Effective Metaheuristic Algorithms for Platelet Collection Routing Problem

Milad Elyasi (), Ramin Talebi Khameneh (), Ali Ekici () and Okan Örsan Özener ()
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Milad Elyasi: Industrial Engineering Department, Özyeğin University
Ramin Talebi Khameneh: Industrial Engineering Department, Özyeğin University
Ali Ekici: Industrial Engineering Department, Özyeğin University
Okan Örsan Özener: Industrial Engineering Department, Özyeğin University

Chapter Chapter 23 in Optimization Essentials, 2024, pp 695-719 from Springer

Abstract: Abstract In medical treatments, patients may need a particular blood product to continue their treatment process. The only source for all blood products is blood donation. In order to extract any product from donated blood, there is a specific time limit after donation occurs. One of the blood products is platelet, which is a vital element in cancer therapies, organ transplant procedures, and different surgical operations. Platelet production is the most time-sensitive process among all other blood products. Donated blood should be processed in blood processing centers within six hours after donation; otherwise, the donated blood will not be appropriate for platelet production. Therefore, blood collection vehicles should pick up and carry the donated blood into the blood processing center in a proper period to save blood products from perishing. Because of the perishability of donated blood and the accumulative nature of blood donation, the scheduling of blood collection vehicles to collect the maximum number of blood units for platelet production is a complicated routing problem. In this study, two metaheuristic methods, namely the hybrid flower pollination algorithm (HFPA) and simulated annealing (SA), are implemented to solve a variant of the maximum blood collection problem (MBCP). The effectiveness of the proposed algorithms through exhaustive computational studies is investigated. The proposed algorithms show an average improvement of around 4% and 6% in the objective function value over the benchmark algorithm.

Date: 2024
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DOI: 10.1007/978-981-99-5491-9_23

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