Abstract
Facility location decisions play a critical role in the design of supply chain networks. This chapter outlines key features of facility location problems and their integration with various logistic operations in the context of network (re)design. Modeling approaches and solution methods are also discussed for these challenging combinatorial optimization problems. In addition, we examine five emerging trends that will continue to drive research in this area, namely, sustainable development, the transition to a circular economy, data source-related issues, disruptive events and resilience, and omnichannel distribution in the retail sector. We review and classify recent contributions, identify research gaps, and provide directions for future research.
Keywords
- Facility location
- Sustainable network design
- Circular economy
- Data scarcity
- Resilience
- Omnichannel
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References
Agrawal, V. V., & Bellos, I. (2016). Servicizing in supply chains and environmental implications. In A. Atasu (Ed.), Environmentally responsible supply chains (pp. 109–124). Springer. https://doi.org/10.1007/978-3-319-30094-8_7
Ahmadi, M., Seifi, A., & Tootooni, B. (2015). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, 75, 145–163. https://doi.org/10.1016/j.tre.2015.01.008
Ahmadi-Javid, A., Seyedi, P., & Syam, S. S. (2017). A survey of healthcare facility location. Computers & Operations Research, 79, 223–263. https://doi.org/10.1016/j.cor.2016.05.018
Akçalı, E., Çetinkaya, S., & Üster, H. (2009). Network design for reverse and closed-loop supply chains: An annotated bibliography of models and solution approaches. Networks, 53(3), 231–248. https://doi.org/10.1002/net.20267
Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369–384. https://doi.org/10.1016/j.cor.2016.10.012
Alumur, S. A., & Bektaş, T. (2019). Green location problems. In G. Laporte, S. Nickel, & F. Saldanha da Gama (Eds.), Location science (2nd ed., pp. 611–630). Springer. https://doi.org/10.1007/978-3-030-32177-2_20
Alumur, S., Kara, B., & Melo, M. T. (2015). Location and logistics. In G. Laporte, S. Nickel, & F. Saldanha da Gama (Eds.), Location science (1st ed., pp. 419–441). Springer. https://doi.org/10.1007/978-3-319-13111-5_16
Anvari, S., & Turkay, M. (2017). The facility location problem from the perspective of triple bottom line accounting of sustainability. International Journal of Production Research, 55(21), 6266–6287. https://doi.org/10.1080/00207543.2017.1341064
Arampantzi, C., & Minis, I. (2017). A new model for designing sustainable supply chain networks and its application to a global manufacturer. Journal of Cleaner Production, 156, 276–292. https://doi.org/10.1016/j.jclepro.2017.03.164
Aranguren, M., Castillo-Villar, K. K., & Aboytes-Ojeda, M. (2021). A two-stage stochastic model for co-firing biomass supply chain networks. Journal of Cleaner Production, 319, 128582. https://doi.org/10.1016/j.jclepro.2021.128582
Arora, N., Charm, T., Grimmelt, A., Ortega, M., Robinson, K., Sexauer, C., & Yamakawa, N. (2020). A global view of how consumer behavior is changing amid COVID-19. McKinsey and Company.
