Fresh-products community group-buying delivery problem for heterogeneous customers

Online community group-buying of fresh products has emerged as a popular model in urban e-commerce. This paper studies fresh products community group buying delivery problem of multiple commodities considering customer behavior. Compared with traditional fresh products e-commerce in which each customer is distributed individually, community group buying introduces a community leader to receive fresh products from distributors and residents in this community pick up their orders from this leader. The time gap between the distributor's delivery to the community leader and the residents’ pick-up from the leader results in further deterioration of fresh products, which is the challenge in online community group-buying. We establish a distribution model considering deterioration of fresh products in refrigerated trucks and at the community leader's location, in which three types of penalty costs are used to represent heterogeneous customer behaviors. Since different residents have separated delivery time windows, prioritizing delivery for which customers must be balanced. We design a memetic algorithm for this non-linear programming. A split algorithm considering multi-commodity delivery and time-varying arc costs is designed to improve the efficiency of memetic algorithm. Experiments show that the proposed method reduces total cost by an average of 13.18% compared to a commercial solver within a fixed time budget. The case study based on data from Beijing provides management insights. Specifically, delivery routes tend to prioritize communities with a higher concentration of retired residents, while increased customer diversity is associated with lower vehicle utilization rates.