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Modeling the supply chain network in the fast-moving consumer goods industry during COVID-19 pandemic

Author

Listed:
  • Ezzatollah Asgharizadeh

    (University of Tehran)

  • Amir Daneshvar

    (Islamic Azad University)

  • Mahdi Homayounfar

    (Islamic Azad University)

  • Fariba Salahi

    (Islamic Azad University)

  • Mohsen Amini Khouzani

    (Islamic Azad University)

Abstract

The outbreak of the COVID-19 pandemic in recent years has raised serious concerns about the distribution of fast-moving consumer goods products, given the freshness of their use. On the one hand, the distribution of fast-moving consumer goods with multiple vehicles has led to maintaining the freshness of items at the supply chain level, and on the other hand, it involves the high costs of using vehicles. Congestion of vehicles and drivers in the distribution of items has also increased the possibility of COVID-19 transmission. The importance of the above issue has led to the modeling of a multi-level supply chain problem in the FMCG industry by considering the freshness of items to reduce COVID-19 transmission. The most important issue considered in this article is to send fresh food in the shortest possible time to customers who cannot go to stores and wait in line to buy items in the conditions of Covid-19. Therefore, the designed model provides the possibility for customers to receive fresh food in addition to reducing costs and also reduce the possibility of contracting Covid-19. Designed supply chain network levels include suppliers of raw materials, manufacturers of consumer goods, distributors and end customers. In order to optimize the objectives of the problem, including minimizing the total costs of supply chain network design and maximizing the freshness of items, various strategic and tactical decisions such as locating potential facilities, routing vehicles, and optimally allocating the flow of goods should be made. Since the supply chain network model is considered to be NP-hard, meta-heuristic algorithms have been used to solve the problem by providing a modified priority-based encoding. The results show the high efficiency of the proposed solution method in a short time.

Suggested Citation

  • Ezzatollah Asgharizadeh & Amir Daneshvar & Mahdi Homayounfar & Fariba Salahi & Mohsen Amini Khouzani, 2023. "Modeling the supply chain network in the fast-moving consumer goods industry during COVID-19 pandemic," Operational Research, Springer, vol. 23(1), pages 1-32, March.
    • Handle: RePEc:spr:operea:v:23:y:2023:i:1:d:10.1007_s12351-023-00757-x
    DOI: 10.1007/s12351-023-00757-x
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    References listed on IDEAS

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    1. Javid Ghahremani-Nahr & Ramez Kian & Ehsan Sabet & Vahid Akbari, 2022. "A bi-objective blood supply chain model under uncertain donation, demand, capacity and cost: a robust possibilistic-necessity approach," Operational Research, Springer, vol. 22(5), pages 4685-4723, November.
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    4. Sanjay Sharma & Amit Singh Sengar, 2012. "Inventory minimisation by service level optimisation for increased freshness and availability to end consumer in a multi echelon system: FMCG case," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 4(2), pages 165-188.
    5. Pilar González-Torre & Jorge Coque, 2016. "How is a food bank managed? Different profiles in Spain," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 33(1), pages 89-100, March.
    6. Agnieszka Szmelter-Jarosz & Javid Ghahremani-Nahr & Hamed Nozari, 2021. "A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19," JRFM, MDPI, vol. 14(11), pages 1-22, November.
    7. Davis, Lauren B. & Sengul, Irem & Ivy, Julie S. & Brock, Luther G. & Miles, Lastella, 2014. "Scheduling food bank collections and deliveries to ensure food safety and improve access," Socio-Economic Planning Sciences, Elsevier, vol. 48(3), pages 175-188.
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    Cited by:

    1. Hanieh Shekarabi & Mohammad Mahdi Vali-Siar & Ashkan Mozdgir, 2024. "Food supply chain network design under uncertainty and pandemic disruption," Operational Research, Springer, vol. 24(2), pages 1-37, June.
    2. Pavlos Delias & Fotis C. Kitsios, 2023. "Operational research and business intelligence as drivers for digital transformation," Operational Research, Springer, vol. 23(3), pages 1-8, September.

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