Logistics Optimization for Online Community Group Buying in Emerging O2O Business Modes

Abstract

This article addresses a critical logistics optimization challenge in the online community group buying (OCGB) business mode, where the stochastic release dates (SRDs) of products create inefficiencies in delivery planning. In general, vehicle routing models assume deterministic release dates (RDs), overlooking the uncertainty of RDs that is inherent in OCGB logistics. To address this shortcoming, we introduce a vehicle routing problem with SRDs and multiple products aimed at minimizing total distance-related and penalty costs. The SRDs of aggregated products affects vehicle departure times, which poses computational challenges. We address this challenge by approximating SRDs with a Gumbel distribution and introducing a quality loss cost function to model overdue penalties. The problem is first formulated as an arc-flow model and then transformed into an equivalent set-partitioning model to increase computational efficiency and provide tighter upper bounds. To solve this problem, we propose a branch-and-price algorithm based on the set-partitioning formulation, incorporating an efficient labeling algorithm to address the pricing problem and improve column generation strategies. Extensive computational experiments validate the advantages of incorporating SRDs in logistics optimization. Additionally, a real-world case study of Meituan’s OCGB operations is used to quantify the impact of SRDs on distribution decisions, providing actionable managerial insights to increase delivery efficiency in stochastic environments.