A robust location-inventory model for food supply chains operating under disruptions with ripple effects

References

• Adebanjo, D. 2009. “Understanding Demand Management Challenges in Intermediary Food Trading: A Case Study.” Supply Chain Management: An International Journal 14 (3): 224–233.
   Web of Science ®Google Scholar
• Aghezzaf, E. H., C. Sitompul, and N. M. Najid. 2010. “Models for Robust Tactical Planning in Multi-Stage Production Systems with Uncertain Demands.” Computers & Operations Research 37 (5): 880–889.
   Web of Science ®Google Scholar
• Amiri-Aref, M., W. Klibi, and M. Z. Babai. 2018. “The Multi-Sourcing Location Inventory Problem with Stochastic Demand.” European Journal of Operational Research 266 (1): 72–87.
   Web of Science ®Google Scholar
• An, K., and Y. Ouyang. 2016. “Robust Grain Supply Chain Design Considering Post-Harvest Loss and Harvest Timing Equilibrium.” Transportation Research Part E: Logistics and Transportation Review 88: 110–128.
   Web of Science ®Google Scholar
• Baghalian, A., S. Rezapour, and R. Z. Farahani. 2013. “Robust Supply Chain Network Design with Service Level Against Disruptions and Demand Uncertainties: A Real-Life Case.” European Journal of Operational Research 227 (1): 199–215.
   Web of Science ®Google Scholar
• Bertsimas, D., and M. Sim. 2003. “Robust Discrete Optimization and Network Flows.” Mathematical Programming 98 (1-3): 49–71.
   Web of Science ®Google Scholar
• Bier, T., A. Lange, and C. H. Glock. 2019. “Methods for Mitigating Disruptions in Complex Supply Chain Structures: A Systematic Literature Review.” International Journal of Production Research 58 (6): 1835–1856.
   Web of Science ®Google Scholar
• Bourlakis, M. A., and P. W. Weightman. 2008. Food Supply Chain Management. Oxford: Wiley.
   Google Scholar
• Brende, B. 2019. The Global Risks Report 2019. 14th ed. Geneva: World Economic Forum.
   Google Scholar
• Buisman, M. E., R. Haijema, and J. M. Bloemhof-Ruwaard. 2019. “Discounting and Dynamic Shelf Life to Reduce Fresh Food Waste at Retailers.” International Journal of Production Economics 209: 274–284.
   Web of Science ®Google Scholar
• Choptiany, J., B. Graub, J. Dixon, and S. Phillips. 2015. Self-evaluation and Holistic Assessment of Climate Resilience of Farmers and Pastoralists (SHARP). Rome: FAO, 155.
   Google Scholar
• Chowdhury, M. M. H., and M. Quaddus. 2016. “Supply Chain Readiness, Response and Recovery for Resilience.” Supply Chain Management: An International Journal 21 (6): 709–731.
   Web of Science ®Google Scholar
• Daskin, M. S., C. R. Coullard, and Z. J. M. Shen. 2002. “An Inventory-Location Model: Formulation, Solution Algorithm and Computational Results.” Annals of Operations Research 110 (1-4): 83–106.
   Web of Science ®Google Scholar
• Dehghani, E., M. S. Jabalameli, A. Jabbarzadeh, and M. S. Pishvaee. 2018. “Resilient Solar Photovoltaic Supply Chain Network Design Under Business-as-Usual and Hazard Uncertainties.” Computers & Chemical Engineering 111: 288–310.
   Web of Science ®Google Scholar
• Dolgui, A., D. Ivanov, and M. Rozhkov. 2019. “Does the Ripple Effect Influence the Bullwhip Effect? An Integrated Analysis of Structural and Operational Dynamics in the Supply Chain.” IFAC-PapersOnLine 51 (11): 1448–1452.
   Google Scholar
• Dolgui, A., D. Ivanov, and B. Sokolov. 2018a. “Ripple Effect in the Supply Chain: An Analysis and Recent Literature.” International Journal of Production Research 56 (1-2): 414–430.
   Web of Science ®Google Scholar
• Dolgui, A., M. K. Tiwari, Y. Sinjana, S. K. Kumar, and Y. J. Son. 2018b. “Optimising Integrated Inventory Policy for Perishable Items in a Multi-Stage Supply Chain.” International Journal of Production Research 56 (1-2): 902–925.
   Web of Science ®Google Scholar
• Esteso, A., M. M. E. Alemany, and A. Ortiz. 2018. “Conceptual Framework for Designing Agri-Food Supply Chains Under Uncertainty by Mathematical Programming Models.” International Journal of Production Research 56 (13): 4418–4446.