Arslan, A. N., Klibi, W., & Montreuil, B. (2021). Distribution network deployment for omnichannel retailing. European Journal of Operational Research, 294(3), 1042–1058. https://doi.org/10.1016/j.ejor.2020.04.016
Azadegan, A., Mellat Parast, M., Lucianetti, L., Nishant, R., & Blackhurst, J. (2020). Supply chain disruptions and business continuity: An empirical assessment. Decision Sciences, 51(1), 38–73. https://doi.org/10.1111/deci.12395
BCI. (2018). Supply chain resilience report 2018, 10th annual survey, & facts. Retrieved 26 April, 2022, from https://www.thebci.org/static/uploaded/c50072bf-df5c-4c98-a5e1876aafb15bd0.pdf
Behzadi, G., O’Sullivan, M. J., & Olsen, T. L. (2020). On metrics for supply chain resilience. European Journal of Operational Research, 287(1), 145–158. https://doi.org/10.1016/j.ejor.2020.04.040
Beresnev, V., & Melnikov, A. (2018). Exact method for the capacitated competitive facility location problem. Computers & Operations Research, 95, 73–82. https://doi.org/10.1016/j.cor.2018.02.013
Biajoli, F. L., Chaves, A. A., & Lorena, L. A. N. (2019). A biased random-key genetic algorithm for the two-stage capacitated facility location problem. Expert Systems with Applications, 115, 418–426. https://doi.org/10.1016/j.eswa.2018.08.024
Bijmolt, T. H., Broekhuis, M., De Leeuw, S., Hirche, C., Rooderkerk, R. P., Sousa, R., & Zhu, S. X. (2021). Challenges at the marketing-operations interface in omni-channel retail environments. Journal of Business Research, 122, 864–874. https://doi.org/10.1016/j.jbusres.2019.11.034
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. https://doi.org/10.1016/j.jclepro.2020.120842
Brandenburg, M., & Rebs, T. (2015). Sustainable supply chain management: A modeling perspective. Annals of Operations Research, 229(1), 213–252. https://doi.org/10.1007/s10479-015-1853-1
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312. https://doi.org/10.1016/j.ejor.2013.09.032
Brandstätter, G., Leitner, M., & Ljubić, I. (2020). Location of charging stations in electric car sharing systems. Transportation Science, 54(5), 1408–1438. https://doi.org/10.1287/trsc.2019.0931
Bressanelli, G., Perona, M., & Saccani, N. (2018). Challenges in supply chain redesign for the circular economy: A literature review and a multiple case study. International Journal of Production Research, 57(23), 7395–7422. https://doi.org/10.1080/00207543.2018.1542176
Brimberg, J., Drezner, Z., Mladenović, N., & Salhi, S. (2014). A new local search for continuous location problems. European Journal of Operational Research, 232(2), 256–265. https://doi.org/10.1016/j.ejor.2013.06.022
Bubicz, M. E., Barbosa-Póvoa, A. P. F. D., & Carvalho, A. (2019). Incorporating social aspects in sustainable supply chains: Trends and future directions. Journal of Cleaner Production, 237, 117500. https://doi.org/10.1016/j.jclepro.2019.06.331
Budak, A. (2020). Sustainable reverse logistics optimization with triple bottom line approach: An integration of disassembly line balancing. Journal of Cleaner Production, 270, 122475. https://doi.org/10.1016/j.jclepro.2020.122475
Çalik, H., & Fortz, B. (2019). A benders decomposition method for locating stations in a one-way electric car sharing system under demand uncertainty. Transportation Research Part B: Methodological, 125, 121–150. https://doi.org/10.1016/j.trb.2019.05.004
Calzolari, T., Genovese, A., & Brint, A. (2022). Circular economy indicators for supply chains: A systematic literature review. Environmental and Sustainability Indicators, 13, 100160. https://doi.org/10.1016/j.indic.2021.100160
Castro, J., Nasini, S., & Saldanha da Gama, F. (2017). A cutting-plane approach for large-scale capacitated multi-period facility location using a specialized interior-point method. Mathematical Programming, 163(1), 411–444. https://doi.org/10.1007/s10107-016-1067-6
Celik Turkoglu, D., & Erol Genevois, M. (2020). A comparative survey of service facility location problems. Annals of Operations Research, 292(1), 399–468. https://doi.org/10.1007/s10479-019-03385-x
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. https://doi.org/10.1016/j.cie.2021.107759
Chen, J., Liang, Y., Shen, H., Shen, Z.-J. M., & Xue, M. (2021). Offline-channel planning in smart omnichannel retailing. Manufacturing & Service Operations Management. https://doi.org/10.1287/msom.2021.1036
