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Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic

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  • Vahdani, Behnam
  • Mohammadi, Mehrdad
  • Thevenin, Simon
  • Meyer, Patrick
  • Dolgui, Alexandre

Abstract

The COVID-19 virus’s high transmissibility has resulted in the virus’s rapid spread throughout the world, which has brought several repercussions, ranging from a lack of sanitary and medical products to the collapse of medical systems. Hence, governments attempt to re-plan the production of medical products and reallocate limited health resources to combat the pandemic. This paper addresses a multi-period production-inventory-sharing problem (PISP) to overcome such a circumstance, considering two consumable and reusable products. We introduce a new formulation to decide on production, inventory, delivery, and sharing quantities. The sharing will depend on net supply balance, allowable demand overload, unmet demand, and the reuse cycle of reusable products. Undeniably, the dynamic demand for products during pandemic situations must be reflected effectively in addressing the multi-period PISP. A bespoke compartmental susceptible-exposed-infectious-hospitalized-recovered-susceptible (SEIHRS) epidemiological model with a control policy is proposed, which also accounts for the influence of people’s behavioral response as a result of the knowledge of adequate precautions. An accelerated Benders decomposition-based algorithm with tailored valid inequalities is offered to solve the model. Finally, we consider a realistic case study – the COVID-19 pandemic in France – to examine the computational proficiency of the decomposition method. The computational results reveal that the proposed decomposition method coupled with effective valid inequalities can solve large-sized test problems in a reasonable computational time and 9.88 times faster than the commercial Gurobi solver. Moreover, the sharing mechanism reduces the total cost of the system and the unmet demand on the average up to 32.98% and 20.96%, respectively.