   Web of Science ®Google Scholar
• Fan, S., R. Pandya-Lorch, and S. Yosef, eds. 2014. Resilience for Food and Nutrition Security. Washington, DC: International Food Policy Research Institute.
   Google Scholar
• Fattahi, M., K. Govindan, and E. Keyvanshokooh. 2017. “Responsive and Resilient Supply Chain Network Design Under Operational and Disruption Risks with Delivery Lead-Time Sensitive Customers.” Transportation Research Part E: Logistics and Transportation Review 101: 176–200.
   Web of Science ®Google Scholar
• Firoozi, M., M. Z. Babai, W. Klibi, and Y. Ducq. 2020. “Distribution Planning for Multi-Echelon Networks Considering Multiple Sourcing and Lateral Transshipments.” International Journal of Production Research 58 (7): 1968–1986.
   Web of Science ®Google Scholar
• Food and Agriculture Organization of the United Nations. 2004. The State of Agricultural Commodity Markets. Rome: FAO.
   Google Scholar
• Gholami-Zanjani, S. M., M. S. Jabalameli, W. Klibi, and M. S. Pishvaee. 2020. The design of resilient food supply chain networks prone to epidemic disruptions. Working Paper 01-2019. Kedge Business School.
   Google Scholar
• Goyal, S. K., and B. C. Giri. 2001. “Recent Trends in Modeling of Deteriorating Inventory.” European Journal of Operational Research 134 (1): 1–16.
   Web of Science ®Google Scholar
• Harris, G. 2008. US identifies tainted heparin in 11 countries. New York Times, 22.
   Google Scholar
• Hasani, A., and A. Khosrojerdi. 2016. “Robust Global Supply Chain Network Design Under Disruption and Uncertainty Considering Resilience Strategies: A Parallel Memetic Algorithm for a Real-Life Case Study.” Transportation Research Part E: Logistics and Transportation Review 87: 20–52.
   Web of Science ®Google Scholar
• Hosseini, S., D. Ivanov, and A. Dolgui. 2019a. “Review of Quantitative Methods for Supply Chain Resilience Analysis.” Transportation Research Part E: Logistics and Transportation Review 125: 285–307.
   Web of Science ®Google Scholar
• Hosseini, S., D. Ivanov, and A. Dolgui. 2019b. “Ripple Effect Modelling of Supplier Disruption: Integrated Markov Chain and Dynamic Bayesian Network Approach.” International Journal of Production Research 58 (11): 3284–3303.
   Web of Science ®Google Scholar
• Ivanov, D. 2019. “Disruption Tails and Revival Policies: A Simulation Analysis of Supply Chain Design and Production-Ordering Systems in the Recovery and Post-Disruption Periods.” Computers & Industrial Engineering 127: 558–570.
   Web of Science ®Google Scholar
• Ivanov, D. 2020a. “Predicting the Impacts of Epidemic Outbreaks on Global Supply Chains: A Simulation-Based Analysis on the Coronavirus Outbreak (COVID-19/SARS-CoV-2) Case.” Transportation Research Part E: Logistics and Transportation Review 136: 101922. doi:10.1016/j.tre.2020.101922.
   PubMed Web of Science ®Google Scholar
• Ivanov, D. 2020b. “Viable Supply Chain Model: Integrating Agility, Resilience and Sustainability Perspectives—Lessons From and Thinking Beyond the COVID-19 Pandemic.” Annals of Operations Research 1. doi:10.1007/s10479-020-03640-6.
   Google Scholar
• Ivanov, D., and A. Dolgui. 2020. “Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles Towards Survivability. A Position Paper Motivated by COVID-19 Outbreak.” International Journal of Production Research 58 (10): 2904–2915.
   Web of Science ®Google Scholar
• Ivanov, D., A. Dolgui, and B. Sokolov. 2019a. “The Impact of Digital Technology and Industry 4.0 on the Ripple Effect and Supply Chain Risk Analytics.” International Journal of Production Research 57 (3): 829–846.
   Web of Science ®Google Scholar
• Ivanov, D., A. Dolgui, and B. Sokolov. 2019b. “Ripple Effect in the Supply Chain: Definitions, Frameworks and Future Research Perspectives.” In Handbook of Ripple Effects in the Supply Chain, 1–33. New York: Springer.
   Google Scholar
• Ivanov, D., R. Hartl, A. Dolgui, A. Pavlov, and B. Sokolov. 2015a. “Integration of Aggregate Distribution and Dynamic Transportation Planning in a Supply Chain with Capacity Disruptions and the Ripple Effect Consideration.” International Journal of Production Research 53 (23): 6963–6979.
