基于灰色预测模型的核事故医学应急装备使用保障费用分析

建立了核事故医学应急装备使用保障费用分析模型,且通过GM(1,1)灰色预测模型对核事故医学应急装备使用保障费用进行预测分析,解决了传统方法因数据缺乏而预测精度不足的问题。并用实际数据对预测模型进行了精度验证,结果表明该方法具有很好的预测精度,可用于核事故医学应急装备使用保障费用的预测和估算,为相关部门经费分配提供决策支持,对加强核事故医学应急装备全系统、全寿命的经济技术可行性论证,提高决策水平、计划的科学性和经费整体使用效益都具有重要意义.     

装备采购费用估算在采购合同谈判中的应用

航天装备采购费用估算是航天装备采购管理工作的重要组成部分.采购合同谈判是航天装备采购工作的重要环节.本文研究在可靠性增长费用和供货方利润固定的情况下,如何确定采购费用的上限.该结果有利于航天装备采购部门在采购合同谈判时把握主动权,控制采购费用增长.     

基于Ⅰ-Monte Carlo的飞机研制费用风险建模与实证分析北大核心

随着信息化技术在武器装备系统中所占比重的扩大,武器装备系统复杂性急剧增加,装备研制与使用保障费用逐年升高,费用分配不合理和使用率低下等问题越来越严重.飞机作为特殊的复杂装备,费用问题更为严峻.因此,对飞机研制费用风险进行研究,进而为有效提高飞机作战性能、严格控制费用增长提供理论支撑具有重大意义.基于蒙特卡洛在费用风险分析方面的适用性及传统蒙特卡洛的不足,提出了利用基于风险驱动理论的改进蒙特卡洛处理飞机研制费用的风险建模.并以飞机结构件某单位机翼研制项目为例,利用@risk软件进行了费用风险的改进蒙特卡罗建模与分析,对改进方法的合理性和有效性进行了验证.     

基于Ⅰ-Monte Carlo的飞机研制费用风险建模与实证分析

随着信息化技术在武器装备系统中所占比重的扩大,武器装备系统复杂性急剧增加,装备研制与使用保障费用逐年升高,费用分配不合理和使用率低下等问题越来越严重.飞机作为特殊的复杂装备,费用问题更为严峻.因此,对飞机研制费用风险进行研究,进而为有效提高飞机作战性能、严格控制费用增长提供理论支撑具有重大意义.基于蒙特卡洛在费用风险分析方面的适用性及传统蒙特卡洛的不足,提出了利用基于风险驱动理论的改进蒙特卡洛处理飞机研制费用的风险建模.并以飞机结构件某单位机翼研制项目为例,利用@risk软件进行了费用风险的改进蒙特卡罗建模与分析,对改进方法的合理性和有效性进行了验证.     

产品开发费用估算模型研究

对于大型复杂项目 ,开发费用是总体费用的一个重要组成部分 .近年来 ,开发费用的估算日益受到重视 .在总结了现有的开发费用估算模型 ,并对这些模型进行了比较与讨论 .在对开发费用的 Weibul分布曲线进行分析和研究后 ,针对开发费用的 Weibul分布曲线拟合的局限 ,提出了开发费用的 Weibul分布累积曲线拟合模型 ,给出了模型使用的条什 .最后 ,给出了模型分析的一个实例     

航空装备飞行机务准备时间估算方法研究

航空装备飞行机务准备是保持和及时恢复飞机良好状态并使之符合具体飞行任务的重要工作内容,直接影响着飞机的出动强度.以航空装备直接飞行机务准备工作为例,建立航空装备机务准备时间估算模型,对模型进行了求解,实例证明该方法对于估算飞行机务准备维修时间,优化机务准备流程,缩短飞行机务准备时间,完善机务维修保障工作提供了一种有效的途径.     

基于回收可替代产品的闭环供应链协调模型

应用博弈论对两种可替代产品回收的定价策略进行研究,得出分散决策模型和集中决策模型的均衡解.提出收益费用共享契约,实现供应链的协调.最后,利用数值仿真得出重要的管理启示:1)制造商提供的最优批发价格和销售商提供的最优销售价格均很大程度上取决于该产品本身的成本结构,也与另外一种产品的价格存在关系.2)收益费用共享契约下,制造商提供的批发价格与两种产品的成本结构均有关,并且呈现出相关规律.     

基于DEA模型的装备费用-效能分析

结合装备费用效能分析的特性,利用数据包络分析法(DEA)对装备费用效能决策方案进行评价.通过计算和处理,得到不同方案的有效程度并制定相应调整措施.数据包络析在对装备费用效能分析中具有很强的适用性,且具有广泛的应用价值.     

