应急消毒物资跨区域调配路径人工蜂群优化算法

Artificial Bee Colony Optimization Algorithm for Cross-Regional Distribution Paths of Emergency Disinfection Supplies

摘要

针对自然灾害发生后应急消毒物资跨区域调配路径的优化问题, 提出一种基于人工蜂群优化算法(ABC: Artificial Bee Colony)的规划方法。运用熵权法综合受灾面积、人口密度、定点医院医疗资源缺口等多维度指标, 对各受灾点及相关医院的需求紧迫度进行量化, 并依据量化结果将应急区域划分为红、橙、黄、蓝4个等级, 据此实现了分区优先级调度。基于此, 设计人工蜂群优化算法的适应度函数, 该函数包含配送总时间与紧迫需求未满足度两个指标。利用人工蜂群优化算法进行求解, 求出调配路径最佳规划方案。实验结果表明, 在两种典型工况下, 该方法最大适应度函数分别达到0.97和0.95, 较人工鱼群算法与遗传算法提升3.19%~13.10%, 显著提高了针对医院等重点机构的物资配送的时效性与应急优先性。

Abstract

A planning method based on artificial bee colony optimization algorithm ABC(Artificial Bee Colony) is proposed to optimize the cross regional allocation path of emergency disinfection materials after natural disasters. Using the entropy weight method to comprehensively evaluate multidimensional indicators such as disaster area, population density, and medical resource gaps in designated hospitals, the urgency of demand for each disaster site and related hospital is quantified. Based on the quantification results, the emergency area is divided into four levels: red, orange, yellow, and blue, in order to achieve priority scheduling in different zones. A fitness function for the artificial bee colony optimization algorithm is designed, which includes total delivery time and unmet urgent needs. Artificial bee colony optimization algorithm is used to solve and find the optimal planning scheme for the allocation path. The experimental results show that under two typical operating conditions, the maximum fitness functions reach 0.97 and 0.95, respectively, which is 3.19% to 13.10% higher than the artificial fish swarm algorithm and genetic algorithm. It significantly improves the timeliness and emergency priority of material distribution for key institutions such as hospitals.

关键词

应急物资调配 / 需求紧迫度 / 区域划分 / 适应度函数设计 / 人工蜂群算法 / 路径优化

Key words

emergency supply distribution / demand urgency / regional zoning / fitness function design / artificial bee colony (ABC)algorithm / path optimization

参考文献

[1] 王培栋, 汪玲. 考虑需求紧迫度的应急物资卡车-无人机联合配送路径优化[J]. 科学技术与工程, 2025, 25(28): 12231-12240.

[2] WANG P D, WANG L. Optimization of Joint Truck-Drone Delivery Paths for Emergency Supplies Considering Demand Urgency[J]. Science Technology and Engineering, 2025, 25(28): 12231-12240.

[3] 黄宝静, 陈诚, 王嘉源. 基于复杂网络的城市轨道交通货物运输路径优化方法[J]. 都市快轨交通, 2025, 38(6): 132-139.

[4] HUANG B J, CHEN C, WANG J Y. Optimization Method for Freight Intermodal Transportation of High Speed Rail and Urban Rail Transit Based on Complex Networks[J]. Urban Rapid Rail Transit, 2025, 38(6): 132-139.

[5] 陈超, 王佳苗, 马鑫晟. 突发公共卫生事件下应急医疗物资配送路径优化模型研究[J]. 重庆交通大学学报(自然科学版), 2024, 43(11): 76-83, 94.

[6] CHEN C, WANG J M, MA X S. Optimization Model of Emergency Medical Material Delivery Path under Sudden Public Health Emergencies[J]. Journal of Chongqing Jiaotong University (Natural Sciences), 2024, 43(11): 76-83,94.

[7] 刘艳秋, 胡绩辉. 基于混合人工鱼群算法的应急物流路径优化研究[J]. 中国管理科学, 2025, 33(8): 198-208.

[8] LIU Y Q, HU J H. Research on Emergency Logistics Path Optimization Based on Hybrid Artificial Fish Swarm Algorithm[J]. Chinese Journal of Management Science, 2025, 33(8): 198-208.

[9] 胡晓伟, 卢泓博, 安实. 考虑暴雨灾害动态影响的城市应急车辆救援路径优化研究[J]. 交通运输系统工程与信息, 2024, 24(3): 75-82, 93.

[10] HU X W, LU H B, AN S. Optimization of Urban Emergency Vehicle Rescue Route Considering Dynamic Impact of Rainstorm Disasters[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 75-82,93.

