Abstract
Today, supply chains are affected by various uncertainties and disruptions. Among the unexpected disruptions, the widespread of COVID-19 pandemic has adversely influenced supply chains (SC) worldwide and is a reminder of the importance of resilience in supply chain networks. In this research, the goal is to present a mathematical programming model for designing a resilient food supply chain that can withstand disruptions caused by pandemics and their ripple effects. A hybrid robust-stochastic optimization approach is proposed to handle random as well as deep uncertainties, and three resilience strategies are applied to make the model resilient. Several numerical examples are generated to validate the presented model and derive practical insights. The method consistently demonstrated a reduced optimality gap, showing an average improvement of \(37{\text{\% }}\) compared to the nominal approach. Resilient strategies, particularly outsourcing, consistently resulted in a substantial average cost reduction of \(25{\text{\% }}\). The combined use of all three strategies indicated a remarkable average cost reduction of up to \(52{\text{\% }}\).
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References
Abbasi S, Saboury A, Jabalameli MS (2021) Reliable supply chain network design for 3PL providers using consolidation hubs under disruption risks considering product perishability: an application to a pharmaceutical distribution network. Comput Ind Eng 152:107019
Alkaabneh F, Diabat A, Gao HO (2020) Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. Comput Oper Res 113:104751
Amorim P et al (2013) Managing perishability in production-distribution planning: a discussion and review. Flex Serv Manuf J 25:389–413
Arabi M, Gholamian MR (2023) Resilient closed-loop supply chain network design considering quality uncertainty: a case study of stone quarries. Resour Policy 80:103290
Arabsheybani A, Khasmeh AA (2021) Robust and resilient supply chain network design considering risks in food industry: flavour industry in Iran. Int J Manag Sci Eng Manag 16(3):197–208
Asgharizadeh E et al (2023) Modeling the supply chain network in the fast-moving consumer goods industry during COVID-19 pandemic. Oper Res 23(1):14
Azad N et al (2013) Strategies for protecting supply chain networks against facility and transportation disruptions: an improved benders decomposition approach. Ann Oper Res 210:125–163
Badejo O, Ierapetritou M (2022) Mathematical programming approach to optimize tactical and operational supply chain decisions under disruptions. Ind Eng Chem Res 61(45):16747–16763
Badejo O, Ierapetritou M (2023) A mathematical modeling approach for supply chain management under disruption and operational uncertainty. AIChE J 69(4):e18037
Baghalian A, Rezapour S, Farahani RZ (2013) Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur J Oper Res 227(1):199–215
Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53
Bezdek JC (1973) Fuzzy-mathematics in pattern classification. Cornell University
Bottani E et al (2019) Resilient food supply chain design: modelling framework and metaheuristic solution approach. Comput Ind Eng 135:177–198
Bourlakis MA, Weightman PWH (eds) (2008) Food supply chain management. Wiley
Cavalcante IM et al (2019) A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. Int J Inf Manag 49:86–97
Cheramin M et al (2021) Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: stochastic programming and Benders decomposition. Transp Res Part E Logist Transp Rev 155:102505
Chowdhury P et al (2021) COVID-19 pandemic related supply chain studies: a systematic review. Transp Res Part E Logist Transp Rev 148:102271
Chowdhury MT et al (2022) A case study on strategies to deal with the impacts of COVID-19 pandemic in the food and beverage industry. Oper Manag Res 15(1):166–178
Devika K, Jafarian A, Nourbakhsh V (2014) Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. Eur J Oper Res 235(3):594–615
Diabat A, Jabbarzadeh A, Khosrojerdi A (2019) A perishable product supply chain network design problem with reliability and disruption considerations. Int J Prod Econ 212:125–138
Dolgui A, Ivanov D (2020) Exploring supply chain structural dynamics: new disruptive technologies and disruption risks. Int J Prod Econ 229:107886
Dolgui A, Ivanov D (2021) Ripple effect and supply chain disruption management: new trends and research directions. Int J Prod Res 59(1):102–109
Esteso A et al (2023) System dynamics model for improving the robustness of a fresh agri-food supply chain to disruptions. Oper Res 23(2):28
Fattahi M, Govindan K, Keyvanshokooh E (2017) Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transp Res Part E Logist Transp Rev 101:176–200
Foroozesh N, Karimi B, Mousavi SM (2022) Green-resilient supply chain network design for perishable products considering route risk and horizontal collaboration under robust interval-valued type-2 fuzzy uncertainty: a case study in food industry. J Environ Manag 307:114470
