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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aikens, C. (1985). Facility location models for distribution planning. European Journal of Operational Research, 22, 263–279.
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.
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.
Arntzen, B., Brown, G., Harrison, T., & Trafton, L. (1995). Global supply chain management at Digital Equipment Corporation. Interfaces, 25(1), 69–93.
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.
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
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.
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.
Cohen, M., & Lee, H. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of Manufacturing and Operations Management, 2, 81–104.
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.
Cordeau, J. F., Pasin, F., & Solomon, M. (2006). An integrated model for logistics network design. Annals of Operations Research, 144, 59–82.
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.
Daskin, M. S. (2011). Network and discrete location: Models, algorithms, and applications. New York: Wiley.
Dogan, K., & Goetschalckx, M. (1999). A primal decomposition method for the integrated design of multi-period production-distribution systems. IIE Transactions, 31, 1027–1036.
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.
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.
Elhedhli, S., & Goffin, J. L. (2005). Efficient production-distribution system design. Management Science, 51(7), 1151–1164.
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.
Fleischmann, B., Ferber, S., & Henrich, P. (2006). Strategic planning of BMW’s global production network. Interfaces, 36, 194–208.
Garcia, D. J., & You, F. (2015). Supply chain design and optimization: Challenges and opportunities. Computers & Chemical Engineering, 81, 153–170.
Geoffrion, A., & Graves, G. (1974). Multicommodity distribution system design by Benders decomposition. Management Science, 20, 822–844.
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.
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.
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.
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.
Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk–definition, measure and modeling. Omega, 52,119–132.
Heckmann, I., & Nickel, S. (2017). Rethinking supply chain risk analysis – common flaws & main elements. Supply Chain Forum, 18, 84–95.
Hinojosa, Y., Kalcsics, J., Nickel, S., Puerto, J., & Velten, S. (2008). Dynamic supply chain design with inventory. Computers & Operations Research, 35(2), 373–391.
Jena, S. D., Cordeau, J. F., & Gendron, B. (2015). Dynamic facility location with generalized modular capacities. Transportation Science, 49(3), 484–499.
Klibi, W., Lasalle, F., Martel, A., & Ichoua, S. (2010a). The stochastic multiperiod location transportation problem. Transportation Science, 44(2), 221–237.
Klibi, W., & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223, 644–658.
Klibi, W., & Martel, A. (2013). The design of robust value-creating supply chain networks. OR Spectrum, 35(4), 867–903.
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.
Laporte, G., Nickel, S., & Saldanha da Gama, F. (2016). Location science. New York: Springer.
Mariel, K., & Minner, S. (2017). Benders decomposition for a strategic network design problem under NAFTA local content requirements. Omega, 68, 62–75.
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.
Martel, A., & Klibi, W. (2016). Designing value-creating supply chain networks. New York: Springer.
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.
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.
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.
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.
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.
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.
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.
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.
ReVelle, C. S., & Eiselt, H. A. (2005). Location analysis: A synthesis and survey. European Journal of Operational Research, 165, 1–19.
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.
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.
Shapiro, A., Dentcheva, D., & Ruszczynski, A. (2009). Lectures on stochastic programming. New York, USA: SIAM and MPS, USA.
Snyder, L., & Daskin, M. (2006). Stochastic p-robust location problems. IIE Transactions, 38(11), 971–985.
Tang, C., & Zhou, S. (2012). Research advances in environmentally and socially sustainable operations. European Journal of Operational Research, 223, 585–594.
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.
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.
Ulstein, N., Christiansen, M., Grønhaug, R., Magnussen, N., & Solomon, M. (2006). Elkem uses optimization in redesigning its supply chain. Interfaces, 36, 314–325.
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.
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.
Wesolowsky, G., & Truscott, W. (1975). The multi-period location-allocation problem with relocation of facilities. Management Science, 22, 57–66.
You, F., & Grossmann, I. E. (2008). Design of responsive supply chains under demand uncertainty. Computers & Chemical Engineering, 32, 3090–3111.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s)
About this chapter
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
DOI: https://doi.org/10.1007/978-3-030-64018-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64017-0
Online ISBN: 978-3-030-64018-7
eBook Packages: Business and ManagementBusiness and Management (R0)