基于可控提前期和延迟供货的短生命周期产品库存模型

本文用斜坡型函数来描述短生命周期产品需求变化特征,用持有成本不断增加的形式来表示存货的无形变质所带来的损失,通过向供应商支付额外的赶工成本来缩短提前期,同时在允许缺货的情况下设置合理的缺货期价格折扣来控制订单的流失,即以价格折扣的损失来挽留住更多的顾客,使其总成本到达最优。本文在此基础上建立了短生命周期产品的库存模型,并提出了最优解的求解方法。     

短生命周期产品的三种库存模型的比较

讨论短生命周期产品库存模型,考虑无形变质因素,并假定无形变质率与需求率成反比关系.在线性需求和常数生产率的条件下,假设,若需求率为1,则视作产品的市场生命周期结束.并且,在产品生命周期的前期不存在缺货的情况下,给出了短生命周期产品分别在理想状态、允许缺货以及价格折扣导致需求率变化等3种状态下的库存模型,最后在相同参数条件下对这3种模型进行比较,找出了单位时间内平均总成本最小的最优库存模型,厂商可以根据自身的情况选择合适的库存策略.     

基于短生命周期产品的供应链订货策略的博弈研究

本文针对短生命周期产品的特点,利用短生命周期产品订货的特殊性,分析了简单的两级供应链的订货机制.构建了当供应商以履行订单时间为决策变量和零售商以是否提前订货和自身促销努力水平为决策变量时,供应链上下游之间的博弈模型.通过分析双方之间的利益关系,供应商修正初始决策从而得到均衡解,并以契约方式提出了有效的激励措施使零售商增大促销努力水平从而加大了订货量,实现了供应链中双方利益最大化.     

零售商主导的短生命周期产品供应链订货策略

针对短生命周期产品的特点,探讨了当零售商为供应链的核心主导商时,由供应商和零售商构成的短生命周期产品二级供应链的订货决策模型.当零售商占主导地位时,零售商则会通过竞价手段将供应商的产品订购价格压制在比较低的水平,并且根据供应商的报价来选择向其订货的数量.同时由于零售商直接面临消费群体,因而可以通过增加营销信号帮助供应商消除产品积压的现象.运用博弈论来分析供应链中各利益主体在不同条件下的决策与利益分配关系,并以契约方式提出了有效的激励措施,最后进行了算例分析和验证.     

短保质期变质产品的两次订货策略研究

一类短保质期变质产品,如生鲜蔬菜、水果等,增加其库存展示量能为顾客提供更多的拣选机会,从而刺激顾客增加购买量.对此,在综合分析已有变质库存基本模型的基础上,建立了一个更能准确反映当前存货水平和需求率、变质率的关系的库存模型.考虑到计划期可能不是最优订货周期的整数倍,从一般意义上研究了基于保质期约束和计划期可修正的变质产品最后两次订货策略.结论表明,最优订货周期须超过一个保质期.在计划期的一定变动范围内,订货周期越短越有利于增加利润和增强经营的社会外部性,并在计划期的上界达到最优;超过计划期的合理变动范围,则须将不完整订货周期控制在一个保质期内,且越接近保质期越好,否则随着计划期延长,变质量增加而利润却逐渐降低.     

混合分销渠道结构下短生命周期产品供应链库存策略分析

随着竞争的加剧,产品生命周期日渐缩短。信息和网络技术的不断进步,使得网络作为一种特殊的分销渠道出现。传统分销渠道和网上直销渠道并存的混合分销渠道结构给理论研究和管理实践提出了新的挑战,本文针对混合分销渠道结构下短生命周期产品供应链,运用报童问题的框架,分析了两种不同运作模式下生产商和零售商库存策略,并通过数值实验研究了需求不确定性对生产商和零售商最优库存策略的影响。最后,根据数值实验的计算结果,总结了本研究的管理启示。     

部分短缺量拖后下基于顾客敏感度的补货策略适用范围研究

顾客对缺货数量和等待时间的敏感度影响着部分短缺量拖后率。基于此,根据顾客对缺货数量和等待时间的敏感度以及成本结构,对部分短缺量拖后下不同补货策略的适用范围进行了研究,得出以下结论:(s,S)连续性检查策略适用于单位缺货和丢单成本较高的库存系统;(t,S)周期性检查策略和(t,s,S)混合策略适用于单位缺货和丢单成本较低的库存系统;在单位缺货和丢单成本较低的库存系统中,当顾客对缺货数量较敏感时,(t,s,S)混合策略的运作成本更低,否则(t,S)周期性检查策略更适用,而顾客对等待时间的敏感度对补货策略适用范围的影响不明显。     

