摘要：

现有选择性众包模式大多考虑从配送中心或中转站集中取货后再进行配送的场景, 无法满足工业互联网平台需要从分布式制造企业取货后配送给行业用户的现实需求。针对物流服务选择性众包的多车辆多起始点取送货路径规划问题, 构建以社会车辆和专用车辆差异化起始点和终点、取送货点对应关系等为约束及以社会车辆物流服务报价和专用车辆配送成本之和最小化为决策目标的整数线性规划模型。设计改进的模因算法(IMA), 开发基于概率的正逆混合交叉(MPNC)算子、路径间邻域搜索(VNS)和路径内邻域搜索(PNS)混合策略及其对应的两阶段路径修复方法。实验结果表明, MPNC算子比传统的部分交叉算子能够在更短的时间内获得更丰富的种群多样性, VNS和PNS混合策略比单邻域搜索可产生更优的可行解。不同规模的人工算例结果表明, IMA比遗传算法(GA)、模拟退火(SA)和改进的粒子群优化(PSO)等算法在寻优性能和局部脱困能力等方面更具优势, 并且其采用选择性众包相比于采用纯社会车辆和纯专用车辆降低了实际案例的物流服务成本。

关键词: 工业互联网平台, 物流服务选择性众包, 取送货问题, 路径规划, 改进的模因算法

Abstract:

Most existing selective crowdsourcing models assume that goods are collected from a centralized distribution center or transfer station and then delivered to their final destinations. However, this approach fails to meet the actual demands of industrial Internet platforms, which require collecting goods from distributed manufacturing enterprises and delivering them to industrial users. To address the multi-vehicle, multi-start point pickup, and delivery path planning problems in selective crowdsourcing for logistics services, we propose an integer linear programming model. This model incorporates constraints related to the start and ending points of social and dedicated vehicles, the correspondence between pickup and delivery points, and other relevant parameters. The primary objective is to minimize the total cost, which includes the logistics service fees of social vehicles and the delivery costs of dedicated vehicles. An Improved Memetic Algorithm (IMA) is designed, which includes probability-based Mixed Positive and Negative Crossover (MPNC) operators, hybrid strategies combining Inter-vehicle Neighborhood Search (VNS) and Inter-path Neighborhood Search (PNS), and the corresponding two-stage path repair methods. Results from the experiments indicate that the newly developed MPNC crossover operators achieve higher population diversity in less time compared to traditional partial crossover operators, whereas the VNS and PNS hybrid strategies generate more feasible solutions than single neighborhood search. The results from artificial examples at different scales show that the IMA outperforms traditional algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and improved Particle Swarm Optimization (PSO) in terms of optimization performance and local problem-solving ability. The IMA adopts a selective crowdsourcing model, which reduces actual logistics service costs compared to the use of either pure social or pure dedicated vehicles.

Key words: industrial internet platform, selective crowdsourcing of logistics service, pickup and delivery problem, path planning, Improved Memetic Algorithm (IMA)