【作者】 王健；

【导师】 胡鹏；

【作者基本信息】 华中科技大学 ， 管理科学与工程， 2020， 硕士

【摘要】 本文主要研究的是风险厌恶型零售商的订货决策问题。在生产经营中,零售商往往趋向于规避风险,追求损失风险最小化。在此设定下,当需求分布已知时,已有大量相关研究;然而,当市场需求分布函数未知,并且订货环节中的相关成本与市场需求分布情况相关时,难以沿用传统思路,先对观测数据拟合需求分布,再确定最优订货策略,这就为实践带来了较大挑战。为解决上述问题,本文引入“数据驱动”的思想和“边学习-边优化”的理念,将数据引入模型代替传统假设,并将学习和优化两个相互独立的阶段进行有效融合,设计了用以求解需求分布函数未知情况下风险厌恶型零售商的订货策略。通过该研究,能实现零售商快速应对数据更新、动态调整决策的效果。同时本研究丰富了订货决策模型的应用范围,促进管理科学若干理论模型在生产实际中的广泛使用。本文用金融学中CVa R准则来衡量零售商面临的风险,研究了风险厌恶型零售商数据驱动的订货决策算法。本文假设市场需求分布函数未知,借助已有的SAA算法和KM算法,给出了两种在需求分布函数未知情况下求解风险厌恶型零售商的最优订货的策略。其中,SAA算法主要是利用零售商历史需求数据优化求解,KM算法主要是利用零售商历史销售数据优化求解。这是由于需求数据存在截尾效应,具有一定的不完备性。同时,本文也考虑了库存参数未知情况下的算法设计等问题,引入了“边学习-边优化”的思想,给出了在需求分布函数与单位缺货成本均未知情况下的订货算法。随后,本文通过数学推导论证了算法的收敛性,并从数值实验方面验证了算法的有效性。更多还原

【Abstract】 This thesis mainly studies the order decision problem of risk-averse retailers.In production and operation,retailers tend to avoid risks and seek to minimize the risk of loss.Under this setting,when the demand distribution is known,there has been a lot of related research.However,when the market demand distribution function is unknown,and the related costs in the ordering are related to the market demand distribution,it is difficult to follow the traditional ideas which fit the demand distribution by observed data,and then determine the optimal ordering strategy.This brings great challenges to practice.In order to solve the above problems,this paper introduces the idea of "data-driven" and the concept of "learning-optimizing",which use data to take the place of traditional assumptions,and effectively integrate the two independent stages of learning and optimization.So an order strategy was designed in this paper to solve the risk-averse retailer when the demand distribution function is unknown.Through this strategy,retailers can quickly respond to data updates and dynamically adjust ordering decisions.At the same time,this study enriches the application scope of ordering decision model and promotes the widespread use of several theoretical models of management science in production practice.This thesis uses the CVa R criterion in finance to measure the risks faced by retailers,and studies the data-driven order decision algorithm for risk-averse retailers.This thesis assumes that the market demand distribution function is unknown,with the help of SAA algorithm and KM algorithm,we gives two policies for solving the optimal order quantity of risk-averse retailers under the unknown demand distribution function.Among them,the SAA algorithm is mainly optimized using the retailer’s historical demand data,and the KM algorithm is mainly optimized using the retailer’s historical sales data.This is due to the censoring effect of demand data,which cause certain incompleteness.At the same time,this paper also considers the problem of algorithm design in the case of unknown inventory parameters,and introduces the idea of "learning-optimizing" to give an ordering algorithm when the demand distribution function and the unit out-of-stock cost are unknown.Subsequently,this paper demonstrates the convergence of the algorithm through mathematical derivation,and verifies the effectiveness of the algorithm from the numerical experiments.更多还原