A non-clustered approach to platelet collection routing problem

Name: A non-clustered approach to platelet collection routing problem
Author: Talebi Khameneh, R., Elyasi, Milad, Özener, Okan Örsan, Ekici, Ali

İsim	A non-clustered approach to platelet collection routing problem
Yazar	Talebi Khameneh, R., Elyasi, Milad, Özener, Okan Örsan, Ekici, Ali
Basım Tarihi:	2023-12
Basım Yeri	- Elsevier
Konu	Blood supply chain, Genetic algorithm, Invasive weed optimization, Platelet production, Transportation
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0305-0548
Kayıt Numarası	0c4438f9-2591-4640-bc4d-ded449666b70
Lokasyon	Industrial Engineering
Tarih	2023-12
Örnek Metin	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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A non-clustered approach to platelet collection routing problem

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

Basım Tarihi 2023-12

Basım Yeri - Elsevier

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0305-0548

Kayıt Numarası 0c4438f9-2591-4640-bc4d-ded449666b70

Lokasyon Industrial Engineering

Tarih 2023-12

Örnek Metin 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