供应中断下具有随机需求的闭环供应链最优差别定价模型

研究了在供应中断下具有随机需求的闭环供应链系统的最优差别定价模型.基于博弈论的理论和方法分别在集中式和分散式决策情形下,确定了最优批发价、最优销售价、最优订购量及系统利润.最后通过数值例子对最优差别定价模型进行了实证分析.     

物价、利率与收入对居民消费需求影响研究——基于时变参数状态空间模型

运用时变参数状态空间模型对我国改革开放三十年来物价、利率与收入对农村和城镇居民消费需求影响的动态特征进行了研究。发现物价、收入对农村和城镇居民的消费需求弹性不同,农村消费需求受收入影响较大,而城市居民消费需求受物价影响较大;利率对农村和城镇居民消费需求影响不显著,利率机制目前还不是调解中国消费需求的理想工具。在此基础上给出了相应的政策建议。     

中国农村居民消费结构的Panel Data模型分析

基于持久收入假设消费理论,建立中国农村居民消费的Panel Data模型,分析中国农村居民不同收入层次之间的消费差异,以及这种差异对农村居民消费结构的影响.结果表明:不同收入层次的农村居民的总消费支出差异显著,衣着消费模型为变截距模型,而居民食品、居住等其他消费支出差异不显著,模型表现形式一致,因此启动农村居民消费需求归根到底是要提高低收入层次居民的收入,拉动高收入层次居民的消费需求。     

基于场景理论的中国城市居住房地产需求研究

源自芝加哥城市社会学派的场景理论最先发现并研究了文化和价值观对城市居民居住需求的影响力正在逐渐增强这一趋势.在当今我国城市,一些富含文化、价值观背景的特殊居住现象,如房奴、蜗居及蚁族也都客观呈现出了这类趋势的日益增强.将场景理论应用到中国城市居住房地产需求的研究中,通过文献和理论分析,提出了文化、价值观因素对中国城市居民的居住房地产需求影响的三个假设,然后基于场景理论模型构建了基于中国375个城区的区域场景文化因子,接着运用分层回归方法,对地区不同年龄层次人群的居住房地产需求进行了实证分析.最终证明了三个假设,并以此提出了相应的政策建议.     

二三产业劳动力需求与产值的关系及其预测

在分析第二、三产业产值与劳动力需求关系相关性的基础上,构建了我国第二、三产业产值与劳动力需求的关系模型,并运用基于拉格朗日乘子法的组合预测方法,对未来九年我国第二产业第三产业劳动力需求进行预测.研究结果表明:第二产业、第三产业的产值和劳动力需求呈现对数函数和线性函数回归模型的趋势.基于此组合预测模型实现了对未来九年第二、三产业劳动力需求的预测,且预测误差小于3.905928%、2.1279%,具有良好的预测精度.该研究不仅为进一步研究二三产业劳动力需求的预测打下了理论基础,也有助于我国各级政府制定相关的发展目标和决策.     

中国碳排放与能源消费关系实证研究——基于VEC模型

中国碳减排问题成为世界关注的焦点问题.研究碳排放与能源消费关系有助于实现2020年碳减排目标.选取1953-2008年中国碳排放和能源消费数据,运用协整理论、向量误差修正模型(VEC)和Granger因果关系分析,对中国碳排放量与能源消费之间的相互关系进行实证研究.研究结果表明:碳排放与能源消费之间存在协整关系,能源消费增加1%,碳排放将增加0.9646%,即碳排放对能源消费的长期弹性为0.9646.从短期误差修正模型来看,碳排放与能源消费之间具有动态调整机制.非均衡误差项的存在,能够保证两者之间长期均衡关系的存在.Granger因果关系研究表明:碳排放与能源消费之间互为双向因果关系.通过脉冲函数和方差分解分析了模型的动态特征.根据研究结果,提出降低能源消费和减少碳排放对策.     

