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A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts
A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts
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A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts
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A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts
A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts

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A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts
A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts
Journal Article

A Location-Allocation-Vehicle Routing Model for Humanitarian Blood Supply Chain in Aftermath of Earthquake under IER Uncertainty Considering Quality Concepts

2025
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Overview
This paper presents a Location-Allocation-Vehicle Routing Problem to design a humanitarian blood supply chain in response to earthquakes, incorporating quality concepts, reliability, and horizontal communication. The aim of the model is to minimize the total cost, minimize maximum shortage of demand points with high priority and low route value, and maximize the satisfaction of customers, including donors, hospitals, and blood transfusion centers. In order to deal with considerations of real world, the structure of the blood supply chain and all the intricacies incorporated in the model are defined based on the network and challenges of Blood Transfusion Center of Tehran. In addition, the blood demand and reliability of routes and facilities are considered uncertain, and the Interval Evidential Reasoning (IER) approach is used to handle the uncertainty. Since the problem is NP-hard, NSGAII and MOPSO algorithms have been applied to solve it. To demonstrate the efficiency of the model and compare the algorithms, several numerical examples in different sizes are designed. Finally, the most favorable algorithm is chosen for each size using the TOPSIS method.