   Web of Science ®Google Scholar
• Ivanov, D., A. Pavlov, A. Dolgui, D. Pavlov, and B. Sokolov. 2016. “Disruption-driven Supply Chain (re)-Planning and Performance Impact Assessment with Consideration of pro-Active and Recovery Policies.” Transportation Research Part E: Logistics and Transportation Review 90: 7–24.
   Web of Science ®Google Scholar
• Ivanov, D., B. Sokolov, and A. Dolgui. 2014. “The Ripple Effect in Supply Chains: Trade-off ‘Efficiency-Flexibility-Resilience’ in Disruption Management.” International Journal of Production Research 52 (7): 2154–2172.
   Web of Science ®Google Scholar
• Ivanov, D., B. Sokolov, I. Solovyeva, A. Dolgui, and F. Jie. 2015b. “Ripple Effect in the Time-Critical Food Supply Chains and Recovery Policies.” IFAC-PapersOnLine 48 (3): 1682–1687.
   Google Scholar
• Jabbarzadeh, A., B. Fahimnia, J. B. Sheu, and H. S. Moghadam. 2016. “Designing a Supply Chain Resilient to Major Disruptions and Supply/Demand Interruptions.” Transportation Research Part B: Methodological 94: 121–149.
   Web of Science ®Google Scholar
• Jonkman, J., A. P. Barbosa-Póvoa, and J. M. Bloemhof. 2019. “Integrating Harvesting Decisions in the Design of Agro-Food Supply Chains.” European Journal of Operational Research 276 (1): 247–258.
   Web of Science ®Google Scholar
• Kamalahmadi, M., and M. Mellat-Parast. 2016. “Developing a Resilient Supply Chain Through Supplier Flexibility and Reliability Assessment.” International Journal of Production Research 54 (1): 302–321.
   Web of Science ®Google Scholar
• Kamalahmadi, M., and M. M. Parast. 2017. “An Assessment of Supply Chain Disruption Mitigation Strategies.” International Journal of Production Economics 184: 210–230.
   Web of Science ®Google Scholar
• Kaya, O., and B. Urek. 2016. “A Mixed Integer Nonlinear Programming Model and Heuristic Solutions for Location, Inventory and Pricing Decisions in a Closed Loop Supply Chain.” Computers & Operations Research 65: 93–103.
   Web of Science ®Google Scholar
• Khor, C. S., A. Elkamel, K. Ponnambalam, and P. L. Douglas. 2008. “Two-stage Stochastic Programming with Fixed Recourse via Scenario Planning with Economic and Operational Risk Management for Petroleum Refinery Planning Under Uncertainty.” Chemical Engineering and Processing: Process Intensification 47 (9-10): 1744–1764.
   Web of Science ®Google Scholar
• Kinra, A., D. Ivanov, A. Das, and A. Dolgui. 2019. “Ripple Effect Quantification by Supplier Risk Exposure Assessment.” International Journal of Production Research 58 (18): 5559–5578.
   Web of Science ®Google Scholar
• Klibi, W., and A. Martel. 2012a. “Modelling Approaches for the Design of Resilient Supply Networks Under Disruptions.” International Journal of Production Economics 135 (2): 882–898.
   Web of Science ®Google Scholar
• Klibi, W., and A. Martel. 2012b. “Scenario-based Supply Chain Network Risk Modeling.” European Journal of Operational Research 223 (3): 644–658.
   Web of Science ®Google Scholar
• Klibi, W., A. Martel, and A. Guitouni. 2010. “The Design of Robust Value-Creating Supply Chain Networks: A Critical Review.” European Journal of Operational Research 203 (2): 283–293.
   Web of Science ®Google Scholar
• Klibi, W., J. B. Rice, and L. Urciuoli. 2018. “Special Dossier: Quantifying Supply Chain Resilience.” Supply Chain Forum: An International Journal 19 (4): 253–254.
   Google Scholar
• KPMG. 2016. Competitive Alternatives: KPMG’s Guide to International Business Location. Quebec: KPMG LLP.
   Google Scholar
• Kröger, W., and E. Zio. 2011. Vulnerable Systems. London: Springer Science & Business Media.
   Google Scholar
• Le Hoa Vo, T., and D. Thiel. 2011. “Economic Simulation of a Poultry Supply Chain Facing a Sanitary Crisis.” British Food Journal 113 (8): 1011–1030.
