This study focuses on a novel variant of the classical two-echelon vehicle routing problem (2E-VRP), termed the two-echelon vehicle routing problem with dual-customer satisfaction (2E-VRP-DS) (i.e. time windows satisfaction and freshness satisfaction) in community group-buying. It is important to obtain better solutions for the 2E-VRP-DS with long-distance distribution in the first echelon and last-mile delivery in the second echelon. Therefore, a new mathematical model is established for the 2E-VRP-DS that incorporates objectives: minimising the total distribution costs, and maximum dual-customer satisfaction (time windows satisfaction, and product freshness satisfaction). To solve the mathematical model, an efficient adaptive genetic hyper-heuristic algorithm (AGA-HH) was proposed, complemented by a k-means clustering approach to generate initial solutions. The adaptive genetic algorithm is considered to be a high-level heuristic, and ten local search operators were considered as low-level heuristics to expand the search region of the solution and achieve robust optimal results. Three sets of experiments were conducted, and the results demonstrated the superiority of AGA-HH in solving the 2E-VRP-DS, showing improvements in distribution costs reduction, time windows compliance, and product freshness preservation. Moreover, sensitivity analyses were carried out to show the influence of the number of DCs and the tolerance range of product freshness, discovering some managerial insights for companies. Future work should consider and investigate VRPs in other new business modes.

中文翻译：

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一种针对社区团购中具有双重客户满意度的两梯队车辆路径问题的自适应遗传超启发式算法


本研究重点研究了社区团购中经典双梯队车辆路径问题 （2E-VRP） 的一种新变体，称为具有双重客户满意度的双梯队车辆路径问题 （2E-VRP-DS） （即时间窗满意度和新鲜度满意度）。重要的是为 2E-VRP-DS 获得更好的解决方案，第一梯队是长距离配送，第二梯队是最后一英里交付。因此，为 2E-VRP-DS 建立了一个新的数学模型，该模型包含了以下目标：最小化总分销成本和最大化双重客户满意度（时间窗口满意度和产品新鲜度满意度）。为了解决数学模型问题，提出了一种高效的自适应遗传超启发式算法 （AGA-HH），并辅以 k-means 聚类方法来生成初始解。自适应遗传算法被认为是一种高级启发式算法，十个本地搜索运算符被视为低级启发式算法，以扩大解决方案的搜索区域并获得稳健的最优结果。进行了三组实验，结果表明 AGA-HH 在解决 2E-VRP-DS 方面具有优势，在降低分销成本、时间窗口合规性和保持产品新鲜度方面有所改善。此外，还进行了敏感性分析，以显示 DC 数量和产品新鲜度容忍范围的影响，为公司发现了一些管理见解。未来的工作应该考虑和研究其他新商业模式中的 VRP。