Improving Multi-Aspect QoE for Large-Scale O2O Order Allocation and Distribution Problem Using Social Behavior Whale Optimization

Abstract

With e-commerce rapidly developing, the Online To Offline (O2O) business model requests high efficiency for order allocation and last-mile delivery. Focusing on the challenges associated with online, same-day, and large-scale order allocation and distribution, we formulate an online dynamic vehicle routing problem with pickup and delivery (ODVRPPD), considering the uncertainty of dynamic orders and sustainability of online reassignments to improve the Quality of Experience (QoE). A novel social behavior whale optimization algorithm (SBWOA) with state machine formulation is proposed to solve this problem and express the order closed-loop fulfillment procedure. Inspired by the social behaviors and sonar communication of whale swarms, we propose SBWOA with a double-zone coding (DZC) scheme and affinity propagation clustering (AP clustering). DZC could make real-coding optimization algorithms be used in integer-coding VRPPDs. SBWOA uses AP clustering for the pickup and delivery locations to minimize delivery distance without specifying the initial clustering center and the number of clusters. Additionally, we use the real order data from Alibaba Cloud to construct 11 test problems (including a multi-day test problem with 12925 tasks and 990 vehicles). SBWOA outperforms four compared algorithms. Moreover, the extensive experimental results demonstrate the feasibility and adaptability of our model and SBWOA.