Fu J, Yuanlue Fu (2015) An adaptive multi-agent system for cost collaborative management in supply chains. Eng Appl Artif Intell 44:91–100
Ghezelhesar AJ, Bozorgi-Amiri A (2022) A novel approach to selection of resilient measures portfolio under disruption and uncertainty: a case study of e-payment service providers. Oper Res 22(5):5477–5527
Gholami-Zanjani SM et al (2021a) A robust location-inventory model for food supply chains operating under disruptions with ripple effects. Int J Prod Res 59(1):301–324
Gholami-Zanjani SM et al (2021b) The design of resilient food supply chain networks prone to epidemic disruptions. Int J Prod Econ 233:108001
Goh M, Lim JYS, Meng F (2007) A stochastic model for risk management in global supply chain networks. Eur J Oper Res 182(1):164–173
Govindan K, Fattahi M, Keyvanshokooh E (2017) Supply chain network design under uncertainty: a comprehensive review and future research directions. Eur J Oper Res 263(1):108–141
Hasani A, Khosrojerdi A (2016) Robust global supply chain network design under disruption and uncertainty considering resilience strategies: a parallel memetic algorithm for a real-life case study. Transp Res Part E Logist Transp Rev 87:20–52
He J et al (2019) A real-option approach to mitigate disruption risk in the supply chain. Omega 88:133–149
Hosseini S, Ivanov D, Dolgui A (2019) Review of quantitative methods for supply chain resilience analysis. Transp Res Part E Logist Transp Rev 125:285–307
Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp Res Part E Logist Transp Rev 136:101922
Ivanov D (2021) Introduction to supply chain resilience: management, modelling, technology. Springer
Ivanov D (2022) Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann Oper Res 319(1):1411–1431
Ivanov D, Dolgui A (2020) Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int J Prod Res 58(10):2904–2915
Ivanov D, Dolgui A, Sokolov B (2019a) Ripple effect in the supply chain: definitions, frameworks and future research perspectives. In: Handbook of ripple effects in the supply chain, pp 1–33
Ivanov D, Dolgui A, Sokolov B (eds) (2019b) Handbook of ripple effects in the supply chain, vol 276. Springer, New York
Jabbarzadeh A et al (2016) Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp Res Part B Methodol 94:121–149
Jabbarzadeh A, Fahimnia B, Sabouhi F (2018) Resilient and sustainable supply chain design: sustainability analysis under disruption risks. Int J Prod Res 56(17):5945–5968
Karacaoglu G, Krawczyk JB (2021) Public policy, systemic resilience and viability theory. Metroeconomica 72(4):826–848
Kazemian I et al (2022) A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators. Oper Res 22(3):1853–1883
Khalili SM, Jolai F, Torabi SA (2017) Integrated production–distribution planning in two-echelon systems: a resilience view. Int J Prod Res 55(4):1040–1064
Klibi W, Martel A (2012a) Modeling approaches for the design of resilient supply networks under disruptions. Int J Prod Econ 135(2):882–898
Klibi W, Martel A (2012b) Scenario-based supply chain network risk modeling. Eur J Oper Res 223(3):644–658
Li Q, Zeng Bo, Savachkin A (2013) Reliable facility location design under disruptions. Comput Oper Res 40(4):901–909
Liu M et al (2021) A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect. Int J Prod Res 59(1):265–285
Melo MT, Nickel S, Saldanha-Da-Gama F (2009) Facility location and supply chain management—a review. Eur J Oper Res 196(2):401–412
Mishra D et al (2021) Evolution of supply chain ripple effect: a bibliometric and meta-analytic view of the constructs. Int J Prod Res 59(1):129–147
Mohan S, Gopalakrishnan M, Mizzi PJ (2013) Improving the efficiency of a non-profit supply chain for the food insecure. Int J Prod Econ 143(2):248–255
Namdar J et al (2018) Supply chain resilience for single and multiple sourcing in the presence of disruption risks. Int J Prod Res 56(6):2339–2360
Nooraie SV, Parast MM (2016) Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities. Int J Prod Econ 171:8–21
Paul SK, Chowdhury P (2020) Strategies for managing the impacts of disruptions during COVID-19: an example of toilet paper. Glob J Flex Syst Manag 21:283–293
Paul SK, Chowdhury P (2021) A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19. Int J Phys Distrib Logist Manag 51(2):104–125
Paul SK et al (2023) A recovery planning model for online business operations under the COVID-19 outbreak. Int J Prod Res 61(8):2613–2635
Peng P et al (2011) Reliable logistics networks design with facility disruptions. Transp Res Part B Methodol 45(8):1190–1211
Ponomarov S (2012) Antecedents and consequences of supply chain resilience: a dynamic capabilities perspective. PhD diss., University of Tennessee. https://trace.tennessee.edu/utk_graddiss/1338