短生命周期产品供应链中供需双方合作的价值研究

本文研究了只有一个销售商和制造商构成的只有一个短生命周期产品的两层供应链系统,当市场需求和制造商履行订单的时间都是随机变化的,制造商可通过一定方法提前履行订单,建立了制造商与销售商合作与共享信息的一般模型,解析分析了信息共享的价值。解析分析和算例仿真表明,双方合作不但能增加供应链系统的利润,保持利润增加的稳定性,而且还能增加产品对市场的供给量。     

短缺量拖后系数不相同且需求时变的供应商管理库存模型

提出了一种需求和产品价格随时间变化且存货影响销售率条件下,短缺量拖后系数与供应商生产状况相关,各补货周期服务水平和周期长度不相同的供应商管理库存模型.数值仿真寻优证明模型存在唯一最优解.主要参数灵敏度分析显示,需求增长因子和产品销售价格变化因子对计划期内供应链总利润的影响大于短缺量拖后系数.企业应根据需求和销售价格以及短缺量拖后系数的大小,调整各补货周期的长度和服务水平.     

考虑批量滞后支付的变质物品EOQ模型

考虑订货商在信用期间将货款用于投资,提出了一种批量滞后支付且短缺量部分拖后的变质物品EOQ模型.证明了单位时间利润函数在一定条件下是订货期长度的上凸函数.仿真寻优和灵敏度分析表明,滞后支付对单位时间利润的影响主要来自投资收益的变化,服务水平、变质系数和短缺量拖后系数对订货商物料成本影响较大,对其它成本及单位时间利润影响较小,且短缺量拖后系数比服务水平和变质系数的影响更大.     

考虑组合预测股价的泛证券投资组合选择策略北大核心CSCD

Online portfolio selection is regarded as an important research issue in the field of quantitative finance, which often aims to maximize returns or risk-adjusted returns. Mean-variance model, a classic portfolio model, assumes that the returns on assets obey a certain probability distribution, which characterizes the return and risk by calculating the mean value and covariance matrix of the portfolio, respectively. However, it is difficult to accurately obtain the future return or return distribution of assets, and the only information that can be accurately grasped is historical price data. Therefore, some scholars try to use only historical information to construct portfolio strategy, so they pay more and more attention to online portfolio selection problem. The so-called “online” means that when making decisions in the current period, the updated investment proportion only depends on the historical data obtained up to the beginning of the current investment, and the cycle is carried out until the end of the whole investment. Stock price prediction based on past information is one of the key problems of online portfolio selection without statistical assumption. In this paper, historical price data are used to predict the stock prices, and then a new online portfolio selection strategy is constructed. In the first part of this paper, we design a new online portfolio selection strategy based on the predicted stock prices with the goal of maximizing expected returns. First of all, in order to minimize the influence of market outliers or white noise, we adopt multiperiod historical price information to predict the stock prices for the next period. Secondly, in order to reduce the prediction bias caused by a single prediction model, the exponential smoothing method and L1-median estimation method are combined to construct a combination forecasting model. Then, the stock estimator can be obtained based on the above-mentioned combination forecasting model. Finally, a new online portfolio selection strategy named Combination Forecasting for Exponential Gradient (CFEG) is proposed by taking the maximization of expected return as the goal and adding a penalty term into the objective function to reduce the transaction costs caused by each transaction adjustment. In the second part, the competitive ratio analysis is adopted to analyze the competitive performance of the proposed strategy theoretically, and the Best Constant Rebalanced Portfolios (BCRP) strategy is regarded as a straw man. After a series of derivations, it is proven that the average logarithmic growth rate of CFEG strategy is asymptotically consistent with that of the BCRP strategy, namely, the proposed strategy CFEG is a universal strategy. In the third part, numerical examples are conducted to test the performance of the proposed strategy in terms of final cumulative wealth, statistical t-test, Sharpe ratio, Calmar ratio, transaction cost sensitivity, and other parameter sensitivities. It is necessary to test the performance of our proposed CFEG strategy. Therefore, this paper further demonstrates the performance of CFEG through numerical experiments related to 8 real stock market datasets in China and the United States. First of all, the most important indicator to judge the performance of a strategy is its final cumulative wealth. We compare the final cumulative wealth between the CFEG strategy with 3 benchmark strategies and 6 related online strategies, and compare the difference of the average logarithmic growth rate between CFEG strategy and BCRP strategy. On 8 datasets, the final cumulative wealth of CFEG strategy is stably higher than that of all online strategies, and the difference of the average logarithmic growth rate between CFEG and BCRP is almost zero. The CFEG strategy has a good performance on the whole, and the p-value is very small on each dataset in the statistical t-test. Secondly, the Sharpe ratio and Calmar ratio of CFEG strategy are compared with other strategies. The results show that CFEG strategy can better balance the returns and risks, and obtain higher risk-adjusted returns. Since the transaction costs are an important realistic constraint, the sensitivity analysis of the transaction cost rate of CFEG strategy is carried out subsequently. Meanwhile, 4 strategies are also selected for the purpose of comparison. The results show that CFEG strategy can withstand reasonable transaction costs and still obtain high returns. Finally, we conduct the sensitivity analyses of 3 parameters included in the design of CFEG strategy. The results show that the proposed CFEG strategy is stable and insensitive to parameter selection. Although the best parameter values are not selected, the CFEG strategy maintains excellent performance. Therefore, effective parameters can be selected easily in practical applications. In conclusion, the proposed strategy CFEG is suitable for investors to make investment decisions effectively and efficiently. The CFEG strategy is able to update the investment proportion in time without the future stock price information, so as to achieve the goal of maximizing returns, and provide some guidance for online investors. © (2023). All Rights Reserved.     