基于灰色组合模型的发动机研制费用估算研究

参数估算法作为LCC的一种估算方法主要应用于武器装备研制费用的估算.由于样本数据较少,且变量之间的线性相关程度较高,如果使用传统的多元线性回归方程法估算,则会严重降低估计结果的有效性.为解决这一问题,研究了灰色组合模型在发动机研制费用估算中的应用.通过实例计算表明,本方法用较少的样本可以得到较高的估算精度.     

偏最小二乘回归法在武器装备研制费用估算中的应用

参数估算法作为 LCC的一种估算方法主要应用于武器装备研制费用的估算 .由于样本数据较少 ,且变量之间的线性相关程度高 ,如果使用传统的普通最小二乘法 (OLS)估算 ,则会严重降低估计结果的有效性 .而偏最小二乘回归法 (PLS)可以克服样本点少和高线性相关性的问题 ,使估算结果更趋于真值 .以某型无人侦察机的研制费用估算和美国军用飞机发动机研制费用估算为例 ,主要研究偏最小二乘回归法 (PLS)在武器装备研制费用估算中的应用     

An evaluation approach to engineering design in QFD processes using fuzzy goal programming models

Quality function deployment (QFD) is a product development process used to achieve higher customer satisfaction: the engineering characteristics affecting the product performance are designed to match the customer requirements. From the viewpoint of QFDs designers, product design processes are performed in uncertain environments, and usually more than one goal must be taken into account. Therefore, when dealing with the fuzzy nature in QFD processes, fuzzy approaches are applied to formulate the relationships between customer requirements (CRs) and engineering design requirements (DRs), and among DRs. In addition to customer satisfaction, the cost and technical difficulty of DRs are also considered as the other two goals, and are evaluated in linguistic terms. Fuzzy goal programming models are proposed to determine the fulfillment levels of the DRs. Differing from existing fuzzy goal programming models, the coefficients in the proposed model are also fuzzy in order to expose the fuzziness of the linguistic information. Our model also considers business competition by specifying the minimum fulfillment levels of DRs and the preemptive priorities between goals. The proposed approach can attain the maximal sum of satisfaction degrees of all goals under each confidence degree. A numerical example is used to illustrate the applicability of the approach.     

An advanced quality function deployment model using fuzzy analytic network process

Quality function deployment (QFD) is a customer-oriented design tool used to ensure that the voice of customers is employed throughout the product planning and design stages. QFD uses the house of quality (HOQ), which is a matrix that provides a conceptual map for inter-functional planning and communication. In this paper, an advanced QFD model, based on fuzzy analytic network process (ANP) approach, is proposed to systematically take into account the interrelationship between and within the QFD components. The proposed method is aimed at expanding the current research scope from the product planning phase to the part deployment phase to provide product developers with more valuable information (ex. the importance and bottleneck level of part characteristics). Both customer requirements and the company’s production demands will be used as the inputs for the QFD process to enhance the completeness and accuracy of the QFD analysis results. A case study is presented to illustrate the application of the proposed method.     

Quality function deployment for the good of soccer

This article presents a model for prioritizing and designing rule changes for the game of soccer in order to make it more attractive to soccer enthusiasts. The model, which is loosely based on quality function deployment (QFD), incorporates market segments, sports enthusiast interests, soccer activities, and rules of the game as rows and columns of interconnected QFD matrices. In addition, analytic hierarchy process (AHP) is used to determine the intensity of the relationship between the row and column variables of each matrix, while analytic network process (ANP) is used to determine the intensity of synergy effects among column variables. Finally, a trend extrapolation forecasting technique is used to suggest rule change specifications. The model fine-tunes and adds precision to the otherwise ad hoc process concerning the improvement of the game of soccer, and can provide insight about improvements in other professional sports.     

Quality function deployment: A literature review

This paper presents a literature review of quality function deployment (QFD) based on a reference bank of about 650 QFD publications established through searching various sources. The origination and historical development of QFD, especially in Japan and the US, are briefly accounted first, followed by a partial list of QFD organizations, softwares, and online resources. Then a categorical analysis is conducted about QFD’s functional fields, applied industries and methodological development. Ten informative QFD publications are also suggested, particularly for those who are not yet familiar with QFD. It is hoped that the paper can serve the needs of researchers and practitioners for easy references of QFD studies and applications, and hence promote QFD’s future development.     