Qi L, Shen Z-JM, Snyder LV (2010) The effect of supply disruptions on supply chain design decisions. Transp Sci 44(2):274–289
Qin X, Liu X, Tang L (2013) A two-stage stochastic mixed-integer program for the capacitated logistics fortification planning under accidental disruptions. Comput Ind Eng 65(4):614–623
Rahman T et al (2021) An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic. Comput Ind Eng 158:107401
Rezapour S, Farahani RZ, Pourakbar M (2017) Resilient supply chain network design under competition: a case study. Eur J Oper Res 259(3):1017–1035
Sabouhi F, Pishvaee MS, Jabalameli MS (2018) Resilient supply chain design under operational and disruption risks considering quantity discount: a case study of pharmaceutical supply chain. Comput Ind Eng 126:657–672
Sabouhi F et al (2020) A multi-cut L-shaped method for resilient and responsive supply chain network design. Int J Prod Res 58(24):7353–7381
Sabouhi F, Jabalameli MS, Jabbarzadeh A (2021) An optimization approach for sustainable and resilient supply chain design with regional considerations. Comput Ind Eng 159:107510
Sawik T (2013) Selection of resilient supply portfolio under disruption risks. Omega 41(2):259–269
Sawik T (2020) A two-period model for selection of resilient multi-tier supply portfolio. Int J Prod Res 58(19):6043–6060
Sawik T (2021) On the risk-averse selection of resilient multi-tier supply portfolio. Omega 101:102267
Sawik T (2022) Stochastic optimization of supply chain resilience under ripple effect: a COVID-19 pandemic related study. Omega 109:102596
Sawik T, Sawik B (2023) Risk-averse decision-making to maintain supply chain viability under propagated disruptions. Int J Prod Res 62:1–15
Sazvar Z et al (2021) A capacity planning approach for sustainable-resilient supply chain network design under uncertainty: a case study of vaccine supply chain. Comput Ind Eng 159:107406
Singh AR et al (2012) Design of global supply chain network with operational risks. Int J Adv Manuf Technol 60:273–290
Snyder LV, Shen Z-JM (2019) Fundamentals of supply chain theory. Wiley
Stone J, Rahimifard S (2018) Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework. Supply Chain Manag Int J 23:207–238
Taleizadeh AA, Ghavamifar A, Khosrojerdi A (2022) Resilient network design of two supply chains under price competition: game theoretic and decomposition algorithm approach. Oper Res 22:1–33
Tendall DM et al (2015) Food system resilience: defining the concept. Glob Food Secur 6:17–23
Torabi SA et al (2016) An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. Int J Prod Res 54(5):1358–1387
Vali-Siar MM, Roghanian E (2020) Resilient mixed supply chain network redesign under operational and disruption risks: a case study. J Ind Eng Res Prod Syst 8(16):113–135
Vali-Siar MM, Roghanian E (2022a) Designing a multi-period and multi-product resilient mixed supply chain network under chain-to-chain competition. Kybernetes 53:935–959
Vali-Siar MM, Roghanian E (2022b) Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption. Sustain Prod Consum 30:278–300
Vali-Siar MM, Roghanian E, Jabbarzadeh A (2022) Resilient mixed open and closed-loop supply chain network design under operational and disruption risks considering competition: a case study. Comput Ind Eng 172:108513
Vlajic JV, Van der Vorst JGAJ, Haijema R (2012) A framework for designing robust food supply chains. Int J Prod Econ 137(1):176–189
Yavari M, Zaker H (2019) An integrated two-layer network model for designing a resilient green-closed loop supply chain of perishable products under disruption. J Clean Prod 230:198–218
Yavari M, Zaker H (2020) Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Comput Chem Eng 134:106680
Ye Y et al (2019) Integrated redundancy and storage design optimization for reliable air separation units based on Markov chain—a game theoretic solution. Ind Eng Chem Res 59(6):2491–2504
Yousefi-Babadi A et al (2017) Designing a reliable multi-objective queuing model of a petrochemical supply chain network under uncertainty: a case study. Comput Chem Eng 100:177–197
Zahiri B, Zhuang J, Mohammadi M (2017) Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp Res Part E Logist Transp Rev 103:109–142
Zhao S, You F (2019) Resilient supply chain design and operations with decision-dependent uncertainty using a data-driven robust optimization approach. AIChE J 65(3):1006–1021
Zokaee S et al (2017) Robust supply chain network design: an optimization model with real world application. Ann Oper Res 257:15–44
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Shekarabi, H., Vali-Siar, M.M. & Mozdgir, A. Food supply chain network design under uncertainty and pandemic disruption. Oper Res Int J 24, 26 (2024). https://doi.org/10.1007/s12351-024-00832-x
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DOI: https://doi.org/10.1007/s12351-024-00832-x