Chhibber, D., Bisht, D. C., & Srivastava, P. K. (2021). Pareto-optimal solution for fixed-charge solid transportation problem under intuitionistic fuzzy environment. Applied Soft Computing, 107, 107368. https://doi.org/10.1016/j.asoc.2021.107368
Chou, M. C., Liu, Q., Teo, C.-P., & Yeo, D. (2019). Models for effective deployment and redistribution of shared bicycles with location choices. In M. Hu (Ed.), Sharing economy (pp. 409–434). Springer. https://doi.org/10.1007/978-3-030-01863-4_17
Coll, N., Fort, M., & Saus, M. (2022). Coverage area maximization with parallel simulated annealing. Expert Systems with Applications, 202, 117185. https://doi.org/10.1016/j.eswa.2022.117185
Correia, I., & Melo, T. (2016). Multi-period capacitated facility location under delayed demand satisfaction. European Journal of Operational Research, 255(3), 729–746. https://doi.org/10.1016/j.ejor.2016.06.039
Correia, I., & Melo, T. (2017). A multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment. Computers & Industrial Engineering, 110, 307–321. https://doi.org/10.1016/j.cie.2017.06.003
Correia, I., & Melo, T. (2021). Integrated facility location and capacity planning under uncertainty. Computational & Applied Mathematics, 40(5), 1–36. https://doi.org/10.1007/s40314-021-01560-0
Correia, I., & Saldanha da Gama, F. (2019). Facility location under uncertainty. In G. Laporte, S. Nickel, & F. Saldanha da Gama (Eds.), Location science (2nd ed., pp. 185–213). Springer. https://doi.org/10.1007/978-3-030-32177-2_8
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(1), 366–380. https://doi.org/10.1016/j.cie.2012.11.001
Cortinhal, M. J., Lopes, M. J., & Melo, M. T. (2015). Dynamic design and re-design of multi-echelon, multi-product logistics networks with outsourcing opportunities: A computational study. Computers & Industrial Engineering, 90, 118–131. https://doi.org/10.1016/j.cie.2015.08.019
Cortinhal, M. J., Lopes, M. J., & Melo, M. T. (2019). A multi-stage supply chain network design problem with in-house production and partial product outsourcing. Applied Mathematical Modelling, 70, 572–594. https://doi.org/10.1016/j.apm.2019.01.046
Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Sciences, 38(1), 131–156. https://doi.org/10.1111/j.1540-5915.2007.00151.x
Cui, A. S., Zhao, M., & Ravichandran, T. (2011). Market uncertainty and dynamic new product launch strategies: A system dynamics model. IEEE Transactions on Engineering Management, 58(3), 530–550. https://doi.org/10.1109/TEM.2010.2100822
Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430. https://doi.org/10.1080/00207543.2017.1387680
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. https://doi.org/10.1016/j.omega.2021.102393
Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Capstone Publishing.
Eriskin, L., Karatas, M., & Zheng, Y. -J. (2022). A robust multi-objective model for healthcare resource management and location planning during pandemics. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04760-x
Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, 54, 11–32. https://doi.org/10.1016/j.omega.2015.01.006
Farahani, R. Z., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34(7), 1689–1709. https://doi.org/10.1016/j.apm.2009.10.005
Farahani, R. Z., Fallah, S., Ruiz, R., Hosseini, S., & Asgari, N. (2019). OR models in urban service facility location: A critical review of applications and future developments. European Journal of Operational Research, 276(1), 1–27. https://doi.org/10.1016/j.ejor.2018.07.036
Fattahi, M., & Govindan, K. (2018). A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study. Transportation Research Part E: Logistics and Transportation Review, 118, 534–567. https://doi.org/10.1016/j.tre.2018.08.008
Fattahi, M., Govindan, K., & Farhadkhani, M. (2021). Sustainable supply chain planning for biomass-based power generation with environmental risk and supply uncertainty considerations: A real-life case study. International Journal of Production Research, 59(10), 3084–3108. https://doi.org/10.1080/00207543.2020.1746427
Feitó-Cespón, M., Sarache, W., Piedra-Jimenez, F., & Cespón-Castro, R. (2017). Redesign of a sustainable reverse supply chain under uncertainty: A case study. Journal of Cleaner Production, 151, 206–217. https://doi.org/10.1016/j.jclepro.2017.03.057