A further appreciation of demand forecasting models: Some methods based on survey information

This paper deals with the data available from survey sources which primarily provides cross-sectional statistics that can be applied in forecasting models. It is intended as a complement to an earlier paper by the authors which appeared in the September 1973 edition of Long Range Planning.     

需求信息预测对制造商回收再制造策略的价值

新能源汽车及其加能站的扩散是多种主体相互作用的复杂过程。现有的分析该过程的基于agent的模型没有考虑现实的路网结构和驾车者行驶路线的分布,不能为具体某一城市或地区新能源汽车的推广提供决策支持。本文探索了一种集成ABM(基于agent的建模)与GIS(地理信息系统)的技术实现方法,其核心思想是利用Repast仿真框架对图形文件的读写接口,建立其与ArcGIS间的通信,形成一种松耦合的、基于矢量的集成方式。应用该集成方式,本文以上海市为例构建了基于城市路网结构的新能源汽车及其加能站交互扩散的仿真平台。     

需求预测信息共享对供应商入侵策略的影响

考虑由一个供应商和一个风险规避的零售商组成的二级供应链． 在零售商拥有私有需求信息和供应商具有开通直销渠道能力情形下,得到了不同策略下供应链成员的最优决策及期望效用,探讨了需求预测精确度及风险规避程度的影响,分析了信息(不) 共享下供应商入侵对供应链成员效用的影响,进一步得到了零售商和供应商之间的均衡策略及其条件． 研究发现: 预测精确度的提升(或零售商风险规避程度的降低) 能够增加供应链成员的效用; 供应商入侵会产生“输 － 赢”、“输 － 输”、“赢 － 输”、“赢 － 赢”四种可能结果; 零售商的最优信息共享策略与基本需求、自身销售优势以及风险规避程度密切相关; 当且仅当基本需求不太高且零售商具有较强的销售成本优势时,零售商才会主动共享信息以诱使供应商开通直销渠道,并且风险规避程度越高,零售商越有可能共享需求信息; 当入侵成本很高时,供应商仍有可能战略性地开通直销渠道．最后,数值分析表明,只有当零售商的销售成本不是太高时,他才有可能共享需求信息．     

基于日度低频价格的波动率预测

利用作者提出的GARCH-X的框架,将以往文献中提出的各种基于金融资产的最高、最低、开盘和收盘等低频价格信息的波动率静态估计,统一地扩展成对波动率的动态预测模型.通过对上证指数近十几年数据的实证分析,并借助于对波动率的高频估计和预测评估的一些最新研究成果,本文揭示出合理地利用价格极差及开盘价的信息可以显著地提高对波动率及风险价值的预测能力.     

基于战略顾客行为的质量差异化产品定价策略

采用博弈论方法构建竞争企业两周期动态定价模型,研究基于战略顾客行为的质量差异化产品最优定价策略,并分析基于行为的定价(behavior-based pricing,BBP) 对企业利润的影响. 研究结论表明：1)企业相对生产效率和决策顺序是影响BBP策略选择的关键因素;2)当竞争企业相对生产效率差异较大时,BBP策略保护弱势企业利润并且损害强势企业利润,从而加剧行业竞争;3)拥有信息优势的企业单独采用BBP策略时,不仅自身利润降低,也会损害竞争对手利润,从而导致“损人不利己”的双输局面,这与直觉相悖. 最后,选择实际企业数据对本文的模型和结论进行说明验证.     

面向客户个性化需求的交互式遗传算法

针对交互式遗传算法(IGA)中的评价噪声问题,提出犹豫度的概念,建立犹豫度调整机制,并使用删除策略和修改策略来处理形成初始种群以及交叉、变异过程中产生的约束不满足个体.通过对汽车操控台的概念设计问题进行建模,建立人性化交互界面用以验证本论文提出的方法体系的先进性和合理性.实验表明,此求解算法能够有效的降低评价噪声,加速收敛,降低疲劳度,提高结果的满意度.