Logistic industry is experiencing its golden era for development due to its supportive role of electronic commerce operation. Big data retrieved from electronic business information system is becoming one of core competitive enterprise resources. Data analytics is playing a pivotal role to enhance effectiveness and efficiency of operation management. Generally, a well-designed delivery routing plan can reduce logistics cost and improve customer satisfaction for online business to a large extent. According to this, literatures on improvement of delivery efficiency are reviewed in this research. In existing literatures, for instance, ant colony algorithm, genetic algorithm and other combined algorithm are quite popular for such a kind of problem. Even though some algorithms are quite advanced, they are still difficult for implementation due to different constraints and larger-scale of raw electronic commerce data obtained from information system. In this paper, an advanced ant colony algorithm, as a heuristic algorithm, is implemented to optimize planning for an asymmetric capacitated vehicle routing problem. This paper not only emphasizes on ACO algorithm improvement and avoiding premature convergence, but also implementation in a real-world e-commerce delivery, which has more practical meaning for big data analytics and operation management.

由于其在电子商务运营中的辅助作用，物流业正处于发展的黄金时期。从电子商务信息系统检索到的大数据正成为企业核心竞争资源之一。数据分析在提高运营管理的有效性和效率方面发挥着关键作用。通常，精心设计的交货路线计划可以在很大程度上降低物流成本并提高客户对在线业务的满意度。据此，本研究综述了有关提高输送效率的文献。在现有文献中，例如，蚁群算法，遗传算法和其他组合算法对于此类问题非常流行。即使某些算法非常先进，由于不同的约束条件和从信息系统获得的大量原始电子商务数据，它们仍然难以实施。本文采用一种先进的蚁群算法作为启发式算法，以优化针对非对称容量车辆路径问题的规划。本文不仅着重于ACO算法的改进和避免过早收敛，而且在现实世界中的电子商务交付中的实现，对于大数据分析和运营管理具有更实际的意义。