A hierarchical model for strategic and operational planning in blood transportation with drones.

Blood transportation is a critical aspect of the healthcare systems, ensuring whole blood and blood products are delivered to patients in a timely and efficient manner. However, transportation of blood and other medical supplies can be challenging, especially in urban areas with limited infrastructu...

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Main Authors: Amirali Amirsahami, Farnaz Barzinpour, Mir Saman Pishvaee
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0291352
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author Amirali Amirsahami
Farnaz Barzinpour
Mir Saman Pishvaee
author_facet Amirali Amirsahami
Farnaz Barzinpour
Mir Saman Pishvaee
author_sort Amirali Amirsahami
collection DOAJ
description Blood transportation is a critical aspect of the healthcare systems, ensuring whole blood and blood products are delivered to patients in a timely and efficient manner. However, transportation of blood and other medical supplies can be challenging, especially in urban areas with limited infrastructure and heavy traffic. Drones have become increasingly important in recent years as a means of delivering medical supplies, including blood, due to their ability to provide fast, reliable, and cost-effective transportation. This study proposes two mathematical programming models in the hierarchical structure to improve decision-making for strategic and operational planning in the blood supply chain network. The limited information available in strategic planning presents risks to the blood supply chain, making it imperative to address uncertainties. To tackle this challenge, a novel approach called Scenario-based Robust Bi-objective Optimization has been proposed. The first model employs this approach to efficiently handle demand uncertainty by simultaneously maximizing the covered demand and minimizing costs. The model is subsequently solved using the augmented ε-constraint method. The second model is a routing-scheduling operational model that aims to minimize the sum of operations time, taking into account time windows for blood collection centers and hospitals. The developed hierarchical model is implemented in a three-level supply chain of Tehran province under three crisis scenarios in different parts. The findings and analysis of this implementation suggest that it is beneficial to set up drone stations in cost-effective and central locations to avoid costly network design. Furthermore, utilizing the minimum number of feasible drones enhances operational time and results in cost savings and increased efficiency. Overall, this study highlights the potential of using drones for blood transportation in urban settings, which can have significant implications for improving the quality of healthcare delivery.
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spelling doaj.art-5e169487c67443c3a05667927fa520d02023-09-21T05:32:29ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01189e029135210.1371/journal.pone.0291352A hierarchical model for strategic and operational planning in blood transportation with drones.Amirali AmirsahamiFarnaz BarzinpourMir Saman PishvaeeBlood transportation is a critical aspect of the healthcare systems, ensuring whole blood and blood products are delivered to patients in a timely and efficient manner. However, transportation of blood and other medical supplies can be challenging, especially in urban areas with limited infrastructure and heavy traffic. Drones have become increasingly important in recent years as a means of delivering medical supplies, including blood, due to their ability to provide fast, reliable, and cost-effective transportation. This study proposes two mathematical programming models in the hierarchical structure to improve decision-making for strategic and operational planning in the blood supply chain network. The limited information available in strategic planning presents risks to the blood supply chain, making it imperative to address uncertainties. To tackle this challenge, a novel approach called Scenario-based Robust Bi-objective Optimization has been proposed. The first model employs this approach to efficiently handle demand uncertainty by simultaneously maximizing the covered demand and minimizing costs. The model is subsequently solved using the augmented ε-constraint method. The second model is a routing-scheduling operational model that aims to minimize the sum of operations time, taking into account time windows for blood collection centers and hospitals. The developed hierarchical model is implemented in a three-level supply chain of Tehran province under three crisis scenarios in different parts. The findings and analysis of this implementation suggest that it is beneficial to set up drone stations in cost-effective and central locations to avoid costly network design. Furthermore, utilizing the minimum number of feasible drones enhances operational time and results in cost savings and increased efficiency. Overall, this study highlights the potential of using drones for blood transportation in urban settings, which can have significant implications for improving the quality of healthcare delivery.https://doi.org/10.1371/journal.pone.0291352
spellingShingle Amirali Amirsahami
Farnaz Barzinpour
Mir Saman Pishvaee
A hierarchical model for strategic and operational planning in blood transportation with drones.
PLoS ONE
title A hierarchical model for strategic and operational planning in blood transportation with drones.
title_full A hierarchical model for strategic and operational planning in blood transportation with drones.
title_fullStr A hierarchical model for strategic and operational planning in blood transportation with drones.
title_full_unstemmed A hierarchical model for strategic and operational planning in blood transportation with drones.
title_short A hierarchical model for strategic and operational planning in blood transportation with drones.
title_sort hierarchical model for strategic and operational planning in blood transportation with drones
url https://doi.org/10.1371/journal.pone.0291352
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