新冠疫情下医疗废物管理的选址-分配优化

The optimization of medical waste location-allocation under the COVID-19 epidemic

新冠疫情的爆发产生大量医疗废物,为降低环境污染和人员感染,提出一类新冠疫情下医疗废物管理的选址-分配优化方法。根据环境系统的动态性,设计时变"环境-人口"风险度量模型,建立风险最小和成本最小的多目标选址-分配模型;针对模型计算复杂性,设计基于字典式赋权切比雪夫方法的多目标优化算法;最后,通过"小-中-大"三种不同规模的4个测试算例,验证模型和算法的有效性。计算结果表明:相较于传统方法,新模型和算法能够为疫情防控提供多个有效的医疗废物管理方案,所得方案能够平均降低约27.27%的物流成本和32.72%的二次传染风险。

The outbreak of COVID-19 epidemic produces a large amount of medical waste.In order to reduce the environmental pollution and human infection derived from the medical wastes management,a multi-objective optimization of the medical wastes location-allocation in the time-varying environment is proposed,in which the facility location and medical waste flows allocation in the network are simultaneously optimized.According to the dynamic environment system,a time-vary"environment-population"risk assessment is developed,and a multi-objective location-allocation model with the minimization of total risks and costs derived from the decisions is formulated.To solve the proposed model,a multi-optimization solution procedure modified from the lexicographic weighted Tchebycheff method is also designed.Finally,four tests with three different scales"small-medium-large scale"are provided to demonstrate the workability.The computational result shows that,comparing to the traditional models,the proposed model and method can provide multiple efficient location-allocation plans of the medical wastes management to defense and control the epidemic,for which the result can provide the average reduction of 27.27%and 32.72%individually in the total cost and risk of secondary infection.