基于可得性和消耗率的备件订货策略研究

对机器维修保养所需备件的订货策略进行了研究,基于备件的可得性和消耗率,将备件分为四类:关键备件、重要备件、一般备件和特殊备件,针对不同的备件,给出了不同的订货策略。     

战时装备分散配置备件运行量优化研究

针对战时装备分散配置,随装备件与运行备件的使用方法不同的特点,本文对运行备件保障率进行了分析,将备件运行量优化问题理解为特殊的二级备件保障问题,建立了基于随装备件的运行备件保障率模型,给出了运行备件最优数量的通用计算公式。并给出了实例分析,验证了模型和算法,为装备分散配置的部队确定运行备件数量提供了决策依据。     

H公司起重设备维修备件分类研究

对备件进行科学合理的分类是有效解决诸多备件管理问题的第一步。文章从H公司起重设备维修备件分类现状入手,基于传统ABC分类法,定义了备件关键性评价指标CRI,通过评估计算,将H公司起重设备维修备件进行了关键性ABC分类,为备件的管理打下了基础。     

基于VIKOR法的VMI备件供应商优化选择

本文基于供应链环境下,备件供应商选择的复杂性,在进行备件分类管理的基础上分析了VMI对B类备件的适用性,构建了VMI备件供应商评价指标体系,运用VIKOR算法对备件供应商进行指标体系的评价。应用熵权法确定了相应的评价指标的权重,有效地避免评价过程中对指标赋权的主观性,最后实例证明了该方法在备件供应商选择中的有效性。     

战时备件申请时机和申请量研究

本文对剩余作战使用时间和备件获取时间进行分析,选定剩余作战使用时间为备件申请的约束条件。根据给定备件保障率,建立了备件申请时机和申请量模型,给出了计算公式。通过举例分析,验证了模型和算法。为战时备件申请问题提供了决策依据。     

连续生产模式下的备件物流运作研究

分析了连续生产模式下备件管理的特殊性,讨论了相关性最强的四个备件控制特征备件重要性、通用性、需求模式和价格。针对备件不同的控制特征组合,以连续生产模式下的电站为例,详细分析了相应的备件物流优化策略。     

基于ABC分类的备件多阶段多类别分类法

建立了基于ABC分类的备件多阶段多类别分类法,并通过实例分析了该法进行备件分类的过程,该分类法得到的备件分类方案将充分考虑影响备件效费比的关键性因素,能够有效地提高备件库存管理的效费比。     

备件储备定额研究

分析了备件储备定额研究的重要性，讨论了备件储备定额的有关问题：备件的科学分类、备件储备定额的确定方法、备件消耗量的确定及备件储备定额的管理。     

汽车企业备件物流改善

介绍了汽车企业备件物流组织机构演变、备件工业化等领域管理模式的改善,阐述了构建备件物流能力需求预测的方法,概述了备件物流降库存等运作模式改善方法,并展望了未来物流改善的方向     

基于VMI的区域煤炭企业备件实体联合库存储备策略

通过剖析煤炭企业备件管理的现状,发现主要是由于备件库存管理落后且存在较多问题,才导致了煤炭企业备件库存逐年增长、居高不下,大量占用库存储备资金。因此,通过对现行备件库存管理模式的比较分析,结合煤炭企业备件管理的特点,提出了基于VMI的区域煤炭企业备件实体联合库存储备策略,并进一步给出了该模式下的实施策略及应用优势。     

物流中心货运需求预测

针对以往物流中心货运需求预测存在的局限性,提出了一种新的方法,即先求出物流中心吸引范围内的区域货运需求预测总量,然后对该区域内的所有物流结点做竞争力评价,求出该物流中心的需求量在整个区域需求总量中所占的比例,最后将预测总量与比例相乘就得到该物流中心的有效货运需求预测量。     

物流园区物流需求预测方法研究

在阐述了物流需求预测存在的问题的基础上,提出了确定物流园区物流需求的“宏观法”和“市场法”,并对这两种方法的预测原理、公式、步骤以及各自的优缺点和适用范围进行了阐述。最后,提出了物流园区物流需求预测的研究方向。     

