Skip to main content

Logistics Network Design

  • 850 Accesses

Abstract

The design of logistics networks is one of the most important areas of application for multicommodity network design models. Logistics networks (or supply chains) connect suppliers, manufacturing plants, warehouses, distribution centers and customers to coordinate the acquisition of raw materials and components, their transformation into finished products and the delivery of these products to the customers. Over the last 40 years, the realism of logistics network design models has greatly improved and efficient solution methods have been developed to solve these models. There is now a vast literature on the topic with a very large number of models addressing the many problem variants encountered in practice. This chapter provides a general modeling framework that can be used to express many of these variants and gives a brief overview of the main solution methodologies. It also discusses two important and recent trends: the treatment of risk and uncertainty in the design of logistics networks and the incorporation of environmental, sustainability and reverse logistics aspects.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-64018-7_19
  • Chapter length: 27 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   149.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-64018-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   199.99
Price excludes VAT (USA)
Hardcover Book
USD   199.99
Price excludes VAT (USA)
Fig. 19.1
Fig. 19.2
Fig. 19.3
Fig. 19.4

References

  • Aikens, C. (1985). Facility location models for distribution planning. European Journal of Operational Research, 22, 263–279.

    CrossRef  Google Scholar 

  • Akçalı, E., Çetinkaya, S., & Uster, H. (2009). Network design for reverse and closed-loop supply chains: An annotated bibliography of models and solution approaches. Networks, 53(3), 231–248.

    CrossRef  Google Scholar 

  • 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.

    CrossRef  Google Scholar 

  • Arntzen, B., Brown, G., Harrison, T., & Trafton, L. (1995). Global supply chain management at Digital Equipment Corporation. Interfaces, 25(1), 69–93.

    CrossRef  Google Scholar 

  • Badri, H., Bashiri, M., & Hejazia, T. (2013). Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method. Computers & Operations Research, 40(4), 1143–1154.

    CrossRef  Google Scholar 

  • Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.

    CrossRef  Google Scholar 

  • Carle, M. A., Martel, A., & Zufferey, N. (2012). The CAT metaheuristic for the solution of multi-period activity-based supply chain network design problems. International Journal of Production Economics, 139(2), 664–677.

    CrossRef  Google Scholar 

  • Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135, 37–49.

    CrossRef  Google Scholar 

  • Cohen, M., & Lee, H. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of Manufacturing and Operations Management, 2, 81–104.

    Google Scholar 

  • Cordeau, J. F., Laporte, G., & Pasin, F. (2008). An iterated local search heuristic for the logistics network design problem with single assignment. International Journal of Production Economics, 113(2), 626–640.

    CrossRef  Google Scholar 

  • Cordeau, J. F., Pasin, F., & Solomon, M. (2006). An integrated model for logistics network design. Annals of Operations Research, 144, 59–82.

    CrossRef  Google Scholar 

  • Correia, I., & Saldanha-da-Gama, F. (2015). Facility location und uncertainty. In G. Laporte, S. Nickel, & F. Saldanha-da-Gama (Eds.), Location science (pp. 177–203). Berlin: Springer.

    Google Scholar 

  • Daskin, M. S. (2011). Network and discrete location: Models, algorithms, and applications. New York: Wiley.

    Google Scholar 

  • Dogan, K., & Goetschalckx, M. (1999). A primal decomposition method for the integrated design of multi-period production-distribution systems. IIE Transactions, 31, 1027–1036.

    Google Scholar 

  • 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.

    CrossRef  Google Scholar 

  • Easwaran, G., Uster, H. (2010). A closed-loop supply chain network design problem with integrated forward and reverse channel decisions. IIE Transactions, 42, 779–792.

    CrossRef  Google Scholar 

  • Elhedhli, S., & Goffin, J. L. (2005). Efficient production-distribution system design. Management Science, 51(7), 1151–1164.

    CrossRef  Google Scholar 

  • Fan, Y., Schwartz, F., Voß, S., & Woodruff, D. L. (2017). Stochastic programming for flexible global supply chain planning. Flexible Services and Manufacturing Journal, 29, 601–633.

    CrossRef  Google Scholar 

  • Fleischmann, B., Ferber, S., & Henrich, P. (2006). Strategic planning of BMW’s global production network. Interfaces, 36, 194–208.

    CrossRef  Google Scholar 

  • Garcia, D. J., & You, F. (2015). Supply chain design and optimization: Challenges and opportunities. Computers & Chemical Engineering, 81, 153–170.

    CrossRef  Google Scholar 

  • Geoffrion, A., & Graves, G. (1974). Multicommodity distribution system design by Benders decomposition. Management Science, 20, 822–844.

    CrossRef  Google Scholar 

  • Giusti, R., Iorfida, C., Li, Y., Manerba, D., Musso, S., Perboli, G., et al. (2019). Sustainable and de-stressed international supply-chains through the synchro-net approach. Sustainability, 11(4), 1083.

    CrossRef  Google Scholar 

  • Goetschalckx, M., Vidal, C., & Dogan, K. (2002). Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms. European Journal of Operational Research, 143, 1–18.

    CrossRef  Google Scholar 

  • Govindan, K., & Fattahi, M. (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, 183, 680–699.

    CrossRef  Google Scholar 

  • Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263, 108–141.

    CrossRef  Google Scholar 

  • Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk–definition, measure and modeling. Omega, 52,119–132.

    CrossRef  Google Scholar 

  • Heckmann, I., & Nickel, S. (2017). Rethinking supply chain risk analysis – common flaws & main elements. Supply Chain Forum, 18, 84–95.

    CrossRef  Google Scholar 

  • Hinojosa, Y., Kalcsics, J., Nickel, S., Puerto, J., & Velten, S. (2008). Dynamic supply chain design with inventory. Computers & Operations Research, 35(2), 373–391.

