Optimization model of medical UAV transportation network for biological sample leakage risk
Article Text (Baidu Translation)
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摘要: 为保障生物样本低空运输的安全性，提出了医用无人机运输系统的多目标优化模型与求解方法；考虑无人机运输事故的随机性和生物样本的危险性，建立了医用无人机运输生物样本的风险度量模型；以总风险最小化和总成本最小化为目标，构建了医用无人机运输网络优化模型，根据模型的计算复杂度，通过改进NSGA-Ⅱ算法思路，设计了求解步骤；通过深圳市实例和多个测试算例验证了模型和算法的有效性。研究结果表明：新模型在3 023.51 s内为深圳市生物样本运输提供165个有效的运输网络优化方案；新建的风险度量模型可定量评估载货医用无人机的运输风险，相比于几类常见的风险度量模型，新模型求得的方案平均减少约18.32%的总成本，平均提升约1.3倍风险均摊度；针对不同规模的优化问题，改进算法能够在有限的求解时间内提供多个有效的非支配解，并保有一定的计算稳定性。建立的模型和求解算法可为生物样本低空运输与应急安全管理提供医用无人机运输网络规划方案和风险控制方法。Abstract: To ensure the safety of low-altitude transportation of biological samples, a multi-objective optimization model and a solution procedure are developed for the medical UAV transportation system. Considering the randomness of UAV transportation accidents, as well as the risk of biological sample leakage, a risk measurement model for medical UAV transportation is established. A medical UAV transportation network optimization model is built with the objectives of minimizing total cost and total risk. Considering the computational complexity of the proposed model, a modified NSGA-Ⅱ algorithm is adopted to design the solution procedure. Finally, a real-life case in Shenzhen, China, and several test cases are used to demonstrate the effectiveness of the proposed model and algorithm. The results show that the proposed model provides 165 effective transportation network optimization schemes for biological sample transportation in Shenzhen within 3 023.51 s. Compared with traditional risk models, the proposed risk measurement model quantitatively evaluates the transportation risk of cargo-carrying medical UAVs, and the obtained solutions reduce the total cost by an average of 18.32% and increase the degree of risk sharing by an average of 1.3 times. When solving optimization problems of different scales, the improved algorithm provides multiple non-dominated solutions within limited solution time and maintains a certain level of computational stability. The proposed model and solution algorithm provide medical UAV transportation network planning schemes and risk control methods for low-altitude transportation and emergency safety management of biological samples.
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表 1 检测中心建设候选点信息
Table 1. Information on candidate locations for inspection centers
节点编号 固定建设成本/106元 最小检测量/(kg·d-1) 最大检测能力/(kg·d-1) 技术1 技术2 技术3 技术1 技术2 技术3 技术1 技术2 技术3 16 6.00 4.50 3.00 10.00 9.00 8.00 100.00 90.00 80.00 17 6.00 4.50 3.00 10.00 9.00 8.00 100.00 90.00 80.00 18 6.00 4.50 3.00 10.00 9.00 8.00 100.00 90.00 80.00 
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表 2 检测技术信息
Table 2. Information on inspection technologies
技术类型 病毒样本 血液样本 细胞样本 技术1 相容 相容 技术2 相容 相容 技术3 相容 相容