   Web of Science ®Google Scholar
• Liu, Y., E. Dehghani, M. S. Jabalameli, A. Diabat, and C. C. Lu. 2020. “A Coordinated Location-Inventory Problem with Supply Disruptions: A two-Phase Queuing Theory–Optimization Model Approach.” Computers & Industrial Engineering 142: 106326. doi:10.1016/j.cie.2020.106326.
   Web of Science ®Google Scholar
• Lütke Entrup, M., H. O. Günther, P. Van Beek, M. Grunow, and T. Seiler. 2005. “Mixed-integer Linear Programming Approaches to Shelf-Life-Integrated Planning and Scheduling in Yoghurt Production.” International Journal of Production Research 43 (23): 5071–5100.
   Web of Science ®Google Scholar
• Minner, S., and S. Transchel. 2010. “Periodic Review Inventory-Control for Perishable Products Under Service-Level Constraints.” OR Spectrum 32 (4): 979–996.
   Web of Science ®Google Scholar
• Miranda, P. A., and R. A. Garrido. 2006. “A Simultaneous Inventory Control and Facility Location Model with Stochastic Capacity Constraints.” Networks and Spatial Economics 6 (1): 39–53.
   Web of Science ®Google Scholar
• Mohammed, A., and Q. Wang. 2017. “The Fuzzy Multi-Objective Distribution Planner for a Green Meat Supply Chain.” International Journal of Production Economics 184: 47–58.
   Web of Science ®Google Scholar
• Mohebalizadehgashti, F., H. Zolfagharinia, and S. H. Amin. 2020. “Designing a Green Meat Supply Chain Network: A Multi-Objective Approach.” International Journal of Production Economics 219: 312–327.
   Web of Science ®Google Scholar
• Mulvey, J. M., R. J. Vanderbei, and S. A. Zenios. 1995. “Robust Optimization of Large-Scale Systems.” Operations Research 43 (2): 264–281.
   Web of Science ®Google Scholar
• Nooraie, S. V., and M. M. Parast. 2016. “Mitigating Supply Chain Disruptions Through the Assessment of Trade-Offs among Risks, Costs and Investments in Capabilities.” International Journal of Production Economics 171: 8–21.
   Web of Science ®Google Scholar
• Pariazar, M., S. Root, and M. Y. Sir. 2017. “Supply Chain Design Considering Correlated Failures and Inspection in Pharmaceutical and Food Supply Chains.” Computers & Industrial Engineering 111: 123–138.
   Web of Science ®Google Scholar
• Pavlov, A., D. Ivanov, F. Werner, A. Dolgui, and B. Sokolov. 2019. “Integrated Detection of Disruption Scenarios, the Ripple Effect Dispersal and Recovery Paths in Supply Chains.” Annals of Operations Research 1–23. doi:10.1007/s10479-019-03454-1.
   Web of Science ®Google Scholar
• Qin, X., X. Liu, and L. Tang. 2013. “A two-Stage Stochastic Mixed-Integer Program for the Capacitated Logistics Fortification Planning Under Accidental Disruptions.” Computers & Industrial Engineering 65 (4): 614–623.
   Web of Science ®Google Scholar
• Raafat, F. 1991. “Survey of Literature on Continuously Deteriorating Inventory Models.” Journal of the Operational Research Society 42 (1): 27–37.
   Web of Science ®Google Scholar
• Rezapour, S., R. Z. Farahani, and M. Pourakbar. 2017. “Resilient Supply Chain Network Design Under Competition: A Case Study.” European Journal of Operational Research 259 (3): 1017–1035.
   Web of Science ®Google Scholar
• Rice, J. B., and F. Caniato. 2003. “Building a Secure and Resilient Supply Network.” Supply Chain Management Review 7 (5): 22–30.
   Google Scholar
• Sadghiani, N. S., S. A. Torabi, and N. Sahebjamnia. 2015. “Retail Supply Chain Network Design Under Operational and Disruption Risks.” Transportation Research Part E: Logistics and Transportation Review 75: 95–114.
   Web of Science ®Google Scholar
• Saha, A. K., A. Paul, A. Azeem, and S. K. Paul. 2020. “Mitigating Partial-Disruption Risk: A Joint Facility Location and Inventory Model Considering Customers’ Preferences and the Role of Substitute Products and Backorder Offers.” Computers & Operations Research 117: 104884.
   Web of Science ®Google Scholar
• Shahabi, M., A. Tafreshian, A. Unnikrishnan, and S. D. Boyles. 2018. “Joint Production–Inventory–Location Problem with Multi-Variate Normal Demand.” Transportation Research Part B: Methodological 110: 60–78.
   Web of Science ®Google Scholar
• Sheffi, Y. 2005. The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage. Cambridge, MA: MIT Press.