基于改进营销收益模型的顾客资产测量——在银行业的应用研究

测量顾客资产是提升顾客资产的基础和前提。为实现对顾客资产的有效测量,本研究在顾客资产驱动要素模型的四维构成基础上,对顾客资产测量的营销收益模型进行了改进,并通过建立计量经济模型解决了需求市场规模变化问题,最后应用主成分分析和Logistic回归分析对顾客资产进行计算。在银行业的应用研究结果表明,改进后的模型能够考虑需求市场规模变化、交叉购买和口碑宣传等要素对顾客资产价值的影响,而且操作简单,可以通过统计软件完成全部计算过程。     

三种物资同时段需求的EOQ模型

本文研究了三种物资同时段需求的EOQ模型,从理论以及实例均说明了该模型相对单一物资的EOQ模型,能够缩小这三种物资对仓库的占用空间,该模型在实际问题中要求需求和采购价格均随着时间的变化而变化,通过算法计算得到了满足三种物资同时段需求的最佳采购次数,最后得出了所要建立满足需求的最小仓库容量．     

Vasicek利率模型下带有零息票债券的投资-消费模型

应用随机最优控制理论研究Vasicek利率模型下的投资-消费问题,其中假设无风险利率是服从Vasicek利率模型的随机过程,且与股票价格过程存在一般相关性.假设金融市场由一种无风险资产、一种风险资产和一种零息票债券所构成,投资者的目标是最大化中期消费与终端财富的期望贴现效用.应用变量替换方法得到了幂效用下最优投资-消费策略的显示表达式,并分析了最优投资-消费策略对市场参数的灵敏度.     

工业化、城镇化进程中电力需求分析及预测

随着工业化、城镇化进程的不断加快,我国电力需求量将持续上升。电力的充足供应是我国经济稳步发展的重要保证,故合理准确的对电力需求进行分析及预测具有重要的现实意义。基于此,分析我国电力需求现状,利用通径分析筛选电力消费需求的核心驱动因素。在模型选择的基础上,基于单变量(ETS、ARIMA模型)和多变量(情景分析)两个维度进行电力需求量分析及预测。结果表明:GDP每提高1%使得电力需求量提高0.5249%;工业化水平每提高1%使得电力需求量提高2.2146%,城镇化水平每提高1%使电力需求量相应提高1.0076%。“十二五”末中国电力消费需求量将近61425.96KW/h,2020年中国电力消费需求将近81410.10KW/h。     

模糊需求下订购批量问题的研究

库存管理模型在现实生活中有着广泛的运用,它为管理决策者有效地确定最佳订购批量提供帮助.然而,由于历史数据的缺乏,需求量在很多情况下往往被主观地确定,因而带有一定的模糊性.本文针对两种不同类型的模糊需求:离散型与连续型,运用模糊理论分别建立了相应的模糊库存模型.该模型不同于已有的模糊库存模型如下:在现有的模糊库存的文献中,大多采用的是利用模糊集的知识对确定EOQ模型加以研究,而本文从模糊理论的角度对报童问题进行研究.     

面向电子商务的应用——基于多Agen t的消费者需求代理系统

首先根据电子商务环境下消费者需求的特点,在对多A gen t技术论述的基础上,建立了基于多A gen t的消费者需求代理系统.接着对系统具体工作流程进行了深入分析:先针对不同消费者建立相应的用户模型,以便为不同消费者提供效用最大化的产品或产品服务的组合,然后采用协商型A gen t,完成网上产品或服务的交易.最后提出了基于遗传算法的协商谈判策略,以提高消费者需求代理系统的协商能力.     

价格和库存均影响需求、部分拖后供给的易变质品生产库存

考虑产品的需求率受库存和销售价格的影响,拖后供给因子与需求得到满足的实际等待时间有关,建立了易变质品生产库存模型,给出了寻求最优生产策略和销售价格的方法,并分析参数变化对于平均利润、销售价格和服务率的影响.     

