Abstract
The supply chain management of blood products specifically deals with the aspect of efficient planning, implementing, and controlling of the in and out flow processes of blood unity in the blood bank system. Therefore, any improvement in the management of these chains would directly influence the manner in which blood and blood products are distributed to all the various sectors requiring such scarce and precious resources. Generally, the management of blood products is difficult due to the four ABO blood groups, which is further complicated by positive/negative rhesus factors. In this paper, a more simplified and robust dynamic mathematical model is presented for the efficient management of the blood bank. The corresponding sets of governing equations from an existing model are extended to cover the rhesus factors and the solution methods of the newly derived equations are subsequently investigated. In addition, a mathematical representation of the decision making process is presented as a function of the blood bank stockpile. Furthermore, in order to demonstrate the robustness of the developed model and to provide managerial insights, a new global hybrid symbiotic organisms search genetic particle swarm optimization algorithm is developed. Several numerical computations are performed using real-world datasets from the Enugu National Blood Transfusion Centre, in Nigeria, which fall within the monthly initial blood volume bounds of 300 over a period of eight years (2010 - 2018). Finally, the experimental results show that the mathematical model and metaheuristic optimization method proposed in this paper offer a better solution approach for blood allocation in dynamic environments. More so, the impact of some essential control parameters on the results are analysed to help the blood bank managers or decision makers to select accurately the desired parameters for optimal results yield.
Highlights
The human blood inventory management is characterized by a string of factors, which can be complicated over time [1]
EXPERIMENTAL RESULTS AND DISCUSSION extensive experiments are described, which were conducted in order to investigate the practicality of the mathematical model formulation and the efficacy of the proposed hybrid SOSGAPSO, in comparison to the performance of other existing algorithms, in solving the blood assignment problem
The representative algorithms namely, the symbiotic organisms search algorithm (SOS), particle swarm optimization (PSO) and genetic algorithm (GA) choice of parameter selection is similar to the implementation by Govender and Ezugwu [10], [31], [46] and Ezugwu et al [29], in terms of parameter configuration, they differ with regards to the real-life data set used for the current study
Summary
The human blood inventory management is characterized by a string of factors, which can be complicated over time [1]. According to Karl Landsteiner in 1901, human blood can be classified into four main groups known as the ABO system. These blood groups are significant with regards to storage and distribution, as compromising or mixing incompatible blood types can lead to blood clumping ( known as agglutination), which can be life-threatening for most patients [1]. The distribution of blood groups within a population may differ from country to country. In South Africa, the O and A blood groups are known to be dominant, with 46% and 37% blood type proportions, respectively. The B and AB blood groups are known to be less assertive, with 14% and 4% blood type proportions, respectively [2]
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