Bundle price, buyer’s preference and shipping cost: forming buyers’ groups for bundles of products

Group buying is attracting a lot of attention, but studies on group buying of bundled products are still in their infancy. In this study, a group purchasing structure is proposed in which each purchaser needs a portion of the items in a bundle and has his or her preferences. However, when the buyers of a bundle are scattered across different locations, the shipper must travel long distances between buyers, which increases shipping costs. Therefore, besides bundle prices, shipping costs are an inseparable expenditure for group purchasing. It is assumed that shipping costs increase on certain days, e.g. around Christmas. A tailored mathematical formulation is formulated to maximize the total group saving. Considering the complexity of the model, a genetic algorithm (GA) is developed as a solution algorithm, and compared with the CPLEX solver. The results confirm the performance of the GA, which provides up to 24% better solutions than CPLEX.

团购正受到广泛关注，但针对捆绑产品团购的研究仍处于起步阶段。本研究提出一种团购架构：每位采购者仅需捆绑套餐中的部分商品，且拥有各自的偏好。然而，当捆绑套餐的采购者分布于不同地区时，配送商需在各采购者间长途跋涉，从而推高了配送成本。因此，除套餐定价外，配送成本亦是团购中不可或缺的支出项。本研究假设，配送成本会在特定时段（如圣诞节前后）出现上涨。本文构建了定制化的数学规划模型，以最大化团购总节省金额。考虑到该模型的复杂性，本文设计了遗传算法（Genetic Algorithm，GA）作为求解算法，并与CPLEX求解器进行对比。实验结果验证了该遗传算法的性能：其求解结果相较于CPLEX最高可提升24%的优化效果。