[11] 张鑫, 叶春明, 曹磊, 等. 考虑LIFO装载约束的绿色车辆路径研究[J]. 计算机仿真, 2025, 42(10): 257-262.

[12] ZHANG X, YE C M, CAO L, et al. Research on Green Vehicle Routing Problem Considering LIFO Loading Constrains[J]. Computer Simulation, 2025, 42(10): 257-262.

[13] 郭瑞军, 范超冉, 刘悦, 等. 基于需求紧迫度的不相容应急物资配送的车辆路径选择[J]. 中国安全生产科学技术, 2024, 20(12): 150-158.

[14] GUO R J, FAN C R, LIU Y, et al. Vehicle Path Selection for Distribution of Incompatible Emergency Materials Based on Demand Urgency[J]. Journal of Safety Science and Technology, 2024, 20(12): 150-158.

[15] 苏莹莹, 李慧茹, 卢洪志. 考虑需求紧迫度的应急物资车辆路径节点优化模型[J]. 机械设计与研究, 2025, 41(4): 229-237.

[16] SU Y Y, LI H R, LU H Z. Vehicle Routing Optimization Model for Emergency Supplies Considering the Urgency of Demand[J]. Machine Design & Research, 2025, 41(4): 229-237.

[17] 张林静, 丁溢. 基于卡车-无人机协同配送的双目标应急物资运送优化[J]. 系统管理学报, 2025, 34(1): 81-95.

[18] ZHANG L J, DING Y. Optimizing Bi-Objective Emergency Supply Transportation Based on Synchronized Truck-Drone Delivery[J]. Journal of Systems & Management, 2025, 34(1): 81-95.

[19] 李国旗, 郝志丹, 杨佳鑫, 等. 运力短缺下的生活物资临时分配点选址-路径优化研究[J]. 交通运输系统工程与信息, 2025, 25(2): 304-313.

[20] LI G Q, HAO Z D, YANG J X, et al. Living Materials Temporary Distribution Points Location-Routing Optimization under Transportation Capacity Shortage[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 304-313.

[21] 魏明, 吴蕾, 孙博. 一类多物资类型无人机应急配送路径优化模型[J]. 数学的实践与认识, 2024, 54(6): 141-150.

[22] WEI M, WU L, SUN B. Optimization Model of Emergency Delivery Routing of UAVs with Multi-Type Goods[J]. Mathematics in Practice and Theory, 2024, 54(6): 141-150.

[23] 唐建勋, 岳帅, 王岩韬. 基于速度暂停粒子群算法的应急物资空地联合调运[J]. 科学技术与工程, 2025, 25(28): 12241-12252.

[24] TANG J X, YUE S, WANG Y T. Air-Ground Scheduling of Emergency Supplies Based on VPPSO Algorithm[J]. Science Technology and Engineering, 2025, 25(28): 12241-12252.

[25] 史晓东, 郭永城, 马铭杞, 等. 公共卫生事件下交叉感染风险的车辆路径优化研究[J]. 系统仿真学报, 2025, 37(4): 910-921.

[26] SHI X D, GUO Y C, MA M Q, et al. Optimization of Vehicle Routing for Cross-Infection Risk in the Epidemic[J]. Journal of System Simulation, 2025, 37(4): 910-921.

[27] 马梓元, 刘伟鹏, 胡春朝, 等. 基于改进人工蜂群算法的无人飞行器路径协同规划[J]. 南京航空航天大学学报(自然科学版), 2025, 57(6): 1124-1134.

[28] MA Z Y, LIU W P, HU C Z, et al. Path Cooperative Planning for Unmanned Aerial Vehicles Based on Improved Artificial Bee Colony Algorithm[J]. Journal of Nanjing University of Aeronautics & Astronautics (Natural Science Edition), 2025, 57(6): 1124-1134.

[29] 刘润恺, 胡伟, 宋彦杰, 等. 面向三维无人机物流路径规划问题的改进人工蜂群算法[J]. 控制理论与应用, 2025, 42(11): 2274-2285.

[30] LIU R K, HU W, SONG Y J, et al. An Improved Artificial Bee Colony Algorithm for 3D UAV Logistics Path Planning Problem[J]. Control Theory & Applications, 2025, 42(11): 2274-2285.

[31] 刘笑, 朱磊, 张媛. 基于人工蜂群算法的循环包装箱回收中心多目标选址[J]. 包装工程, 2025, 46(11): 267-276.

[32] LIU X, ZHU L, ZHANG Y. Multi-Objective Site Selection of Circular Packaging Recycling Centers Based on Artificial Bee Colony Algorithm[J]. Packaging Engineering, 2025, 46(11): 267-276.

基金资助

山西省基础研究计划基金资助项目(202403021221141)