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  • Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Meyer, Patrick & Dolgui, Alexandre, 2023. "Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic," Omega, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:jomega:v:120:y:2023:i:c:s0305048323000737
    DOI: 10.1016/j.omega.2023.102909
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    1. Boucekkine Raouf & Hritonenko Natali & Yatsenko Yuri, 2013. "On the Optimal Control of the Vintage Capital Growth Model with Endogenous Labour Supply," Mathematical Economics Letters, De Gruyter, vol. 1(1), pages 3-7, October.
    2. Pachkova, Elena V., 2009. "Restricted reallocation of resources," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1049-1057, August.
    3. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Ivanov, Dmitry & Keskin, Burcu B., 2023. "Post-pandemic adaptation and development of supply chain viability theory," Omega, Elsevier, vol. 116(C).
    5. Goenka, Aditya & Liu, Lin & Nguyen, Manh-Hung, 2021. "SIR economic epidemiological models with disease induced mortality," Journal of Mathematical Economics, Elsevier, vol. 93(C).
    6. Bagal, Dilip Kumar & Rath, Arati & Barua, Abhishek & Patnaik, Dulu, 2020. "Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Davide Torre & Danilo Liuzzi & Rosario Maggistro & Simone Marsiglio, 2022. "Mobility Choices and Strategic Interactions in a Two-Group Macroeconomic–Epidemiological Model," Dynamic Games and Applications, Springer, vol. 12(1), pages 110-132, March.
    8. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    9. Jeroen Struben & Brandon H. Lee & Christopher B. Bingham, 2020. "Collective Action Problems and Resource Allocation During Market Formation," Post-Print hal-02927584, HAL.
    10. Jeroen Struben & Brandon H. Lee & Christopher B. Bingham, 2020. "Collective Action Problems and Resource Allocation During Market Formation," Strategy Science, INFORMS, vol. 5(3), pages 245-270, September.
    11. Hui Zhao & Vinayak Deshpande & Jennifer K. Ryan, 2005. "Inventory Sharing and Rationing in Decentralized Dealer Networks," Management Science, INFORMS, vol. 51(4), pages 531-547, April.
    12. Alexandre Dolgui & Dmitry Ivanov & Suresh P. Sethi & Boris Sokolov, 2019. "Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications," International Journal of Production Research, Taylor & Francis Journals, vol. 57(2), pages 411-432, January.
    13. Raouf Boucekkine & Carmen Camacho & Giorgio Fabbri, 2013. "On the Optimal Control of Some Parabolic Partial Differential Equations Arising in Economics," AMSE Working Papers 1334, Aix-Marseille School of Economics, France, revised 05 Jun 2013.
    14. Aditya Goenka & Lin Liu & Nguyen, Manh-Hung, 2020. "Modeling optimal quarantines under infectious disease related mortality," Discussion Papers 20-24, Department of Economics, University of Birmingham.
    15. Bruno Albert Neumann-Saavedra & Teodor Gabriel Crainic & Bernard Gendron & Dirk Christian Mattfeld & Michael Römer, 2020. "Integrating Resource Management in Service Network Design for Bike-Sharing Systems," Transportation Science, INFORMS, vol. 54(5), pages 1251-1271, September.
    16. Xavier Brusset & Aida Jebali & Davide La Torre, 2023. "Production optimisation in a pandemic context," International Journal of Production Research, Taylor & Francis Journals, vol. 61(5), pages 1642-1663, March.
    17. Frank Karsten & Marco Slikker & Geert-Jan van Houtum, 2015. "Resource Pooling and Cost Allocation Among Independent Service Providers," Operations Research, INFORMS, vol. 63(2), pages 476-488, April.
    18. Sanjay Mehrotra & Hamed Rahimian & Masoud Barah & Fengqiao Luo & Karolina Schantz, 2020. "A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 303-320, August.
    19. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    20. Yiqun Ma & Sen Pei & Jeffrey Shaman & Robert Dubrow & Kai Chen, 2021. "Role of meteorological factors in the transmission of SARS-CoV-2 in the United States," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    21. Saedinia, R. & Vahdani, Behnam & Etebari, F. & Afshar Nadjafi, B., 2019. "Robust gasoline closed loop supply chain design with redistricting, service sharing and intra-district service transfer," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 121-141.
    22. Edward H. Kaplan, 2020. "OM Forum—COVID-19 Scratch Models to Support Local Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 22(4), pages 645-655, July.
    23. Xiaowei Chen & Jing Li & Chen Xiao & Peilin Yang, 2021. "Numerical solution and parameter estimation for uncertain SIR model with application to COVID-19," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 189-208, June.
    24. Dmitry Ivanov & Alexandre Dolgui & Jennifer V. Blackhurst & Tsan-Ming Choi, 2023. "Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems," International Journal of Production Research, Taylor & Francis Journals, vol. 61(8), pages 2402-2415, April.
    25. Gary Gereffi, 2020. "What does the COVID-19 pandemic teach us about global value chains? The case of medical supplies," Journal of International Business Policy, Palgrave Macmillan, vol. 3(3), pages 287-301, September.
    26. Dmitry Ivanov & Alexandre Dolgui, 2022. "The shortage economy and its implications for supply chain and operations management," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7141-7154, December.
    27. Elisa F. Long & Eike Nohdurft & Stefan Spinler, 2018. "Spatial Resource Allocation for Emerging Epidemics: A Comparison of Greedy, Myopic, and Dynamic Policies," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 181-198, May.
    28. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
    29. Ali Ekici & Pınar Keskinocak & Julie L. Swann, 2014. "Modeling Influenza Pandemic and Planning Food Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 11-27, February.
    30. Stephanie Carew & Mahesh Nagarajan & Steven Shechter & Jugpal Arneja & Erik Skarsgard, 2021. "Dynamic Capacity Allocation for Elective Surgeries: Reducing Urgency-Weighted Wait Times," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 407-424, March.
    31. Dmitry Ivanov & Alexandre 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, Taylor & Francis Journals, vol. 58(10), pages 2904-2915, May.
    32. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Gendreau, Michel & Dolgui, Alexandre & Meyer, Patrick, 2023. "Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1249-1272.
    33. Fouad El Ouardighi & Eugene Khmelnitsky & Suresh P. Sethi, 2022. "Epidemic control with endogenous treatment capability under popular discontent and social fatigue," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1734-1752, April.
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