层次分析法---文献翻译
层次分析法---文献翻译
888大学
毕业设计(论文)文献翻译
题目层次分析法院、系(部) 计算机科学与技术学院专业及班级计科0903班姓名 888 指导教师 888 日期 2013年3月
Analytic Hierarchy Process
The Analytic Hierarchy Process (AHP) is a structured technique for helping
people deal with complex decisions. Rather than prescribing a "correct" decision, the AHP helps people to determine one that suits their needs and wants. Based on mathematics and psychology, it was developed by Thomas L. Saaty in the 1970s and has been extensively studied and refined since then. The AHP provides a comprehensive and rational framework for structuring a problem, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions. It is used throughout the world in a wide variety of decision situations, in fields such as government, business, industry, healthcare, and education.
Several firms supply computer software to assist in using the process.
Users of the AHP first decompose their decision problem into a hierarchy of more easily comprehended sub-problems, each of which can be analyzed independently. The elements of the hierarchy can relate to any
aspect of the decision problem—tangible or intangible, carefully measured or roughly estimated, well- or poorly-understood—anything at all that applies to the decision at hand.
Once the hierarchy is built, the decision makers systematically evaluate its various elements, comparing them to one another in pairs. In making the comparisons, the decision makers can use concrete data about the elements, or they can use their judgments about the elements' relative meaning and importance. It is the essence of the AHP that human judgments, and not just the underlying information, can be used in performing the evaluations.
The AHP converts these evaluations to numerical values that can be processed and compared over the entire range of the problem. A numerical weight or priority is derived for each element of the hierarchy, allowing diverse and often incommensurable elements to be compared to one another in a rational and consistent way. This capability distinguishes the AHP from other decision making techniques.
In the final step of the process, numerical priorities are derived
for each of the decision alternatives. Since these numbers represent the alternatives' relative ability to achieve the decision goal, they allow a straightforward consideration of the various courses of action.
Uses and applications
While it can be used by individuals working on straightforward decisions, Analytic Hierarchy Process (AHP) is most useful where teams
of people are working on complex problems, especially those with high stakes, involving human perceptions
and judgments, whose resolutions have long-term repercussions. It
has unique advantages where important elements of the decision are difficult to quantify or compare, or where communication among team members is impeded by their different specializations, terminologies, or perspectives.
Decision situations to which the AHP can be applied include:
, Choice - The selection of one alternative from a given set of alternatives,
usually where there are multiple decision criteria involved.
, Ranking - Putting a set of alternatives in order from most to
least
desirable Prioritization - Determining the relative merit of a set
of
alternatives, as opposed to selecting a single one or merely ranking them
, Resource allocation - Apportioning resources among a set of alternatives
, Benchmarking - Comparing the processes in one's own organization with
those of other best-of-breed organizations
, Qualitymanagement - Dealing with the multidimensional aspects of quality and quality improvement
The applications of AHP to complex decision situations have numbered in the thousands, and have produced extensive results in problems involving planning, Resource allocation, priority setting, and selection among alternatives. Other areas have included forecasting, toreotal quality management, business process re-engineering ,quality function deployment， and the Balanced Scorecard.Many
AHP applications are never reported to the world at large, because they take place at high levels of large organizations where security and privacy considerations prohibit their disclosure. But some uses of AHP are discussed in the literature. Recently these
have included:
, Deciding how best to reduce the impact of global climate change (Fondazione Eni Enrico Mattei)
, Quantifying the overall quality of software system(Microsoft
corporation)
, Selecting university faculty(Bloomsburg University of Pennsy)
, Deciding where to locate offshore manufacturing plants(University of
Cambridge)
, Assessing risk in operating cross-country prtroleum
pipelines(American
Society of Civil Engineers)
, Deciding how best to manage U.S. watersheds(U.S. Department of Agriculture)
AHP is sometimes used in designing highly specific procedures for particular situations, such as the rating of buildings by historic significance. It was recently applied to a project that uses video footage to assess the condition of highways in
Virginia. Highway engineers first used it to determine the optimum scope of the project, then to justify its budget to lawmakers.
AHP is widely used in countries around the world. At a recent international conference on AHP, over 90 papers were presented from 19 countries, including the U.S., Germany, Japan, Chile , Malaysia, andNepal. Topics covered ranged from Establishing Payment Standards for Surgical Specialists, to Strategic Technology
Roadmapping, to Infrastructure Reconstruction in Devastated Countries. AHP was
introduced in China in 1982, and its use in that country has expanded greatly since then—its methods are highly compatible with the traditional Chinese decision making framework, and it has been used for many decisions in the fields
ofeconomics,energy,management,environment,traffic,agriculture, industry, and the military.
Though using AHP requires no specialized academic trainning, the subject is widely taught at the university level—one AHP software provider lists over a hundred
colleges and universities among its clients. AHP is considered an important subject in many institutions of higher learning, including
schools of engineering and Graduate school of Business . AHP is also an important subject in the quality field, and is taught in many specialized courses including Six Sigma, Lean Six Sigma, and QFD.