基于影响因素多维融合与贝叶斯概率更新的电力物资需求预测

通过梳理电力物资需求预测中存在的真实需求和计划需求、物资出库量和真实需求的不一致,以及电力物资需求的内外部影响问题,提出基于影响因素多维融合与贝叶斯概率更新的电力物资需求预测方法。首先分析了电力物资需求的内外部影响因素及其筛选,并按其对需求预测的影响程度进行权重赋值;其次设计了影响因素多维融合与贝叶斯概率更新的电力物资需求预测框架,介绍了贝叶斯概率更新的需求预测流程步骤;最后以温州市10kv配网项目的电力电缆需求预测为例进行算例说明。应用算例表明该方法能有效反映需求因素对电力物资需求变动的影响,符合电力物资需求特性,且具有很强的拓展性。     

Classification-based model selection in retail demand forecasting

Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in terms of demand quantity, demand frequency, demand regularity, and demand variation. Given this diversity in demand patterns, it is unlikely that any single model for demand forecasting can yield the highest forecasting accuracy across all SKUs. To save costs through improved forecasting, there is thus a need to match any given demand pattern to its most appropriate prediction model. To this end, we propose an automated model selection framework for retail demand forecasting. Specifically, we consider model selection as a classification problem, where classes correspond to the different models available for forecasting. We first build labeled training data based on the models’ performances in previous demand periods with similar demand characteristics. For future data, we then automatically select the most promising model via classification based on the labeled training data. The performance is measured by economic profitability, taking into account asymmetric shortage and inventory costs. In an exploratory case study using data from an e-grocery retailer, we compare our approach to established benchmarks. We find promising results, but also that no single approach clearly outperforms its competitors, underlying the need for case-specific solutions.     

安徽省能源需求的组合预测

根据预测理论,结合安徽省历年能耗的数据,在建立能源需求单项预测模型的基础上,建立组合预测模型,对2008～2014年的能源需求量进行预测。结果表明:组合预测模型的精度高于单项预测模型;安徽省能源需求量正以较快的速度在增长。     

Using weather ensemble predictions in electricity demand forecasting

Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.     

CPFR中的联合预测研究

文章研究了联合计划、预测和补货（CPFR）中的联合预测流程,并建立了相关的预测模型。在建模的过程中,使用了状态空间方程来描述实际市场需求和观测到的市场需求（销售量）,并通过卡尔曼滤波来预测零售商下期的销售量．结合零售商库存策略,预测出零售商下期的订单量。     

Improving demand forecasting in the air cargo handling industry: a case study

Air transportation plays a crucial role in the agile and dynamic environment of contemporary supply chains. This industry is characterised by high air cargo demand uncertainty, making forecasting extremely challenging. An in-depth case study has been undertaken in order to explore and untangle the factors influencing demand forecasting and consequently to improve the operational performance of an air cargo handling company. It has been identified that in practice, the demand forecasting process does not provide the necessary level of accuracy, to effectively cope with the high demand uncertainty. This has a negative impact on a whole range of air cargo operations, but especially on the management of the workforce, which is the most expensive resource in the air cargo handling industry. Besides forecast inaccuracy, a range of additional hidden factors that affect operations management have been identified. A number of recommendations have been made to improve demand forecasting and workforce management.     

Forecasting spare part demand with installed base information: A review

The classical spare part demand forecasting literature studies methods for forecasting intermittent demand. However, the majority of these methods do not consider the underlying demand-generating factors. The demand for spare parts originates from the replacement of parts in the installed base of machines, either preventively or upon breakdown of the part. This information from service operations, which we refer to as installed base information, can be used to forecast the future demand for spare parts. This paper reviews the literature on the use of such installed base information for spare part demand forecasting in order to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) the value of using installed base information to improve forecasting; and (4) the limits of the existing methods. This serves as motivation for future research.     

基于粗糙集理论的我国物流需求预测

将粗糙集理论引入物流需求预测，首先对物流需求与经济关系进行分析后选取了预测指标，接着运用属性约简提取出关键指标，并根据各指标的重要程度得出一致、完备的预测规则。通过实证证明了该方法可有效处理物流需求与影响因素之间的强耦合与非线性关系，避免主观因素影响，是一种适用性强的预测方法。