This paper studies a concept for protecting vulnerable population groups during pandemics using direct home deliveries of essential supplies, from a distribution logistics perspective. The purpose of this paper is to evaluate feasible and resource-efficient home delivery strategies, including collaboration between retailers and logistics service providers based on a practical application.
A food home delivery concept in urban areas during pandemics is mathematically modeled. All seniors living in a district of Berlin, Germany, represent the vulnerable population supplied by a grocery distribution center. A capacitated vehicle routing problem (CVRP) is developed in combination with a k-means clustering algorithm. To manage this large-scale problem efficiently, mixed-integer programming (MIP) is used. The impact of collaboration and additional delivery scenarios is examined with a sensitivity analysis.
Roughly 45 medically vulnerable persons can be served by one delivery vehicle in the baseline scenario. Operational measures allow a drastic decrease in required resources by reducing service quality. In this way, home delivery for the vulnerable population of Berlin can be achieved. This requires collaboration between grocery and parcel services and public authorities as well as overcoming accompanying challenges.
Developing a home delivery concept for providing essential goods to urban vulnerable groups during pandemics creates a special value. Setting a large-scale CVRP with variable fleet size in combination with a clustering algorithm contributes to the originality.
This work has partly been funded by the German Federal Ministry of Education and Research (BMBF) in the NOLAN (grant number: 13N14459) project.
Breitbarth, E., Groβ, W. and Zienau, A. (2021), "Protecting vulnerable people during pandemics through home delivery of essential supplies: a distribution logistics model", Journal of Humanitarian Logistics and Supply Chain Management, Vol. 11 No. 2, pp. 227-247. https://doi.org/10.1108/JHLSCM-07-2020-0062
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