In China, nearly a hundred schools offer courses in AHP, and many doctoral students choose AHP as the subject of their research and dissertations. Over 900 papers have been published on the subject in
that country, and there is at least one Chinese scholarly journal devoted exclusively to AHP.
Implementation
As can be seen in the examples that follow, using the AHP involves the mathematical synthesis of numerous judgments about the decision problem at hand. It is not uncommon for these judgments to number in the dozens or even the hundreds. While the math can be done by hand or with a calculator, it is far more common to use one of several computerized methods for entering and synthesizing the judgments. The simplest of these involve standard spreadsheet software, while the most complex use custom software, often augmented by special devices for acquiring the judgments
of decision makers gathered in a meeting room.
Steps in using the process
The procedure for using the AHP can be summarized as:
1. Model the problem as a hierarchy containing the decision goal,
the alternatives
for reaching it, and the criteria for evaluating the alternatives.
2. Establish priorities among the elements of the hierarchy by
making a series of
judgments based on pairwise comparisons of the elements. For example, when
comparing potential real-estate purchases, the investors might say they prefer
location over price and price over timing.
3. Synthesize these judgments to yield a set of overall priorities
for the hierarchy.
This would combine the investors' judgments about location, price
and timing
for properties A, B, C, and D into overall priorities for each property.
4. Check the consistency of the judgments.
5. Come to a final decision based on the results of this process.
Criticisms
The AHP is now included in most operations research and management science textbooks, and is taught in numerous universities; it is used extensively in organizations that have carefully investigated its theoretical underpinnings. While the general consensus is that it is
both technically valid and practically useful, the method does have its critics.
In the early 1990s a series of debates between critics and
proponents of AHP was published in Management Science and The Journal of the Operational Research
Society. These debates seem to have been settled in favor of AHP.
Occasional criticisms still appear. A 1997 paper examined possible flaws in the verbal (vs. numerical) scale often used in AHP pairwise comparisons. Another from the same year claimed that innocuous changes
to the AHP model can introduce order where no order exists. A 2006 paper found that the addition of criteria for which all alternatives perform equally can alter the priorities of alternatives. An in-depth paper discussing the academic criticisms of AHP was published in Operations Research in
2001.
Most of the criticisms involve a phenomenon called rank reversal, discussed in
the following section.
Rank reversal
Many people hear about rank reversal and assume that there is some sort of proven principle about it that needs to be upheld in making decisions. That assumption has led to much misunderstanding of AHP and other decision making techniques. In actuality, rank reversal is a complex matter about which there are many conflicting ideas and opinions. This section offers a simplified explanation of the situation.
Decision making involves ranking alternatives in terms of criteria
or attributes of those alternatives. It is an axiom of some decision theories that when new alternatives are added to a decision problem, the ranking of the old alternatives must not change. But in the real world, adding new alternatives can change the rank of the old ones. These rank reversals do not occur often, but the possibility of their occurrence
has
substantial logical implications about the methodology used to make decisions, the underlying assumptions of various decision theories, etc.
A simple example will demonstrate the phenomenon of rank reversal: Consider a pretty girl in a small town. She's having a party next week, and she wants to buy a dress that will impress her guests. She visits
the town's only dress store and goes to the rack of party dresses. There are five such dresses, and after long consideration she ranks them by desirability as follows:
Rank Style Color Price
1 Style A Blue $109
2 Style A Green $109
3 Style B Red $119
4 Style C Yellow $99
5 Style D Off-White $149
Now imagine that she enters the back room and sees the store's
entire inventory of dresses. The dresses she has looked at in Styles B, C, and D are the only ones of their kind, but there are four more Style
A dresses in green and eight more Style A dresses in blue. In the language of decision science, these dresses are copies of the existing alternatives. In our one-store small town scenario, there's a reasonable chance that one or more party guests would buy and wear one of the copies.
When made aware of these new alternatives, our fashion-conscious
girl might rank her choices in a different order. Considering her great embarrassment if a guest were to wear the same dress that she did, she might rank her choices like this:
Old
Rank Style Color Price
Rank
1 3 Style B Red $119
2 4 Style C Yellow $99
3 5 Style D Off-White $149
4 2 Style A Green $109
5 1 Style A Blue $109
Notice that the rankings of the two Style A dresses have reversed (since there are more copies of the blue dress than of the green one). Not only that, but Style A has gone from the most preferred style to the least preferred. Rank reversal has occurred. Axioms of decision theories have been violated. Scholars and researchers can cry
"foul," or impugn the method by which the girl has made her choice, but there is no denying that in the world of our example, ranks have
been reversed. There is no doubt that the reversal is due to the introduction of additional alternatives that are no different from the existing ones.
The above is but one example of rank reversal. Rank reversal can
also occur when additional alternatives are added/removed that are not copies of the original alternatives (e.g., red and yellow dresses in completely different styles). Another example of rank reversal occurred in the 2000 U.S. presidential election. Ralph Nader was an 'irrelevant' alternative, in that he was dominated by both the Democrat and Republican candidates. However, since he attracted more votes from those who would have voted Democrat rather than Republican, his presence caused the ranks to reverse. Put another way, if Nader were not in the race, it is widely accepted that Al Gore would have won.
