大数据驱动的决策范式转变——以个性化O2O即时物流调度为例  

Paradigm shift for big data-driven decision making:New paradigm for O2O on-demand logistics

作　　者：代宏砚[1] 陶家威 姜海[2] 周伟华[3] DAI Hong-yan;TAO Jia-wei;JIANG Hai;ZHOU Wei-hua(Business School,Central University of Finance and Economics,Beijing 100081,China;Department of Industrial Engineering,Tsinghua University,Beijing 100084,China;School of Management,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]中央财经大学商学院,北京100081 [2]清华大学工业工程系,北京100084 [3]浙江大学管理学院,杭州310058

出　　处：《管理科学学报》2023年第5期53-69,共17页Journal of Management Sciences in China

基　　金：国家自然科学基金资助重大研究计划培育项目(91646125);国家自然科学基金资助项目(72172169);国家自然科学基金资助重大项目(72192823);中央高校基本科研业务费专项资金资助项目。

摘　　要：大数据环境下传统的决策范式正转变为基于数据的决策范式,本文以O2O即时服务这种新型商业模式为情景,研究大数据驱动的新决策范式下的O2O即时物流调度模型.本文跨域融合物流部门、其他运营部门以及外部环境信息构成全景式数据,同时放宽传统决策范式的经典假设,实现从无差异化配送时间到个性化配送时间的转变,以及需求服从先验分布到考虑未来需求时空属性的转变.本文融合机器学习和运筹优化方法,实现新决策范式下的O2O即时物流调度模型.在预测层面,构建个性化众包配送时间预测模型和基于订单集时空相似性的需求场景预测算法;在决策层面,同时考虑个性化预测模型的点估计及其不确定性,并考虑未来订单集的时空分布,构建考虑预测不确定性的调度模型,同时设计同步预测和决策算法求解.本文与中国主流的O2O商超平台合作,通过基于真实数据的模拟仿真,验证了新决策范式下的O2O即时物流调度模型的可行性和有效性.相较于传统的决策范式,本文提出的模型能实现更精准的供需匹配,降低延误订单数、平均配送时间和配送成本.The rapid development of big data has shifted the traditional decision-making to data-driven decision-making.This research aims to propose an O2O on-demand logistics scheduling model based on the new big data-driven paradigm.This model incorporates different data sources from the internal logistics departments,other operations departments and external environment to form a panoramic dataset.Based on this,the classical assumptions of the traditional decision-making paradigm are relaxed,and two new assumptions are proposed:Personalized delivery time is assumed instead of traditional consistent delivery time,and a temporal-spatial demand distribution considering future demand is assumed instead of the prior demand distribution.This research aims to realize the personalized O2O on-demand delivery management by applying both machine learning and operations research technologies.More specifically,a personalized delivery time forecast model and a scenario-based demand forecast algorithm are proposed.Considering the point estimate and forecast uncertainty of the delivery time forecast model,as well as the temporal-spatial distribution of future orders,a dispatch model is established which takes into account forecast uncertainties for O2O on-demand logistics systems.A forecast-while-optimizing algorithm is also developed to optimize the decision-making model based on the feature-dependent predictions.This research verifies the feasibility and effectiveness of our O2O on-demand logistics management model based on the new paradigm by analyzing a real dataset from one of the largest O2O platforms in China.Compared with the traditional mode,the model based on the big data-driven paradigm can precisely match the highly uncertain demand and supply,and reduce the number of delayed orders,average delivery time and delivery cost.

关 键 词：决策范式转变 大数据驱动的决策 O2O即时物流 个性化 

分 类 号：F224.3[经济管理—国民经济]