多管火箭炮武器系统效能评估

基于关联度理论提出一种新的多属性决策模型，并应用于多管火箭炮武器系统效能评估，通过指标数据的处理、专家可信度及权重的确定、决策模型的建立等步骤得到系统效能度量值。从决策角度看，该方法适用面较广，可用性较强，便于推广。     

基于加权α优势关系的多属性决策排序方法*

α优势粗糙集模型利用简单平均法赋权多个阈值α的排序结果,忽视数据集本身信息,导致不同数据集的排序质量差异性较大.针对此问题,文中提出基于加权α优势关系的优势度排序方法.首先运用α优势粗糙集方法详细分析决策对象.在此基础上,为了克服α主观赋权导致多属性决策排序结果中“并列”决策现象存在的不足,依据排序结果采用2种准则赋权α,并融合2种准则下所有对象的综合优势度,进一步细化排序结果.最后在具体算例中对比分析其它排序方法,验证文中方法的可行性和有效性.     

On the design of multiple key hashing files for concurrent orthogonal range retrieval between two disks

This paper concerns the following problem: given a set of multi-attribute records, a fixed number of buckets and a two-disk system, arrange the records into the buckets and then store the buckets between the disks in such a way that, over all possible orthogonal range queries (ORQs), the disk access concurrency is maximized. We shall adopt the multiple key hashing (MKH) method for arranging records into buckets and use the disk modulo (DM) allocation method for storing buckets onto disks. Since the DM allocation method has been shown to be superior to any other allocation methods for allocating an MKH file onto a two-disk system for answering ORQs, the real issue is knowing how to determine an optimal way for organizing the records into buckets based upon the MKH concept.

A performance formula that can be used to evaluate the average response time, over all possible ORQs, of an MKH file in a two-disk system using the DM allocation method is first presented. Based upon this formula, it is shown that our design problem is related to a notoriously difficult problem, namely the Prime Number Problem. Then a performance lower bound and an efficient algorithm for designing optimal MKH files in certain cases are presented. It is pointed out that in some cases the optimal MKH file for ORQs in a two-disk system using the DM allocation method is identical to the optimal MKH file for ORQs in a single-disk system and the optimal average response time in a two-disk system is slightly greater than one half of that in a single-disk system.     

A method for the efficient prioritization of infrastructure renewal projects

The infrastructure renewal program at MIT consists of a large number of projects with an estimated budget that could approach $1 billion. Infrastructure renewal at the Massachusetts Institute of Technology (MIT) is the process of evaluating and investing in the maintenance of facility systems and basic structure to preserve existing campus buildings. The selection and prioritization of projects must be addressed with a systematic method for the optimal allocation of funds and other resources. This paper presents a case study of a prioritization method utilizing multi-attribute utility theory. This method was developed at MIT's Department of Nuclear Engineering and was deployed by the Department of Facilities after appropriate modifications were implemented to address the idiosyncrasies of infrastructure renewal projects and the competing criteria and constraints that influence the judgment of the decision-makers. Such criteria include minimization of risk, optimization of economic impact, and coordination with academic policies, programs, and operations of the Institute. A brief overview of the method is presented, as well as the results of its application to the prioritization of infrastructure renewal projects. Results of workshops held at MIT with the participation of stakeholders demonstrate the feasibility of the prioritization method and the usefulness of this approach.     

产品设计方案总体性能评价模型及应用

产品设计方案的准确评价与决策对设计过程的成败至关重要。文中通过分析目前评价方法的特点,融合最小二乘法和可拓学理论,提出了一种面向产品设计方案总体性能评价模型。借助模糊层次分析法对指标权重进行确定;将多属性关联函数应用于不同评价特征指标的量化;建立权衡各评价指标的性能优度函数;并通过迭代积完成对产品设计方案总体性能的评价。最后,通过对槽轮机构的评价过程验证了该模型的有效性。     

An interactive approach for multi-attribute auctions

Auction processes are commonly employed in many environments. With rapid advances in Internet and computing technologies, electronic auctions have become very popular. People sell and buy a wide range of goods and services online. There is a growing need for the proper management of online auctions and for providing support to parties involved. In this paper, we develop an interactive approach supporting both the buyer and the bidders in a multi-attribute, single-item, multi-round, reverse auction environment. We demonstrate the algorithm on a number of problems.     

Reliability analysis and optimal version-updating for open source software

Context

Although reliability is a major concern of most open source projects, research on this problem has attracted attention only recently. In addition, the optimal version-dating for open source software considering its special properties is not yet discussed.

Objective

In this paper, the reliability analysis and optimal version-updating for open source software are studied.

Method

A modified non-homogeneous Poisson process model is developed for open source software reliability modeling and analysis. Based on this model, optimal version-updating for open source software is investigated as well. In the decision process, the rapid release strategy and the level of reliability are the two most important factors. However, they are essentially contradicting with each other. In order to consider these two conflicting factors simultaneously, a new decision model based on multi-attribute utility theory is proposed.

Results

Our models are tested on the real world data sets from two famous open source projects: Apache and GNOME. It is found that traditional software reliability models provide overestimations of the reliability of open source software. In addition, the proposed decision model can help management to make a rational decision on the optimal version-updating for open source software.

Conclusion

Empirical results reveal that the proposed model for open source software reliability can describe the failure process more accurately. Furthermore, it can be seen that the proposed decision model can assist management to appropriately determine the optimal version-update time for open source software.     

基于幂均算子的区间型多属性决策方法

针对决策矩阵元素为区间数的不确定多属性决策问题,提出一种新的决策方法．定义了区间数幂均算子和区间数的相似度,利用一致度矩阵获得每个属性与其他属性的相对一致度．通过区间数幂均算子集成得到方案的综合属性值,进而给出了方案的排序结果．该方法不需要求解属性的权重．应用实例表明了所提出方法的有效性和实用性．     

基于云-神经网络的多属性模糊规则分类

针对模糊规则分类中数据边界硬性划分的局限性问题,建立了云-神经网络模型,并提出了基于云-神经网络的模糊规则分类算法.在不影响数据模糊性和随机性的基础上,将数据转化为规则,并利用神经网络的学习能力,进行多属性模糊规则分类,与传统方法相比,该方法在保证数据模糊性和随机性的基础上,提高了模型精度和分类准确率,应用实例表明了该方法的有效性和可行性.