    CrossRef  Google Scholar 

  • Jena, S. D., Cordeau, J. F., & Gendron, B. (2015). Dynamic facility location with generalized modular capacities. Transportation Science, 49(3), 484–499.

    CrossRef  Google Scholar 

  • Klibi, W., Lasalle, F., Martel, A., & Ichoua, S. (2010a). The stochastic multiperiod location transportation problem. Transportation Science, 44(2), 221–237.

    CrossRef  Google Scholar 

  • Klibi, W., & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223, 644–658.

    CrossRef  Google Scholar 

  • Klibi, W., & Martel, A. (2013). The design of robust value-creating supply chain networks. OR Spectrum, 35(4), 867–903.

    CrossRef  Google Scholar 

  • Klibi, W., Martel, A., & Guitouni, A. (2010b). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283–293.

    CrossRef  Google Scholar 

  • Laporte, G., Nickel, S., & Saldanha da Gama, F. (2016). Location science. New York: Springer.

    Google Scholar 

  • Mariel, K., & Minner, S. (2017). Benders decomposition for a strategic network design problem under NAFTA local content requirements. Omega, 68, 62–75.

    CrossRef  Google Scholar 

  • Martel, A. (2005). The design of production-distribution networks: A mathematical programming approach. In J. Geunes, & P. Pardalos (Eds.), Supply chain optimization. New York: Springer.

    Google Scholar 

  • Martel, A., & Klibi, W. (2016). Designing value-creating supply chain networks. New York: Springer.

    CrossRef  Google Scholar 

  • Melo, M., 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(1), 181–208.

    CrossRef  Google Scholar 

  • Melo, M., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401–412.

    CrossRef  Google Scholar 

  • Melo, M., Nickel, S., & Saldanha-da-Gama, F. (2012). A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon. International Journal of Production Economics,136(1), 218–230.

    Google Scholar 

  • Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2014). An efficient heuristic approach for a multi-period logistics network redesign problem. TOP, 22(1), 80–108.

    CrossRef  Google Scholar 

  • Mota, B., Gomes, M., Carvalho, A., & Barbosa-Povoa, A. (2014). Towards supply chain sustainability: Economic, environmental and social design and planning. Journal of Cleaner Production, 105, 14–27.

    CrossRef  Google Scholar 

  • Nickel, S., & Saldanha-da-Gama, F. (2015). Multi-period facility location. In G. Laporte, S. Nickel, & F. Saldanha-da-Gama (Eds.), Location science (pp. 289–310). Berlin: Springer.

    Google Scholar 

  • Pimentel, B. S., Mateus, G. R., & Almeida, F. A. (2013). Stochastic capacity planning and dynamic network design. International Journal of Production Economics, 145, 139–149.

    CrossRef  Google Scholar 

  • Pirkul, H., & Jayaraman, V. (1998). A multi-commodity, multi-plant, capacitated facility location problem: Formulation and efficient heuristic solution. Computers & Operations Research, 25, 869–878.

    CrossRef  Google Scholar 

  • ReVelle, C. S., & Eiselt, H. A. (2005). Location analysis: A synthesis and survey. European Journal of Operational Research, 165, 1–19.

    CrossRef  Google Scholar 

  • Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167, 96–115.

    CrossRef  Google Scholar 

  • Schutz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, 199, 409–419.

    CrossRef  Google Scholar 

  • Shapiro, A., Dentcheva, D., & Ruszczynski, A. (2009). Lectures on stochastic programming. New York, USA: SIAM and MPS, USA.

    CrossRef  Google Scholar 

  • Snyder, L., & Daskin, M. (2006). Stochastic p-robust location problems. IIE Transactions, 38(11), 971–985.

    CrossRef  Google Scholar 

  • Tang, C., & Zhou, S. (2012). Research advances in environmentally and socially sustainable operations. European Journal of Operational Research, 223, 585–594.

    CrossRef  Google Scholar 

  • Thanh, P. N., Bostel, N., & Peton, O. (2008). A dynamic model for facility location in the design of complex supply chains. International Journal of Production Economics, 113(2), 678–693.

    CrossRef  Google Scholar 

  • Thanh, P. N., Peton, O., & Bostel, N. (2010). A linear relaxation-based heuristic approach for logistics network design. Computers & Industrial Engineering, 59(4), 964–975.

    CrossRef  Google Scholar 

  • Ulstein, N., Christiansen, M., Grønhaug, R., Magnussen, N., & Solomon, M. (2006). Elkem uses optimization in redesigning its supply chain. Interfaces, 36, 314–325.

    CrossRef  Google Scholar 

  • Vidal, C., & Goetschalckx, M. (1997). Strategic production-distribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Research, 98, 1–18.

    CrossRef  Google Scholar 

  • Vidal, C., & Goetschalckx, M. (2001). A global supply chain model with transfer pricing and transportation cost allocation. European Journal of Operational Research, 129, 134–158.

    CrossRef  Google Scholar 

  • Wesolowsky, G., & Truscott, W. (1975). The multi-period location-allocation problem with relocation of facilities. Management Science, 22, 57–66.

    CrossRef  Google Scholar 

  • You, F., & Grossmann, I. E. (2008). Design of responsive supply chains under demand uncertainty. Computers & Chemical Engineering, 32, 3090–3111.

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jean-François Cordeau .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s)

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Cordeau, JF., Klibi, W., Nickel, S. (2021). Logistics Network Design. In: Crainic, T.G., Gendreau, M., Gendron, B. (eds) Network Design with Applications to Transportation and Logistics. Springer, Cham. https://doi.org/10.1007/978-3-030-64018-7_19

Download citation