Feitó-Cespón, M., Costa, Y., Pishvaee, M. S., & Cespón-Castro, R. (2021). A fuzzy inference based scenario building in two-stage optimization framework for sustainable recycling supply chain redesign. Expert Systems with Applications, 165, 113906. https://doi.org/10.1016/j.eswa.2020.113906
Gao, X., & Cao, C. (2020). A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction. Journal of Cleaner Production, 270, 122405. https://doi.org/10.1016/j.jclepro.2020.122405
German Federal Ministry of Labour and Social Affairs. (2021). Supply Chain Act. Retrieved 21 May, 2022, from https://www.csr-in-deutschland.de/EN/Business-Human-Rights/Supply-Chain-Act/supply-chain-act.html
Ghaderi, H., Moini, A., & Pishvaee, M. S. (2018). A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design. Journal of Cleaner Production, 179, 368–406. https://doi.org/10.1016/j.jclepro.2017.12.218
Gholizadeh, H., & Fazlollahtabar, H. (2020). Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry. Computers & Industrial Engineering, 147, 106653. https://doi.org/10.1016/j.cie.2020.106653
Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603–626. https://doi.org/10.1016/j.ejor.2014.07.012
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(1), 108–141. https://doi.org/10.1016/j.ejor.2017.04.009
Govindan, K., Jafarian, A., & Nourbakhsh, V. (2019). Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics. Computers & Operations Research, 110, 220–235. https://doi.org/10.1016/j.cor.2018.11.013
Guerrero-Lorente, J., Gabor, A. F., & Ponce-Cueto, E. (2020). Omnichannel logistics network design with integrated customer preference for deliveries and returns. Computers & Industrial Engineering, 144, 106433. https://doi.org/10.1016/j.cie.2020.106433
Guo, Y., Yu, J., Boulaksil, Y., Allaoui, H., & Hu, F. (2021). Solving the sustainable supply chain network design problem by the multi-neighborhoods descent traversal algorithm. Computers & Industrial Engineering, 154, 107098. https://doi.org/10.1016/j.cie.2021.107098
Guo, C., Hu, H., Wang, S., Rodriguez, L. F., Ting, K., & Lin, T. (2022). Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply. Renewable Energy, 186, 378–393. https://doi.org/10.1016/j.renene.2021.12.144
Haeri, A., Hosseini-Motlagh, S.-M., Ghatreh Samani, M. R., & Rezaei, M. (2020). A mixed resilient-efficient approach toward blood supply chain network design. International Transactions in Operational Research, 27(4), 1962–2001. https://doi.org/10.1111/itor.12714
Hendricks, K. B., & Singhal, V. R. (2005). An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm. Production and Operations Management, 14(1), 35–52. https://doi.org/10.1111/j.1937-5956.2005.tb00008.x
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. https://doi.org/10.1016/j.tre.2019.03.001
International Organization for Standardization. (2010). ISO 26000. Retrieved 20 May, 2022, from https://www.iso.org/iso-26000-social-responsibility.html
Ishfaq, R., & Bajwa, N. (2019). Profitability of online order fulfillment in multi-channel retailing. European Journal of Operational Research, 272(3), 1028–1040. https://doi.org/10.1016/j.ejor.2018.07.047
Ishfaq, R., Defee, C. C., Gibson, B. J., & Raja, U. (2016). Realignment of the physical distribution process in omni-channel fulfillment. International Journal of Physical Distribution & Logistics Management, 6(6–7), 543–561. https://doi.org/10.1108/IJPDLM-02-2015-0032
Ishfaq, R., Davis-Sramek, B., & Gibson, B. (2021). Digital supply chains in omnichannel retail: A conceptual framework. Journal of Business Logistics, 43(2), 169–188. https://doi.org/10.1111/jbl.12277
Islam, M. T., & Huda, N. (2018). Reverse logistics and closed-loop supply chain of waste electrical and electronic equipment (WEEE)/E-waste: A comprehensive literature review. Resources, Conservation & Recycling, 137, 48–75. https://doi.org/10.1016/j.resconrec.2018.05.026
Ivanov, D., & Dolgui, A. (2019). Low-certainty-need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15–16), 5119–5136. https://doi.org/10.1080/00207543.2018.1521025
Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2021). Global supply chain and operations management: A decision-oriented introduction to the creation of value (3rd ed.). Springer. https://doi.org/10.1007/978-3-030-72331-6
Jouzdani, J., & Govindan, K. (2021). On the sustainable perishable food supply chain network design: A dairy products case to achieve sustainable development goals. Journal of Cleaner Production, 278, 123060. https://doi.org/10.1016/j.jclepro.2020.123060
Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: An empirical study. Supply Chain Management, 16(4), 246–259. https://doi.org/10.1108/13598541111139062
Kembro, J., Eriksson, E., & Norrman, A. (2022). Sorting out the sorting in omnichannel retailing. Journal of Business Logistics. https://doi.org/10.1111/jbl.12305
Khajavi, S. H., Partanen, J., Holmström, J., & Tuomi, J. (2015). Risk reduction in new product launch: A hybrid approach combining direct digital and tool-based manufacturing. Computers in Industry, 74, 29–42. https://doi.org/10.1016/j.compind.2015.08.008
Khalilpourazari, S., & Arshadi Khamseh, A. (2019). Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: A comprehensive study with real world application. Annals of Operations Research, 283(1), 355–393. https://doi.org/10.1007/s10479-017-2588-y
Kheirabadi, M., Naderi, B., Arshadikhamseh, A., & Roshanaei, V. (2019). A mixed-integer program and a Lagrangian-based decomposition algorithm for the supply chain network design with quantity discount and transportation modes. Expert Systems with Applications, 137, 504–516. https://doi.org/10.1016/j.eswa.2019.07.004
Kungwalsong, K., Mendoza, A., Kamath, V., Pazhani, S., & Marmolejo-Saucedo, J. A. (2022). An application of interactive fuzzy optimization model for redesigning supply chain for resilience. Annals of Operations Research, 315, 1803–1839. https://doi.org/10.1007/s10479-022-04542-5
Laporte, G., Meunier, F., & Wolfler Calvo, R. (2018). Shared mobility systems: An updated survey. Annals of Operations Research, 271(1), 105–126. https://doi.org/10.1007/s10479-018-3076-8
Laporte, G., Nickel, S., & Saldanha da Gama, F. (2019). Introduction to location science. In G. Laporte, S. Nickel, & F. Saldanha da Gama (Eds.), Location science (2nd ed., pp. 1–21). Springer. https://doi.org/10.1007/978-3-030-32177-2_1
Lee, C. (2017). A GA-based optimisation model for big data analytics supporting anticipatory shipping in retail 4.0. International Journal of Production Research, 55(2), 593–605. https://doi.org/10.1080/00207543.2016.1221162
Lin, J.-R., & Yang, T.-H. (2011). Strategic design of public bicycle sharing systems with service level constraints. Transportation Research Part E: Logistics and Transportation Review, 47(2), 284–294. https://doi.org/10.1016/j.tre.2010.09.004
Lin, Y., Jia, H., Yang, Y., Tian, G., Tao, F., & Ling, L. (2018). An improved artificial bee colony for facility location allocation problem of end-of-life vehicles recovery network. Journal of Cleaner Production, 205, 134–144. https://doi.org/10.1016/j.jclepro.2018.09.086
Liu, K., Zhou, Y., & Zhang, Z. (2010). Capacitated location model with online demand pooling in a multi-channel supply chain. European Journal of Operational Research, 207(1), 218–231. https://doi.org/10.1016/j.ejor.2010.04.029
Lohmer, J., Bugert, N., & Lasch, R. (2020). Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International Journal of Production Economics, 228, 107882. https://doi.org/10.1016/j.ijpe.2020.107882
Mahar, S., & Wright, P. D. (2017). In-store pickup and returns for a dual channel retailer. IEEE Transactions on Engineering Management, 64(4), 491–504. https://doi.org/10.1109/TEM.2017.2691466
Mahmoum Gonbadi, 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. https://doi.org/10.1016/j.jclepro.2021.129101
Mara, S. T. W., Kuo, R., & Asih, A. M. S. (2021). Location-routing problem: A classification of recent research. International Transactions in Operational Research, 28(6), 2941–2983. https://doi.org/10.1111/itor.12950
Martins, C. L., Melo, M. T., & Pato, M. V. (2019). Redesigning a food bank supply chain network in a triple bottom line context. International Journal of Production Economics, 214, 234–247. https://doi.org/10.1016/j.ijpe.2018.11.011
Mehrjerdi, Y. Z., & Shafiee, M. (2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. Journal of Cleaner Production, 289, 125141. https://doi.org/10.1016/j.jclepro.2020.125141
Melacini, M., Perotti, S., Rasini, M., & Tappia, E. (2018). E-fulfilment and distribution in omni-channel retailing: A systematic literature review. International Journal of Physical Distribution & Logistics Management, 48(4), 391–414. https://doi.org/10.1108/IJPDLM-02-2017-0101
Melnyk, S. A., Closs, D. J., Griffis, S. E., Zobel, C. W., & Macdonald, J. R. (2014). Understanding supply chain resilience. Supply Chain Management Review, 18(1), 34–41.
