基于无人机与卡车协作的封控社区生活物资配送方法

针对突发公共卫生事件情况下生活物资被病毒污染，小区内居民交叉感染及配送人员短缺等问题，本文提出一种基于无人机与卡车协作的封控社区生活物资配送方法。首先，规定无人机在完成每次配送后需要返回卡车位置处进行消杀，以配送过程中社区居民交叉感染风险和配送成本最小为优化目标，以配送服务完整性、无人机载货重量限制及生活物资时效性要求等为约束条件，建立混合整数规划模型，协同优化卡车额定载重、停靠位置、所需无人机数量和无人机组配送方案；然后，设计改进多目标粒子群算法求解模型，引入一种特殊的粒子编码方式，并耦合变异算子更新部分码位的粒子位置；最后，以长春市师范南园小区为例进行案例分析，从安全、配送成本、工作强度及一次性防控物资消耗等4个方面和卡车与人工协作配送模式进行比较分析发现，卡车与人工协作配送方法可以令社区居民交叉感染风险降低至少 91.8%，日配送成本降低16.9%，人力资源投入减少50%，一次性防护物资支出节约46.1%左右。

Delivery Method of Living Goods in Controlled CommunitiesBased on Cooperation Between Drones and Truck

Considering the event of public health emergencies that living goods are contaminated by viruses, crossinfection of residents in the communities, and shortage of delivery personnel, this study proposes a method to deliverthe living goods using coordinated drones and a truck. Drones are required to return to the parking position of the truckfor disinfecting after each delivery. In this process, the optimization objective is minimizing the risk of cross-infectionamong community residents and the delivery cost, and the constraints are delivery service integrity, payload capacityof drones and timeliness requirements of living goods. A mixed-integer programming model is developed tocollaboratively optimize the rated capacity and parking position of the truck, the required number and delivery plan ofdrones. The improved multi-objective particle swarm algorithm is then used to solve the proposed model. A specialparticle coding method is introduced, and the mutation operator is coupled to update the particle positions of some codepoints. The method is applied to the Nanyuan Normal residential area in Changchun city. The proposed method wascompared to the truck and manual collaborative delivery mode in safety, delivery cost, personnel working intensity, andconsumption of one-off prevention and control material. It was found that the proposed method can reduce the risk ofcross-infection among community residents at least 91.8%, diminish delivery cost by 16.9%, decrease human resourceinput by 50%, and save about 46.1% one-off prevention and control material expenditure.