自我概念结构与女性旅游消费行为的实证研究

本文结合旅游消费情况,把女性自我概念结构应用于女性旅游消费,通过对女性旅游消费者的调查问卷并对问卷结果进行因子分析、相关分析,从而有效的揭示女性旅游消费行为的特点。     

Oligopoly with Hyperbolic Demand: A Differential Game Approach

Convex demand functions, although commonly used in consumer theory and in accordance with a large amount of empirical evidence, are known to be problematic in the analysis of firms’ behavior; therefore, they are rarely used in oligopoly theory, due to the possible lack of concavity of the firms’ profit functions and the indeterminacy arising in the limit as marginal costs tend to zero. We investigate a dynamic oligopoly model with hyperbolic demand and sticky price, characterizing the open-loop optimal control and the related steady-state equilibrium, to show that the indeterminacy associated with the limit of the static model is indeed confined to the steady state of the dynamic model, while the latter allows for a well-behaved solution at any time during the game. Although the feedback solution cannot be analytically attained since the model is not built in linear-quadratic form, we show that analogous considerations also apply to the Bellman equation of the individual firm.     

Ordering and pricing decisions in a two-echelon supply chain with asymmetric demand information

This article examines coordinated decisions in a decentralized supply chain that consists of one supplier and one retailer, and faces random demand of a single product with a short life cycle. We consider a setting where the retailer has accurate demand information while the supplier does not. Such a problem with asymmetric demand information can be viewed as an extension of the newsboy problem in which both the supplier and the retailer possess the same demand information. Combining the mechanism of sharing demand information and that of quantity discount and return policy enables us to develop three coordinated models in contrast with the basic and uncoordinated model. We are able to show the ordinal relationship among the retailer’s optimal order quantities in these four models under a general form of random demand, and compare the supply chain profits and conduct sensitivity analysis analytically in four models under uniform random demand. We also provide numerical results under normal random demand that bear a resemblance to those under uniform random demand.     

Estimating retail demand with Poisson mixtures and out‐of‐sample likelihood

Estimation of retail demand is critical to decisions about procuring, shipping, and shelving. The idea of Poisson demand process is central to retail inventory management and numerous studies suggest that negative binomial (NB) distribution characterize retail demand well. In this study, we reassess the adequacy of estimating retail demand with the NB distribution. We propose two Poisson mixtures—the Poisson–Tweedie family (PTF) and the Conway–Maxwell–Poisson distribution—as generic alternatives to the NB distribution. On the basis of the principle of likelihood and information theory, we adopt out‐of‐sample likelihood as a metric for model selection. We test the procedure on consumer demand for 580 stock‐keeping unit store sales datasets. Overall the PTF and the Conway–Maxwell–Poisson distribution outperform the NB distribution for 70% of the tested samples. As a general case of the NB model, the PTF has particularly strong performance for datasets with relatively small means and high dispersion. Our finding carries useful implications for researchers and practitioners who seek for flexible alternatives to the oft‐used NB distribution in characterizing retail demand. Copyright © 2013 John Wiley & Sons, Ltd.     

A note on the inventory models for deteriorating items with ramp type demand rate

In this research we study the inventory models for deteriorating items with ramp type demand rate. We first clearly point out some questionable results that appeared in (Mandal, B., Pal, A.K., 1998. Order level inventory system with ramp type demand rate for deteriorating items. Journal of Interdisciplinary Mathematics 1, 49–66 and Wu, K.S., Ouyang, L.Y., 2000. A replenishment policy for deteriorating items with ramp type demand rate (Short Communication). Proceedings of National Science Council ROC (A) 24, 279–286). And then resolve the similar problem by offering a rigorous and efficient method to derive the optimal solution. In addition, we also propose an extended inventory model with ramp type demand rate and its optimal feasible solution to amend the incompleteness in the previous work. Moreover, we also proposed a very good inventory replenishment policy for this kind of inventory model. We believe that our work will provide a solid foundation for the further study of this sort of important inventory models with ramp type demand rate.     

Municipal Water Demand Forecasting: Tools for Intervention Time Series

This article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.