Melo, M. T., Nickel, S., & Saldanha da Gama, F. (2009). Facility location and supply chain management: A review. European Journal of Operational Research, 196(2), 401–412. https://doi.org/10.1016/j.ejor.2008.05.007
Melo, M. T., 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. https://doi.org/10.1016/j.ijpe.2011.11.022
Millstein, M. A., & Campbell, J. F. (2018). Total hockey optimizes omnichannel facility locations. Interfaces, 48(4), 340–356. https://doi.org/10.1287/inte.2018.0942
Millstein, M. A., Bilir, C., & Campbell, J. F. (2022). The effect of optimizing warehouse locations on omnichannel designs. European Journal of Operational Research, 301(2), 576–590. https://doi.org/10.1016/j.ejor.2021.10.061
Mishra, D., Kumar, S., & Hassini, E. (2019). Current trends in disaster management simulation modelling research. Annals of Operations Research, 283(1), 1387–1411. https://doi.org/10.1007/s10479-018-2985-x
Moheb-Alizadeh, H., Handfield, R., & Warsing, D. (2021). Efficient and sustainable closed-loop supply chain network design: A two-stage stochastic formulation with a hybrid solution methodology. Journal of Cleaner Production, 308, 127323. https://doi.org/10.1016/j.jclepro.2021.127323
Moradi, A., Razmi, J., Babazadeh, R., & Sabbaghnia, A. (2019). An integrated principal component analysis and multi-objective mathematical programming approach to agile supply chain network design under uncertainty. Journal of Industrial and Management Optimization, 15(2), 855–879. https://doi.org/10.3934/jimo.2018074
Mula, J., Poler, R., & Garcia, J. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74–97. https://doi.org/10.1016/j.fss.2005.05.045
Muren, Li, H., Mukhopadhyay, S. K., Wu, J.-J., Zhou, L., & Du, Z. (2020). Balanced maximal covering location problem and its application in bike-sharing. International Journal of Production Economics, 223, 107513. https://doi.org/10.1016/j.ijpe.2019.09.034
Naderi, M. J., Pishvaee, M. S., & Torabi, S. A. (2016). Applications of fuzzy mathematical programming approaches in supply chain planning problems. In C. Kahraman, U. Kaymak, & A. Yazici (Eds.), Fuzzy logic in its 50th year (pp. 369–402). Springer. https://doi.org/10.1007/978-3-319-31093-0_16
Nakao, H., Shen, S., & Chen, Z. (2017). Network design in scarce data environment using moment-based distributionally robust optimization. Computers & Operations Research, 88, 44–57. https://doi.org/10.1016/j.cor.2017.07.002
Ni, W., Shu, J., Song, M., Xu, D., & Zhang, K. (2021). A branch-and-price algorithm for facility location with general facility cost functions. INFORMS Journal on Computing, 33(1), 86–104. https://doi.org/10.1287/ijoc.2019.0921
Omar, I. A., Debe, M., Jayaraman, R., Salah, K., Omar, M., & Arshad, J. (2022). Blockchain-based supply chain traceability for COVID-19 personal protective equipment. Computers & Industrial Engineering, 167, 107995. https://doi.org/10.1016/j.cie.2022.107995
Omidi, S., & Fathali, J. (2022). Inverse single facility location problem on a tree with balancing on the distance of server to clients. Journal of Industrial and Management Optimization, 18(2), 1247. https://doi.org/10.3934/jimo.2021017
Ortiz-Astorquiza, C., Contreras, I., & Laporte, G. (2018). Multi-level facility location problems. European Journal of Operational Research, 267(3), 791–805. https://doi.org/10.1016/j.ejor.2017.10.019
Pariazar, M., & Sir, M. Y. (2018). A multi-objective approach for supply chain design considering disruptions impacting supply availability and quality. Computers & Industrial Engineering, 121, 113–130. https://doi.org/10.1016/j.cie.2018.05.026
Parodos, L., Tsolakis, O., Tsoukos, G., Xenou, E., & Ayfantopoulou, G. (2022). Business model analysis of smart city logistics solutions using the business model canvas: The case of an on-demand warehousing e-marketplace. Future Transportation, 2(2), 467–481. https://doi.org/10.3390/futuretransp2020026
Peng, D., Ye, C., & Wan, M. (2022). A multi-objective improved novel discrete particle swarm optimization for emergency resource center location problem. Engineering Applications of Artificial Intelligence, 111, 104725. https://doi.org/10.1016/j.engappai.2022.104725
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20. https://doi.org/10.1016/j.fss.2012.04.010
