Abstract: For developing countries with uneven medical resources and a large population base, how to reasonably locate and allocate plasma banks to effectively ensure the supply of plasma for the treatment of severe infectious disease patients in the recovery period is an urgent problem. In order to better cope with the impact of large-scale infectious diseases on the health and safety system, a multi-objective LAP optimization model of plasma banks considering multiple scenarios, capacity constraints, supply chain networks, collaborative positioning and other factors is established with the goal of maximizing the timeliness of emergency plasma guarantee and minimizing the total cost.
The gray wolf optimization algorithm uses real number vectors to simulate the location of wolf packs to solve continuous optimization problems. For both functional and high-dimensional combinatorial optimization problems, it has excellent optimization capabilities. According to the characteristics of the model as a multi-objective discrete optimization problem, an improved multi-objective gray wolf optimization algorithm (IMOGWO) is designed to solve it. IMOGWO mainly makes several changes. Firstly, it uses external population to store the current non-dominant solution. Secondly, it proposes a new head wolf selection strategy for discrete multi-objective optimization. Finally, it uses non-dominant strategy and elite strategy. These strategies can work well with the original algorithm to improve the ability to solve problems.
Taking into account medical conditions, convenient transportation and other conditions, the article plans to identify 19 candidate sites for plasma banks and 47 candidate sites for collection facilities. As India’s population base and density are similar to those of China, the plasma demand here is based on India, which is also a developing country. Relevant Indian epidemic data can be obtained at covidinda.org. Based on the population density ratio, it is possible to estimate the possible 30-day epidemic data of China under the liberalization policy. Blood donation data are obtained from the Health Commission. All these are used to set various data for this scenario.
In order to verify the effectiveness of the algorithm for solving the model example, this algorithm is compared and analyzed with the traditional multi-objective gray wolf algorithm (MOGWO) and other three different multi-objective intelligent optimization algorithms. The experimental results show that both IMOGWO based on the total time and cost are better, and it is also the best of the algorithms compared with the average values of the two targets after normalization. The results of the model can minimize the total travel time of plasma, ensure the timely storage and supply of plasma, reduce the total cost, and quickly and effectively select a reasonable plasma bank location and distribution scheme.
The article’s research model still has many aspects that can be explored in the future, and this article proposes prospects for future research: (1)Though the article’s model is reasonable, it still cannot reflect the complexity of large-scale infectious disease environments, making it difficult to truly predict and implement solutions. In the future, it can be used to build more realistic models. (2)Certainty has a significant impact on the formulation of the entire scheme. The future article will consider the impact of uncertainty to make the entire model more reasonable. In future research, it will be improved in order to extend the basic model established in this article to the actual location and distribution planning of plasma banks.

Key words: large-scale infectious diseases, location-allocation problem, Pareto solutions, improved multi-objective gray wolf optimization algorithm

摘要： 针对医疗资源不均、人口基数大的发展中国家,如何对血浆库进行合理选址并分配,以有效保证恢复期血浆对传染病重症患者治疗的供给是亟待解决的问题。为更好应对大规模传染病对卫生安全系统带来的冲击,文章以应急血浆保障时效性最高和总成本最少为目标,建立了一个考虑多情景、容量限制、带有供应链网络及协同定位等因素的血浆库多目标LAP优化模型。根据该模型的性质特点,设计了一种改进多目标灰狼优化算法进行求解。实验结果表明,该算法能够有效获得一簇Pareto解,可权衡实际需求和对不同目标,考虑血浆时效性和成本,在Pareto解中可选择恰当的血浆库选址分配方案,对于大规模传染病下血浆库的合理选址和分配具有重要指导意义。

关键词: 大规模传染病, 选址分配问题, Pareto解, 改进多目标灰狼优化算法