带全量价格折扣的供应商最低免运费订货批量研究

本文从供应商角度,首先基于经济订货批量模型建立了使供应商利润最大化的最小订货量优化模型,并给出了解析解。然后,我们又将其拓展到商品带全量价格折扣和运费带全量折扣的情形,并基于折扣函数的非连续性,运用逆向归纳法建立了最优免运费最小订货量的数值算法。最后通过算例对最优免费送货策略的有效性及使用条件进行了理论分析。     

一类供应链的Stackelberg主从对策问题研究

本文研究了供应链中的一般订货模式和协调模式,提出了一类供应链Stackelberg主从对策问题,卖方作为主方给出最小补充期策略,买方作为从方以最优库存策略响应。考虑一致价格折扣弥补买方的库存成本增加,以及买方的库存成本合理化,建立买方需求确定下的卖方成本优化模型。最后,应用遗传算法对石油分销系统Stackelberg主从对策问题离线仿真计算,得出Stackelberg主从对策均衡解。     

关于销售折扣账务处理的案例分析

企业在产品(商品)销售过程中,经常采用实物折扣方式达到促销目的。对实物折扣如何进行会计处理,实践中有多种方法并存,如体现折扣的方法、不体现折扣的方法、实物折扣视同降价的方法等。以美登高食品公司的实际案例,采用比较分析方法论证了只要运用得当,各种会计处理方法的不同选择对企业有关各方的经济利益并无实质性影响。     

需求具有价格弹性情况下协调供应链的数量折扣策略研究

本文探讨了需求具有价格弹性情况下,批量折扣和总量折扣作为单个供应商和单个零售商组成的供应链的协调机制的效率问题。分别研究了单独提供批量折扣或总量折扣时确定最优折扣策略的方法以及同时提供总量折扣和批量折扣的情形下确定最优联合折扣方案的方法。然后通过数值研究对各种折扣方案的相对效力进行评价。研究结果表明：在协调供应链方面,当需求价格弹性较高时,总量折扣非常有效;需求价格弹性较小时,批量折扣有效;而当联合运用这两种折扣策略时,供应链总能达到完美协调。     

两级供应链Stackelberg主从对策的优化模型及其应用

本文研究了供应链的协调订货模式及其特例,提出了两级供应链Stackelberg主从对策问题模型,其中卖方作为主方给出最小补充期策略,买方作为从方以最优库存策略响应。建立了求解Stackelberg主从对策问题的成本优化模型,并考虑了累积价格折扣激励和买方库存成本合理化。对一个石油分销系统Stackelberg主从对策问题应用遗传算法离线仿真计算,得出Stackelberg均衡解,达到帕累托最优。     

一种新的计算组合预测权重的方法

建立了基于最小二乘原理的组合预测模型,提出了求解此组合预测模型的一种新的算法,即Frank-Wolfe算法,并将其应用于实证研究.结果表明,Frank-Wolfe方法用于求解组合预测问题的权重时,是一种行之有效的方法.     

基于供需协调的批量折扣模型研究

本文对供应链中的供需双方进行考虑，在传统的需方确定订货批量的前提下，通过价格折扣作为激励措施以保证不增加需方的成本，来影响需方改变订货批量，从而做到供方成本的最小化。并根据建立的模型，利用数值实验来进行验证和分析。     

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。     

基于马尔可夫链的企业应收账款管理

随着市场经济的发展,企业通过商业信用来促进销售,扩大销售收入,以期提高自己的竞争能力,但同时大量的应收账款却造成了企业资金周转困难,坏账带来的损失也越来越大。如何预测企业的应收账款,进而有效的对企业的应收账款进行管理就成为了企业经营管理的一个重点。本文将通过齐次马尔可夫链的理论与方法构建企业应收账款发生坏账的预测模型,并用该模型对企业的现金折扣的制定以及追讨欠款的决策制定提出建议,以有效的减少坏账损失从而提高企业的利润。     

不同折扣方式销售的税收筹划案例分析

企业在促销活动中,往往采取各种各样的折扣方式.在不同的折扣方式下,其税负会有所不同.这就为企业选择不同的折扣方式提供了税收筹划空间.纳税人应当依据税法相关规定,在达到促销目的的同时,对折扣方式进行合理的选择,以实现最大的经济效益.     

Revenue Management for Intermodal Transportation: The Role of Dynamic Forecasting

We study a joint capacity leasing and demand acceptance problem in intermodal transportation. The model features multiple sources of evolving supply and demand, and endogenizes the interplay of three levers—forecasting, leasing, and demand acceptance. We characterize the optimal policy, and show how dynamic forecasting coordinates leasing and acceptance. We find (i) the value of dynamic forecasting depends critically on scarcity, stochasticity, and volatility; (ii) traditional mean‐value equivalence approach performs poorly in volatile intermodal context; (iii) mean‐value‐based forecast may outperform stationary distribution‐based forecast. Our work enriches revenue management models and applications. It advances our understanding on when and how to use dynamic forecasting in intermodal revenue management.     