   Google Scholar
• Sheth, A. N., M. Hoekstra, N. Patel, G. Ewald, C. Lord, C. Clarke, and D. Zink. 2011. “A National Outbreak of Salmonella Serotype Tennessee Infections From Contaminated Peanut Butter: A new Food Vehicle for Salmonellosis in the United States.” Clinical Infectious Diseases 53 (4): 356–362.
   PubMed Web of Science ®Google Scholar
• Sokolov, B., D. Ivanov, A. Dolgui, and A. Pavlov. 2016. “Structural Quantification of the Ripple Effect in the Supply Chain.” International Journal of Production Research 54 (1): 152–169.
   Web of Science ®Google Scholar
• Soysal, M., J. M. Bloemhof-Ruwaard, and J. G. A. J. Van der Vorst. 2014. “Modelling Food Logistics Networks with Emission Considerations: The Case of an International Beef Supply Chain.” International Journal of Production Economics 152: 57–70.
   Web of Science ®Google Scholar
• Stone, J., and S. Rahimifard. 2018. “Resilience in Agri-Food Supply Chains: A Critical Analysis of the Literature and Synthesis of a Novel Framework.” Supply Chain Management: An International Journal 23 (3): 207–238.
   Web of Science ®Google Scholar
• Tassier, T. 2005. SIR model of epidemics. Annual Report. http://www.ma.utexas.edu/users/%20mks/RA/SIR.pdf.
   Google Scholar
• Tendall, D. M., J. Joerin, B. Kopainsky, P. Edwards, A. Shreck, Q. B. Le, … J. Six. 2015. “Food System Resilience: Defining the Concept.” Global Food Security 6: 17–23.
   Google Scholar
• Tirkolaee, E. B., I. Mahdavi, M. M. S. Esfahani, and G. W. Weber. 2020. “A Robust Green Location-Allocation-Inventory Problem to Design an Urban Waste Management System Under Uncertainty.” Waste Management 102: 340–350.
   PubMed Web of Science ®Google Scholar
• Tukamuhabwa, B. R., M. Stevenson, J. Busby, and M. Zorzini. 2015. “Supply Chain Resilience: Definition, Review and Theoretical Foundations for Further Study.” International Journal of Production Research 53 (18): 5592–5623.
   Web of Science ®Google Scholar
• Vlajic, J. V., J. G. Van der Vorst, and R. Haijema. 2012. “A Framework for Designing Robust Food Supply Chains.” International Journal of Production Economics 137 (1): 176–189.
   Web of Science ®Google Scholar
• Wang, X., and D. Li. 2012. “A Dynamic Product Quality Evaluation Based Pricing Model for Perishable Food Supply Chains.” Omega 40 (6): 906–917.
   Web of Science ®Google Scholar
• Wang, M., L. Zhao, and M. Herty. 2019. “Joint Replenishment and Carbon Trading in Fresh Food Supply Chains.” European Journal of Operational Research 277 (2): 561–573.
   Web of Science ®Google Scholar
• Wassener, B. 2010. Chinese heparin supplier posts strong trading debut. New York Times. http://www.nytimes.com/2010/05/07/business/global/07drug.html.
   Google Scholar
• Xie, S., K. An, and Y. Ouyang. 2019. “Planning Facility Location Under Generally Correlated Facility Disruptions: Use of Supporting Stations and Quasi-Probabilities.” Transportation Research Part B: Methodological 122: 115–139.
   Web of Science ®Google Scholar
• Zahiri, B., J. Zhuang, and M. Mohammadi. 2017. “Toward an Integrated Sustainable-Resilient Supply Chain: A Pharmaceutical Case Study.” Transportation Research Part E: Logistics and Transportation Review 103: 109–142.
   Web of Science ®Google Scholar
• Zhang, Y., F. Chu, A. Che, Y. Yu, and X. Feng. 2019. “Novel Model and Kernel Search Heuristic for Multi-Period Closed-Loop Food Supply Chain Planning with Returnable Transport Items.” International Journal of Production Research 57 (23): 7439–7456.
   Web of Science ®Google Scholar
• Zhang, S., and H. Song. 2018. “Production and Distribution Planning in Danone Waters China Division.” INFORMS Journal on Applied Analytics 48 (6): 578–590.
   Google Scholar
• Zhao, S., and F. You. 2019. “Resilient Supply Chain Design and Operations with Decision-Dependent Uncertainty Using a Data-Driven Robust Optimization Approach.” AIChE Journal 65 (3): 1006–1021.
   Web of Science ®Google Scholar