基于样本划分的数据驱动报童问题研究  

Data-driven optimization with samples partition for thenewsvendor problem

作　　者：陈碎雷[1] CHEN Suilei(International Business School,Zhejiang Industry&Trade Vocational College,Wenzhou 325003,China)

机构地区：[1]浙江工贸职业技术学院国际商贸学院,浙江温州325003

出　　处：《管理工程学报》2024年第3期161-171,共11页Journal of Industrial Engineering and Engineering Management

基　　金：浙江省科技厅软科学项目(2020C35029);浙江省教育厅“十三五”教学改革研究项目(jg20190893)。

摘　　要：大数据时代的到来为企业库存管理带来了前所未有的机遇,同时也对其相应的决策方法提出了新的挑战。高效利用实时市场数据支撑库存管理智能决策已经成为企业提升库存管理效率的关键。传统库存管理研究是基于需求预测的基础上再做库存决策。而近年来流行的数据驱动库存管理方法是跳过需求预测的过程,直接建立需求数据与库存决策的关系。本文研究数据驱动的经典报童问题,其随机需求分布是未知的,报童只有若干期历史的需求数据。本文基于数据样本划分提出了新的数据驱动鲁棒优化方法来解决报童的订货问题。该方法通过将需求数据样本划分成不同的区间来构造随机需求分布的模糊集,从而求解相应的鲁棒优化问题。理论证明了该方法随着样本量的增加,其解收敛于最优解。数值实验表明相对已有的鲁棒方法,新方法具有相对弱的鲁棒性,但平均性能更好,并且计算复杂度相对小很多。同时表明本文提出的样本划分鲁棒方法比SAA等数据驱动方法具有更好的平均性能和更强的鲁棒性。The advent of the era of big data has brought unprecedented opportunities for enterprise inventory management,but also has raised new challenges to its corresponding decision-making methods.On the one hand,the richness and availability of data in the era of big data has brought rich information to support enterprise’s decision-making,such as real-time customer behavior data and product-related data owned by large e-commerce platforms.How to efficiently use big data to support inventory management has become a new growth point for enterprises.On the other hand,the big data method has promoted the transformation of research paradigm in the field of inventory management.Traditional inventory management typically assumes that the demand distribution function is known.However,the real data is usually massive,multi-dimensional and unstructured,which raised new big challenges for statistical methods,and it becomes more difficult to estimate and predict.In the context of big data,the research paradigm of inventory management has also undergone significant changes,from traditional model driven to data-driven decision-making.Data driven decision-making is to skip the link of demand forecasting and directly establishes the relationship between data and decision-making.Efficient use of real-time market data to support intelligent decision-making for inventory management has become the key for enterprises to improve the efficiency of inventory management.This paper studies the classical newsboy problem,that is,newsboy needs to determine the order quantity in each period to meet the random demand.The random demand distribution is unknown,and newsboy has only several periods of historical demand data.The goal of newsboy is to minimize inventory costs,including shortage costs and inventory holding costs.Based on the data sample partition,this paper proposes a new data-driven robust optimization method(DROSP)to solve the newsboy ordering problem.DROSP method divides the demand data samples into different intervals to construct

关 键 词：库存管理 数据驱动 鲁棒优化 报童问题 

分 类 号：O227[理学—运筹学与控制论]