A Comparative Study of Metaheuristics for Blood Assignment Problem

Abstract

Prinolan Govender School of Mathematics, Statistics and Computer ScienceMaster of Computer ScienceThe Blood Assignment Problem (BAP) is a real world and NP-hard combinatorial optimization problem. The study of BAP is significant due to the continuous demand for blood transfusion during medical emergencies. However, the formulation of this problem faces various challenges that stretch from managing critical blood shortages, limited shelf life and, blood type incompatibility that constrain the random transfusion of blood to patients.The transfusion of incompatible blood types between patient and donor can lead to adverse side effects on the patients. Usually, the sudden need for blood units arises as a result of unforeseen trauma that requires urgent medical attention. This condition can interrupt the supply of blood units and may result in the blood bank importing additional blood products from external sources, thereby increasing its running cost and other risk factors associated with blood transfusion. This however, might have serious consequences in terms of medical emergency, running cost and supply of blood units. Therefore, by taking these factors into consideration the aforementioned study implemented five global metaheuristic optimization algorithms to solve the BAP. Each of these algorithms was hybridized with a sustainable blood assignment policy that relates to the South Africa blood banks. The objective of this study was to minimize blood product wastage with emphasis on expiry and reduction in the amount of importation from external sources. Extensive computational experiments were conducted over a total of six different datasets, and the results validate the reliability and effectiveness of each of the proposed algorithms. Results were analysed across three major aspects, namely, the average levels of importation, expiry across a finite time period and computational time experienced by each of the metaheuristic algorithms. The numerical results obtained show that the Particle Swarm Optimization algorithm was better in terms of computational time. Furthermore, none of the algorithms experienced any form of expiry within the allotted time frame. Moreover, the results also revealed that the Symbiotic Organism Search algorithm produced the lowest average result for importation; therefore, it was considered the most reliable and proficient algorithm for the BAP.