Puerto, J., Ricca, F., & Scozzari, A. (2014). Reliability problems in multiple path-shaped facility location on networks. Discrete Optimization, 12, 61–72. https://doi.org/10.1016/j.disopt.2014.01.003
Rahimi, M., Ghezavati, V., & Asadi, F. (2019). A stochastic risk-averse sustainable supply chain network design problem with quantity discount considering multiple sources of uncertainty. Computers & Industrial Engineering, 130, 430–449. https://doi.org/10.1016/j.cie.2019.02.037
Rajeev, A., Pati, R., Padhi, S., & Govindan, K. (2017). Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, 162, 299–314. https://doi.org/10.1016/j.jclepro.2017.05.026
Rizk-Allah, R. M., Abo-Sinna, M. A., & Hassanien, A. E. (2021). Intuitionistic fuzzy sets and dynamic programming for multi-objective non-linear programming problems. International Journal of Fuzzy Systems, 23(2), 334–352. https://doi.org/10.1007/s40815-020-00973-z
Rohmer, S., Gerdessen, J. C., & Claassen, G. (2019). Sustainable supply chain design in the food system with dietary considerations: A multi-objective analysis. European Journal of Operational Research, 273(3), 1149–1164. https://doi.org/10.1016/j.ejor.2018.09.006
Rozhkov, M., Ivanov, D., Blackhurst, J., & Nair, A. (2022). Adapting supply chain operations in anticipation of and during the COVID-19 pandemic. Omega, 110, 102635. https://doi.org/10.1016/j.omega.2022.102635
Sadghiani, N. S., Torabi, S., & Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95–114. https://doi.org/10.1016/j.tre.2014.12.015
Sahebjamnia, N., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of Cleaner Production, 196, 273–296. https://doi.org/10.1016/j.jclepro.2018.05.245
Sauvey, C., Melo, T., & Correia, I. (2020). Heuristics for a multi-period facility location problem with delayed demand satisfaction. Computers & Industrial Engineering, 139, 106171. https://doi.org/10.1016/j.cie.2019.106171
Sawik, T. (2022). Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study. Omega, 109, 102596. https://doi.org/10.1016/j.omega.2022.102596
Schatteman, O., Woodhouse, D., & Terino, J. (2020). Supply chain lessons from Covid-19: Time to refocus on resilience (pp. 1–12). Bain & Company.
Scholten, K., Stevenson, M., & van Donk, D. P. (2020). Dealing with the unpredictable: Supply chain resilience. International Journal of Operations & Production Management, 40, 1–10. https://doi.org/10.1108/IJOPM-01-2020-789
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. https://doi.org/10.1016/j.tre.2021.102588
Shekarian, M., & Mellat Parast, M. (2021). An integrative approach to supply chain disruption risk and resilience management: A literature review. International Journal of Logistics Research and Applications, 24(5), 427–455. https://doi.org/10.1080/13675567.2020.1763935
Sherafati, M., Bashiri, M., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2019). Supply chain network design considering sustainable development paradigm: A case study in cable industry. Journal of Cleaner Production, 234, 366–380. https://doi.org/10.1016/j.jclepro.2019.06.095
Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & Industrial Engineering, 109, 191–203. https://doi.org/10.1016/j.cie.2017.04.038
Suryawanshi, P., & Dutta, P. (2022). Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions. Transportation Research Part E: Logistics and Transportation Review, 157, 102553. https://doi.org/10.1016/j.tre.2021.102553
Taleizadeh, A. A., Ahmadzadeh, K., Sarker, B. R., & Ghavamifar, A. (2022). Designing an optimal sustainable supply chain system considering pricing decisions and resilience factors. Journal of Cleaner Production, 332, 129895. https://doi.org/10.1016/j.jclepro.2021.129895
Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488. https://doi.org/10.1016/j.ijpe.2005.12.006
Tang, C. S., & Veelenturf, L. P. (2019). The strategic role of logistics in the industry 4.0 era. Transportation Research Part E: Logistics and Transportation Review, 129, 1–11. https://doi.org/10.1016/j.tre.2019.06.004
Ting, C.-J., & Chen, C.-H. (2013). A multiple ant colony optimization algorithm for the capacitated location routing problem. International Journal of Production Economics, 141(1), 34–44. https://doi.org/10.1016/j.ijpe.2012.06.011
