scholar 

  62. Thomas, A., & Kopczak, L. R. (2005). From logistics to supply chain management: the path forward in the humanitarian sector. Fritz Institute, White paper. Available at: http://www.fritzinstitute.org/PDFs/WhitePaper/EnablingDisasterResponse.pdf

  63. Van Engeland, J., Beliën, J., De Boeck, L., & De Jaeger, S. (2020). Literature review: Strategic network optimization models in waste reverse supply chains. Omega,91, 102012.

    Article  Google Scholar 

  64. Xu, N., & Nozick, L. (2009). Modeling supplier selection and the use of option contracts for global supply chain design. Computers & Operations Research,36, 2786–2800.

    Article  Google Scholar 

  65. Zanjirani Farahani, R., Rashidi Bajgan, H., Fahimnia, B., & Kaviani, M. (2015). Location-inventory problem in supply chains: A modelling review. International Journal of Production Research,53, 3769–3788.

    Article  Google Scholar 

  66. Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review,89, 182–214.

    Article  Google Scholar 

  67. Zhang, X., Zheng, Z., Zhu, Y., & Cai, K.-Y. (2014). Protection issues for supply systems involving random attacks. Computers & Operations Research,43, 137–156.

    Article  Google Scholar 

  68. Zsidisin, G. A., & Henke, M. (Eds.) (2019). Revisiting supply chain risk. Springer Nature Switzerland.

    Google Scholar 

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Authors and Affiliations

  1. Sheffield University Management School, Sheffield, UK

    Francisco Saldanha-da-Gama

  2. University of Chinese Academy of Science, Bei**g, China

    Shuming Wang

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  1. Francisco Saldanha-da-Gama
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  2. Shuming Wang
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Saldanha-da-Gama, F., Wang, S. (2024). Logistics and Supply Chain Management. In: Facility Location Under Uncertainty. International Series in Operations Research & Management Science, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-031-55927-3_12

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