目的 针对即时物流中客户心理感知偏差与车辆动态碳排放量化不足的问题,构建一种兼顾低成本、低客户不满意度与绿色低碳的卡车与无人机协同配送路径优化模型,以寻求城市配送中服务质量与环境效益之间的最优平衡。方法 首先,根据客户位置的可达性,将客户划分为3种服务类型,以明确协同服务原则。其次,综合考虑车辆载重、软硬时间窗约束、客户不满意度及碳排放等因素,以配送总成本、客户不满意度和碳排放最小化为目标,建立多目标优化模型。通过引入冲突检测修复机制并融合局部搜索策略,对NSGA-Ⅱ算法进行改进,增强算法的全局寻优能力。最后,通过性能检验、算法模型对比、敏感性分析及真实场景实证分析,验证模型与算法的有效性。结果 该算法求解质量更高,能有效处理协同配送的复杂约束。相比传统卡车配送模式,协同配送方案在配送总成本、客户不满意度与碳排放方面分别下降了7.07%、19.47%、24.84%,真实场景分析进一步验证了模型的有效性。结论 研究验证了协同配送模型与改进NSGA-Ⅱ算法在即时物流场景下的适用性,能够有效实现降低客户不满意度,在实现末端配送降本增效的同时,兼顾了低碳环保与用户体验的协同提升。
Abstract
To address the issues of customer perception biases and insufficient quantification of dynamic vehicle carbon emissions in instant logistics, the work aims to construct a collaborative delivery route optimization model for trucks and drones that balances low costs, low customer dissatisfaction, and green, low-carbon operations, achieving an optimal balance between service quality and environmental benefits in urban delivery. Firstly, customers were classified into three service types based on the accessibility of their locations to establish the principles of collaborative service. Secondly, a multi-objective optimization model was established with the objectives of minimizing total delivery cost, customer dissatisfaction, and carbon emissions by taking into account factors such as vehicle payload, soft and hard time window constraints, customer dissatisfaction, and carbon emissions. The NSGA-II algorithm was improved by introducing a conflict detection and resolution mechanism and integrating local search strategies to enhance its global optimization capability. Finally, the effectiveness of the model and algorithm was validated through performance testing, algorithm model comparisons, sensitivity analysis, and real-world empirical analysis. The algorithm yielded higher-quality solutions and could effectively handle the complex constraints of collaborative delivery. Compared to traditional truck delivery models, the collaborative delivery scheme reduced total delivery costs, customer dissatisfaction, and carbon emissions by 7.07%, 19.47%, and 24.84%, respectively. Real-world scenario analysis further validated the model's effectiveness. This work validates the applicability of the collaborative delivery model and the improved NSGA-II algorithm in on-demand logistics scenarios, effectively reducing customer dissatisfaction while simultaneously achieving cost reduction and efficiency gains in last-mile delivery, and balancing the synergistic improvement of low-carbon sustainability and user experience.
关键词
多目标优化 /
改进非支配排序遗传算法 /
即时物流 /
卡车与无人机协同配送
Key words
multi-objective optimization /
improved non-dominated sorting genetic algorithm /
instant logistics /
truck-drone collaborative delivery
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基金
湖北汽车工业学院博士科研启动基金项目（BK202340）
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