Tirkolaee, E., Goli, A., Ghasemi, P., & Goodarzian, F. (2022). Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms. Journal of Cleaner Production, 333, 130056. https://doi.org/10.1016/j.jclepro.2021.130056
Torabi, S., Namdar, J., Hatefi, S., & Jolai, F. (2016). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International Journal of Production Research, 54(5), 1358–1387. https://doi.org/10.1080/00207543.2015.1070215
Tosarkani, B. M., & Amin, S. H. (2018). A possibilistic solution to configure a battery closed-loop supply chain: Multi-objective approach. Expert Systems with Applications, 92, 12–26. https://doi.org/10.1016/j.eswa.2017.09.039
Tucker, E. L., Daskin, M. S., Sweet, B. V., & Hopp, W. J. (2020). Incentivizing resilient supply chain design to prevent drug shortages: Policy analysis using two- and multi-stage stochastic programs. IISE Transactions, 52(4), 394–412. https://doi.org/10.1080/24725854.2019.1646441
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. https://doi.org/10.1016/j.omega.2018.12.001
Varsei, M., & Polyakovskiy, S. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. Omega, 66, 236–247. https://doi.org/10.1016/j.omega.2015.11.009
Verhoef, P. C. (2021). Omni-channel retailing: Some reflections. Journal of Strategic Marketing, 29(7), 608–616. https://doi.org/10.1080/0965254X.2021.1892163
Wang, Y., Hong, A., Li, X., & Gao, J. (2020). Marketing innovations during a global crisis: A study of China firms’ response to COVID-19. Journal of Business Research, 116, 214–220. https://doi.org/10.1016/j.jbusres.2020.05.029
Weber, A. (1962). Theory of the location of industries. University of Chicago Press.
West, V. (2022). Truckers’ strike in Spain disrupts food industry. Reuters. Retrieved 17 March, 2022, from https://www.reuters.com/business/energy/truckers-strike-spain-disrupts-food-industry-2022-03-17/
Wieland, A. (2021). Dancing the supply chain: Toward transformative supply chain management. Journal of Supply Chain Management, 57(1), 58–73. https://doi.org/10.1111/jscm.12248
Wu, Y., Qureshi, A. G., & Yamada, T. (2022). Adaptive large neighborhood decomposition search algorithm for multi-allocation hub location routing problem. European Journal of Operational Research, 302(3), 1113–1127. https://doi.org/10.1016/j.ejor.2022.02.002
Xie, W., Jiang, Z., Zhao, Y., & Hong, J. (2014). Capacity planning and allocation with multi-channel distribution. International Journal of Production Economics, 147, 108–116. https://doi.org/10.1016/j.ijpe.2013.08.005
Xu, H., Gong, Y. Y., Chu, C., & Zhang, J. (2017). Dynamic lot-sizing models for retailers with online channels. International Journal of Production Economics, 183, 171–184. https://doi.org/10.1016/j.ijpe.2016.10.020
Yadav, V. S., Tripathi, S., & Singh, A. (2018). Bi-objective optimization for sustainable supply chain network design in omnichannel. Journal of Manufacturing Technology Management, 30(6), 972–986. https://doi.org/10.1108/JMTM-06-2017-0118
Yao, Z., Gendreau, M., Li, M., Ran, L., & Wang, Z. (2022). Service operations of electric vehicle carsharing systems from the perspectives of supply and demand: A literature review. Transportation Research Part C: Emerging Technologies, 140, 103702. https://doi.org/10.1016/j.trc.2022.103702
Yildiz, H., Yoon, J., Talluri, S., & Ho, W. (2016). Reliable supply chain network design. Decision Sciences, 47(4), 661–698. https://doi.org/10.1111/deci.12160
Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review, 103, 109–142. https://doi.org/10.1016/j.tre.2017.04.009
Zhang, Y., & Jiang, Y. (2017). Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty. Waste Management, 60, 329–339. https://doi.org/10.1016/j.wasman.2016.11.004
Zhang, S., Lee, C. K. M., Wu, K., & Choy, K. L. (2016). Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels. Expert Systems with Applications, 65, 87–99. https://doi.org/10.1016/j.eswa.2016.08.037
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Costa, Y., Melo, T. (2023). Facility Location Modeling in Supply Chain Network Design: Current State and Emerging Trends. In: Sarkis, J. (eds) The Palgrave Handbook of Supply Chain Management. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-89822-9_101-1
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