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表 3 试验中心和处置中心建设候选点信息
Table 3. Information on candidate locations for testing centers and response centers
节点编号 固定成本/106元 最大能力/(kg·d-1) 节点编号 固定成本/106元 最大能力/(kg·d-1) 19 2.00 100.00 22 2.20 90.00 20 1.80 85.00 23 2.80 80.00 21 3.00 90.00 24 2.80 85.00
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表 4 各类优化方案的计算结果
Table 4. Computational results of different optimization schemes
优化方案 检测中心(检测技术) 试验中心 处置中心 总成本/ 107元 总风险/ m3 收集路线(生物样本) 总成本最小 16(2), 18(3) 20 22 1.15 128.22 16-7-3-8-6-10-9-11-14-15-2-4-12-5-1-16(病毒样本)；
16-13-16(病毒样本)；18-1-2-4-9-11-5-12-18(血液样本)；
18-10-7-14-3-8-6-15-13-18(血液样本);
16-7-3-8-11-9-15-16(细胞样本)；
18-10-6-12-4-2-1-5-14-13-18(细胞样本)总风险最小 16(1), 16(2) 19, 20, 21 22, 23, 24 2.51 36.24 16-3-8-11-13-14-16(病毒样本)；
16-5-10-2-15-4-7-6-12-1-9-16(病毒样本)；
16-8-5-12-3-2-16(血液样本)；
16-11-4-13-9-10-14-15-7-1-6-16(血液样本)；
16-8-3-15-9-5-12-4-16(细胞样本)；
16-10-2-7-6-1-11-14-13-16(细胞样本)推荐方案 16(2), 16(3) 19, 20, 21 22, 23, 24 2.22 77.69 16-3-5-8-6-4-15-16(病毒样本)；
16-13-9-7-1-10-14-12-2-11-16(病毒样本)；
16-11-3-5-4-15-16(血液样本)；
16-13-9-7-1-10-14-2-12-8-6-16(血液样本)；
16-14-12-3-15-4-11-5-16(细胞样本)；
16-6-8-10-9-7-1-2-13-16(细胞样本)
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表 5 风险度量模型的对比结果
Table 5. Comparativeresults of risk measurement models
风险度量模型 总成本/107元 均摊运输风险 传统风险度量模型 3.09 0.67 箱式模型 2.51 0.81 高斯烟羽模型 2.28 0.39 本文模型 2.51 0.27
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表 6 改进算法与常规多目标求解方法的对比结果
Table 6. Comparative results of the proposed algorithm and conventional multi-objective solution methods
求解方法 求解时间/s Pareto解数量 线性加权 19 269.51 11 增广域约束 12 981.02 10 遗传算法 8 741.93 48 本文改进算法 3 023.51 165
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表 7 改进NSGA-Ⅱ算法与常规NSGA-Ⅱ算法测试对比结果
Table 7. Comparative results of the improved NSGA-Ⅱ algorithm and the conventional NSGA-Ⅱ algorithm
算法 推荐方案的总成本/107元 推荐方案的总风险范围/m3 Pareto解的数量 求解时间/s 常规NSGA-Ⅱ 2.29 93.10 144 2 821.48 改进NSGA-Ⅱ 2.22 77.69 165 3 023.51 变化率/% -3.06 -16.55 14.58 7.16
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表 8 NSGA-Ⅱ算法改进前后对比
Table 8. Comparison of NSGA-Ⅱ before and after improvement
迭代测试组 迭代次数 求解方法 推荐方案的平均成本/107元 推荐方案的平均风险范围/m3 平均求解时间/s Pareto解的平均数量 1 1 000 常规NSGA-Ⅱ算法 2.13 90.67 2 761.53 141 改进NSGA-Ⅱ算法 2.01 76.93 2 973.92 162 变化率/% -5.63 -15.15 7.69 14.89 2 3 000 常规NSGA-Ⅱ算法 1.98 89.35 5 310.29 150 改进NSGA-Ⅱ算法 1.86 75.06 5 847.23 167 变化率/% -6.06 -15.99 10.11 11.33 3 5 000 常规NSGA-Ⅱ算法 1.93 86.42 11 285.22 149 改进NSGA-Ⅱ算法 1.84 74.95 11 863.72 164 变化率/% -4.66 -13.27 5.13 10.07
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表 9 事故概率的敏感性测试
Table 9. Sensitivity analysis for incident probability
测试组 平均总成本/ 107元 平均总风险范围/ m3 Pareto解的平均数量 测试组1 2.20 835.09 117 测试组2 2.25 5.73 131 推荐方案 2.22 77.69 165
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