基于PSO-PLS的组合预测方法在GDP预测中的应用

GDP预测是经济预测中一个非常重要的问题,随着经济的发展,对其预测精度的要求也越来越高.在考虑样本权重的基础上,提出一种微粒群算法与部分最小二乘回归方法相结合的组合预测方法,即采用微粒群方法对样本最优权重进行求解,在所得样本权重系数的基础上,用部分最小二乘回归方法确定组合预测的权重系数.将该方法用于中国GDP预测取得了较好的结果,与其他几种传统方法相比,预测精度有一定程度的提高,说明算法的有效性和可行性.     

农产品销量预测的支持向量机方法

运用支持向量机(Support Vector Machine,SVM)智能预测方法对农产品的消费市场需求进行动态预测。为提高农产品销量预测精度,充分考虑了农产品供需随天气变化、气候条件、节假日等因素的影响而动态变化的情况,将这些影响因素纳入农产品销量预测中,运用模糊理论进行模糊化处理;在此基础上提出以支持向量机方法为主、多方法融合为辅的智能预测系统,对农产品销量进行动态预测。实际算例验证了这一智能预测系统的精确性。     

Improving the accuracy and operational predictability of project cost forecasts: an adaptive combination approach

Conducting an early warning forecast to detect potential cost overrun is essential for timely and effective decision-making in project control. This paper presents a forecast combination model that adaptively identifies the best forecast and optimises various combinations of commonly used project cost forecasting models. To do so, a forecast error simulator is formulated to visualise and quantify likely error profiles of forecast models and their combinations. The adaptive cost combination (ACC) model was applied to a pilot project for numerical illustration as well as to real world projects for practical implementation. The results provide three valuable insights into more effective project control and forecasting. First, the best forecasting model may change in individual projects according to the project progress and the management priority (i.e. accuracy, outperformance or large errors). Second, adaptive combination of simple, index-based forecasts tends to improve forecast accuracy, while mitigating the risk of large errors. Third, a post-mortem analysis of seven real projects indicated that the simple average of two most commonly used cost forecasts can be 31.2% more accurate, on average, than the most accurate alternative forecasts.     

基于漂移度的组合预测方法研究

由于不同的预测方法能够提供不同的有用信息,其预测精度往往也存在差异,为了分散预测的风险,采用组合预测方法。本文首先提出相容方法集和互补模型集,然后在对不同单一预测模型的漂移性和互补性研究的基础上提出了基于漂移度的组合预测模型,为组合预测模型研究提供一种新的思路。最后通过实例来说明基于漂移度的组合预测模型能够提高样本期预测精度和外推预测精度及实际应用的有效性。     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

基于季节调整和Holt-Winters的月度负荷预测方法

针对负荷序列中异常数据会导致模型误设或参数估计发生偏差的问题,提出利用季节调整方法,先对原始负荷序列进行季节调整,获得消除离群值、节假日影响的季节调整后序列和季节成分序列;然后用改进的Holt-Winters方法对季节调整后成分进行预测,用虚拟回归方法预测季节成分序列;最后对各成分预测结果重构得到最终预测结果的月度负荷预测方法。通过实例检验,提出的方法能明显提高预测精度,预测效果要优于季节性Holt-Winters、SARIMA、神经网络、支持向量机等模型。     

ON THE IMPLICATIONS OF SPECIFICATION UNCERTAINTY IN FORECASTING*

Forecasters typically select a statistical forecasting model from among a set of alternative models. Subsequently, forecasts are generated with the chosen model and reported to management (forecast consumers) as if specification uncertainty did not exist (i.e., as if the chosen model were the “true” model of the forecast variable). In this note, a well-known Bayesian model-comparison procedure is used to illustrate some of the ambiguities and distortions of forecasts that do not reflect specification uncertainty. It is shown that a single selected forecasting model (however chosen) will generally misstate measures of forecast risk and lead to point and interval forecasts that are misplaced from a decision-theoretic point of view.     

不确定需求情境下的供应链预测契约设计

以报童模型为基础,研究了在由单一生厂商和零售商组成的供应链系统中,生产商如何通过契约设计来影响零售商的需求预测行为,使其收益最大化的问题。文章基于静态博弈模型对此问题进行了分析,发现在整合供应链情境下,当需求预测成本较小时选择预测能够获得更高的期望收益;在分散式供应链情境下,当生产商选择预测契约时,预测成本最终由生产商承担,且其期望收益为预测成本的减函数,而选择无预测契约时则为预测成本的非减函数;最后通过生产商期望收益对比,给出了最优策略。     

模糊AR模型的两步机会规划多期决策

利用系数为对称三角形模糊数的模糊AR模型预测投资收益,以模糊收益中值的协方差衡量投资风险,在引入流动性约束的条件下,拓展MARKOWITZ的单期均值-方差模型为多期模糊投资规划,每期期末利用改进的清晰化公式将预测的模糊收益转化为清晰的投资收益,构造出一个两步机会规划寻求投资收益和投资风险的Pareto解。采用含遗传交叉变异因子的粒子群算法求解。对上证50指数中的32只股票进行实证分析,并利用离散多期均值-方差模型检验所提出模型的有效性。检验结果表明:两步机会规划模型的最优投资组合的投资收益落在模糊收益值的允许区间之内,且其预测误差较小;在相同投资收益约束条件下,两步机会规划模型可以比多期MV模型承担更低的投资风险。