ABSTRACT

Blood is a valuable commodity in society due to its ability to save lives during crises. Furthermore, because of the scarcity of blood donors, blood assignment by blood banks requires meticulous planning and solid issuing policy. The multiple components of a blood banking system contribute to the complexity of maintaining an efficient structure for such a system. One particular aspect relates to the stochastic nature of the demand for blood units. This paper implements a mathematical model for a blood bank system in South Africa and additionally explores the possible implementation of a hybrid global optimisation metaheuristic approach for the efficient assignment of blood products in the blood bank system. The approximate optimisation method used is the hybridisation of the symbiotic organism search (SOS) algorithm and a pre-processing ecosystem services (PES) techniques. In order to show the practicability of the model and evaluate the accuracy and robustness of the newly proposed hybrid algorithm, several numerical computations were performed using synthetically generated datasets that fall within the initial blood volume bounds of 500 to 20, 000. The experimental results indicate that the hybrid symbiotic organisms search ecosystem services optimisation algorithm offers better solutions for blood allocation under a dynamic environment than does the standard symbiotic organism search algorithm and other previously proposed hybrid versions of the SOS methods.

摘要

血液是社会中宝贵的商品，因为它有能力在危机中挽救生命。此外，由于献血者的缺乏，血库的血液分配需要精心计划和可靠的发行政策。血库系统的多个组件导致维持用于这种系统的有效结构的复杂性。一个特定方面涉及对血液单位需求的随机性。本文为南非的血库系统实现了数学模型，并另外探索了在血库系统中有效分配血液产品的混合全局优化元启发式方法的可能实现。使用的近似优化方法是共生生物搜索（SOS）算法与预处理生态系统服务（PES）技术的混合。为了显示该模型的实用性并评估新提出的混合算法的准确性和鲁棒性，使用合成生成的数据集进行了一些数值计算，这些数据集的初始血容量范围为500至20，000。实验结果表明与标准的共生生物搜索算法和其他先前提出的SOS方法的混合版本相比，杂合共生生物搜索生态系统服务优化算法为动态环境下的血液分配提供了更好的解决方案。