Fuzzy linear programming models for new product design using QFD with FMEA

Quality function deployment (QFD) is a customer-driven approach in processing new product developments in order to maximize customer satisfaction. Determining the fulfillment levels of design requirements (DRs) and parts characteristics (PCs) is an important decision problem during QFD activity processes for new product development. Unlike the existing literature, which mainly focuses on the determination of DRs, this paper proposes fuzzy linear programming models to determine the fulfillment levels of PCs under the requirement to achieve the determined contribution levels of DRs for customer satisfaction. In addition, considering the design risk, this paper incorporates failure modes and effect analysis (FMEA) into QFD processes, which is treated as the constraint in the models. To cope with the vague nature of product development processes, fuzzy approaches are used for both FMEA and QFD. The illustration of the proposed models is performed with a numerical example to demonstrate the applicability in practice.     

Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept

Quality function deployment (QFD) is a customer-driven approach in processing new product development (NPD) to maximize customer satisfaction. Determining the fulfillment levels of the “hows”, including design requirements (DRs), part characteristics (PCs), process parameters (PPs) and production requirements (PRs), is an important decision problem during the four-phase QFD activity process for new product development. Unlike previous studies, which have only focused on determining DRs, this paper considers the close link between the four phases using the means-end chain (MEC) concept to build up a set of fuzzy linear programming models to determine the contribution levels of each “how” for customer satisfaction. In addition, to tackle the risk problem in NPD processes, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy approaches are used for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model.     

Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator

Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements into the technical attributes of a product. Deriving the rating order of technical attributes from input variables is a crucial step in applying QFD. When the relative weights of customer requirements and the relationship measures between customer requirements and technical attributes are expressed as fuzzy numbers, calculating the importance of each technical attribute falls into the category of fuzzy weighted average, in which the derived membership function of the fuzzy importance of each technical attribute is not explicitly known. Thus, most ranking methods are not suitable under these circumstances. A method is proposed in this paper using fuzzy weighted average method in the fuzzy expected value operator in order to rank technical attributes in fuzzy QFD. An example of a flexible manufacturing system design is cited to demonstrate the application of the proposed approach.     

Equipment selection with heterogeneous fleets for multiple-period schedules

In a simple surface mining scenario we consider a mine to have one mining location and dumpsite connected by a truck route. We determine a purchase and salvage policy for trucks and loaders, which minimises the cost of materials handling over a multiple period schedule. This problem becomes large scale when we consider large sets of equipment and long schedules. Pre-existing equipment can lead to heterogeneous fleets; non-uniformity in the operating cost function coupled with compatibility issues also adds to the complexity of the problem. We present an integer programme for this problem, where we introduce a specialised linear constraint set to ensure satisfaction of production requirements while accounting for equipment compatibility. Many aspects of the presented model, such as the consideration of multiple periods and pre-existing equipment, are novel for the mining industry and ensure that the model is both a new and advanced equipment selection tool.     

Product design and selection using fuzzy QFD and fuzzy MCDM approaches

Quality function deployment (QFD) is a useful analyzing tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have applied the fuzzy set theory to QFD and developed various fuzzy QFD models. Three issues are investigated by examining their models. First, the extant studies focused on identifying important engineering characteristics and seldom explored the subsequent prototype product selection issue. Secondly, the previous studies usually use fuzzy number algebraic operations to calculate the fuzzy sets in QFD. This approach may cause a great deviation in the result from the correct value. Thirdly, few studies have paid attention to the competitive analysis in QFD. However, it can provide product developers with a large amount of valuable information. Aimed at these three issues, this study integrates fuzzy QFD and the prototype product selection model to develop a product design and selection (PDS) approach. In fuzzy QFD, the α-cut operation is adopted to calculate the fuzzy set of each component. Competitive analysis and the correlations among engineering characteristics are also considered. In prototype product selection, engineering characteristics and the factors involved in product development are considered. A fuzzy multi-criteria decision making (MCDM) approach is proposed to select the best prototype product. A case study is given to illustrate the research steps for the proposed PDS method. The proposed method provides product developers with more useful information and precise analysis results. Thus, the PDS method can serve as a helpful decision-aid tool in product design.     

A combined approach for fuzzy multi-objective multiple knapsack problems for defence project selection

In this study, a model representing military requirements as scenarios and capabilities is offered. Pair-wise comparisons of scenarios are made according to occurrence probabilities by using the Analytical Hierarchy Process (AHP). The weights calculated from AHP are used as the starting weights in a Quality Function Deployment (QFD) matrix. QFD is used to transfer war fighter requirements into the benefit values of projects. Two levels of QFD matrices are used to evaluate new capability areas versus capabilities and capabilities versus projects. The benefit values of the projects are used in a multi-objective problem (multi-objective multiple knapsack problem) that considers the project benefit, implementation risks and environmental impact as multiple objectives. Implementation risk and environmental impact values are also calculated using the same combined AHP and QFD methodology. Finally, the results of the fuzzy multi-objective goal programming suggest a list of projects that offers optimal benefit when carried out within multiple budgets.