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A non-clustered approach to platelet collection routing problem

Title	A non-clustered approach to platelet collection routing problem
Author	Talebi Khameneh, R., Elyasi, Milad, Özener, Okan Örsan, Ekici, Ali
Publication Date:	2023-12
Publication Place	- Elsevier
Subject	Blood supply chain, Genetic algorithm, Invasive weed optimization, Platelet production, Transportation
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0305-0548
Record ID	0c4438f9-2591-4640-bc4d-ded449666b70
Library Location	Industrial Engineering
Date	2023-12
Sample Text	One of the blood components that can be extracted from whole blood is the platelet, which has a wide range of uses in medical fields. Due to the perishable nature of platelets, it is recommended that the separation occurs within six hours after the donation. Moreover, platelets constitute less than one percent of the whole blood volume, yet they are highly demanded. Given the importance of platelets in healthcare, their perishability, and their limited supply, an effective platelet supply chain leans on well-managed whole blood collection operations. In this study, we consider a blood collection problem (BCP) focusing on the collection of whole blood donations from the blood donation sites (BDSs). Different from the basic form of BCP, we consider the processing time limit (PTL) of blood and arbitrary donation patterns of donors as well as relaxing the assumption of assigning each blood collection vehicle (BCV) to a set of BDSs. Therefore, we define the non-clustered maximum blood collection problem (NCMBCP) as a variant of BCP. In this study, we examine routing decisions for platelet collections while relaxing the clustering requirement from the BDSs, which results in a significant increase in the complexity of the problem. In order to solve the problem, we propose a hybrid genetic algorithm (HGA) and an invasive weed optimization (IWO) algorithm that provide considerable improvements over the best solution in the literature for the clustered variant of the problem and outperform it (on average) by 8.68% and 8.16%, respectively.
DOI	10.1016/j.cor.2023.106366
Cilt	160

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Özyeğin University

A non-clustered approach to platelet collection routing problem

Author Talebi Khameneh, R., Elyasi, Milad, Özener, Okan Örsan, Ekici, Ali

Publication Date 2023-12

Publication Place - Elsevier

Subject Blood supply chain, Genetic algorithm, Invasive weed optimization, Platelet production, Transportation

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0305-0548

Record ID 0c4438f9-2591-4640-bc4d-ded449666b70

Library Location Industrial Engineering

Date 2023-12

Sample Text One of the blood components that can be extracted from whole blood is the platelet, which has a wide range of uses in medical fields. Due to the perishable nature of platelets, it is recommended that the separation occurs within six hours after the donation. Moreover, platelets constitute less than one percent of the whole blood volume, yet they are highly demanded. Given the importance of platelets in healthcare, their perishability, and their limited supply, an effective platelet supply chain leans on well-managed whole blood collection operations. In this study, we consider a blood collection problem (BCP) focusing on the collection of whole blood donations from the blood donation sites (BDSs). Different from the basic form of BCP, we consider the processing time limit (PTL) of blood and arbitrary donation patterns of donors as well as relaxing the assumption of assigning each blood collection vehicle (BCV) to a set of BDSs. Therefore, we define the non-clustered maximum blood collection problem (NCMBCP) as a variant of BCP. In this study, we examine routing decisions for platelet collections while relaxing the clustering requirement from the BDSs, which results in a significant increase in the complexity of the problem. In order to solve the problem, we propose a hybrid genetic algorithm (HGA) and an invasive weed optimization (IWO) algorithm that provide considerable improvements over the best solution in the literature for the clustered variant of the problem and outperform it (on average) by 8.68% and 8.16%, respectively.

DOI 10.1016/j.cor.2023.106366

Cilt 160