Multi-Resource Flow Problem for Relief Supply Planning in Humanitarian Logistics

After a disaster, there’s often a disparity in the distribution of supplies among affected areas, with some experiencing excesses while others face shortages. To support the affected people more effectively with limited resources, we propose a multi-period, multi-resource distribution model that considers the shortages and inventory levels in disaster areas. This model integrates location, inventory, and flexible allocation of relief supplies for dynamic supply planning. To illustrate the use of our model in practical operation, we conduct numerical experiments. The results show the fulfillment or unfulfillment, and inventory conditions of multiply supplies for affected people groups during each subperiod. Additionally, a sensitivity analysis also demonstrates the impact of the penalty coefficient on the model.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/ieem62345.2024.10857118
Primary Topic
Facility Location and Emergency Management
Type
article
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article

Multi-Resource Flow Problem for Relief Supply Planning in Humanitarian Logistics

Etsuko Nishimura, Yufeng Guo
Facility Location and Emergency Management
article

Multi-Resource Flow Problem for Relief Supply Planning in Humanitarian Logistics

Etsuko Nishimura, Yufeng Guo
article en

Abstract

After a disaster, there’s often a disparity in the distribution of supplies among affected areas, with some experiencing excesses while others face shortages. To support the affected people more effectively with limited resources, we propose a multi-period, multi-resource distribution model that considers the shortages and inventory levels in disaster areas. This model integrates location, inventory, and flexible allocation of relief supplies for dynamic supply planning. To illustrate the use of our model in practical operation, we conduct numerical experiments. The results show the fulfillment or unfulfillment, and inventory conditions of multiply supplies for affected people groups during each subperiod. Additionally, a sensitivity analysis also demonstrates the impact of the penalty coefficient on the model.

Kobe University (JP)
Openalex Percentile: Top 63%
Facility Location and Emergency Management
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