There are two schools of thought about rank reversal. One maintains that new alternatives that introduce no additional attributes should not cause rank reversal under any circumstances. The other maintains that there are both situations in which rank reversal is not reasonable as well as situations where they are to be expected. The current version of the AHP can accommodate both these schools — its Ideal Mode preserves rank, while its Distributive Mode allows the ranks to change. Either mode is selected according to the problem at hand.
层次分析法
层次分析法(AHP)是一种帮助人们处理复杂决策的结构化技术，比起一种指定
的“正确”的方法，层次分析法能帮助人们决定哪一种是更适合他们的需求。基于
数学和心理学，Thomas L. Saaty于1970年深入研究了层次分析法，从此以后，层次分析法被广泛的学习和重定义。AHP为构建一个问题，描述和权衡它的因素，相关那样的因素达到整体的目标，评估交互的解决方法提供了一种广泛的和理性的框架结构。在这个世界中，它被广泛的应用在各种决策形式:像政府，商业，工业，医疗保健和教育等各种领域。
一些公司提供计算机软件来支持这些过程。
AHP的用户一开始把他们的决策分解成更简单的包含各种子问题的层次结构，每一种子问题都能够单独的分析。这种层次架构的各种因素关系到决策问题的各种方面，包括明确的和不明确的问题，仔细的估量和粗略的估计，好的和不好的任何事情。这些方面的问题用于当前的决策。
一旦层次结构建立，决策者系统评估其各个组成部分，比较彼此在对。在作出这一比较，决策者可以使用的具体数据内容，也可以利用其判断的因素的相对意义和重要性。这是AHP的本质，人类的判断，而不仅仅是基本的信息，可用于履行的评价。
层次分析法评价转换这些数值，可处理和比较在整个范围内的问题。数值重量或优先源自各层次的每个因素，允许让不同的而且往往不可加以比较的要素以一个以合理和一致的方式进行比较。这是AHP区分其他决策方法的能力。
在最后一步的过程中，数值的优先权得出每一个可供选择的决定。由于这些数字表示以实现决策的目标相对能力，他们可以从各种复杂的行动中找出直接的方案。
使用和应用
虽然它可用于个人从事简单的决定，层次分析法( AHP )是最有用的地方是一个团队正在努力处理复杂的问题，尤其是那些高风险，涉及人权的认识和判断，其
决议具有长期影响。它具有独特的优势在于其重要组成部分的决定都是难以量化或
比较的，或在团队成员之间阻碍了他们难以沟通的不同专业，术语，或观点。
AHP在以下决策情况下使用:
?选择——由一组给定的替代品中选择一个替代品，通常当市场上有多个决策
标准参与。
?排序——从最不理想的方案中实施了一套替代方案。
?优先级——确定一套替代方案的相对优点，而不是选择一个人或者仅仅是
凭借他们的排名。
?资源分配——在一套替代品方案中分配资源。
?基准——在自己的组织与其他同类最佳组织比较过程。
?质量管理——处理多层面的质量和质量改进。
对复杂的情况下应用层次分析法决定了编号数以千计，在题涉及规划，资源分配，确定优先次序，选择其中替代品中产生了广泛的结果，其他领域包括预测，全
面质量管理，业务流程再设计，质量功能配置，和平衡计分卡。许多层次的应用从
来没有报告给整个世界，因为它们应用在高层次的大型组织，基于安全和隐私的考虑，禁止其披露。但是，一些利用层次分析法，讨论了文献中。最近这些活动包括: ?决定如何最好地减少全球气候变化的影响(埃尼恩马德基金会)
?软件系统整体素质的量化(微软公司)
?选择大学教师(宾夕法尼亚大学)
?决定在何处建造海外制造工厂(剑桥大学)
?评估经营跨国石油管道的风险(美国土木工程师学会)
?决定如何最好地管理美国的流域(美国农业部)
层次分析法有时用在设计高度的具体程序的特殊情况，如评价的建筑物的历史
意义。这是最近申请的一个项目，利用录像资料，以评估在弗吉尼亚州的高速公路
的状况。公路工程师首先用它来确定最合适的项目范围，然后来证明其预算给国会议员。
层次分析法被广泛应用于世界各国。在最近举行的一次AHP国际会议，超过来自19个国家提交文件的90多份文件，包括美国，德国，日本，智利，马来西亚和尼泊尔。话题涉及范围从建立支付标准的外科专家，路况战略技术，受灾国家基础设施的重建。层次分析法在1982年引进中国，它的使用在该国已大大增加，因为当时它的方法非常符合中国传统的决策框架，并已用于许多决策领域如:经济，能源，管理，环境，交通，农业，工业和军事。
虽然采用层次分析法不需要专门的学术训练，这一问题是广泛任教于大学一级层次。软件供应商客户名单超过一百个是学院和大学。层次分析法被认为是一个重要课题，许多高等学府，其中包括学校的工程和研究生院的业务。层次分析法在质量领域也是一个重要的主题，并传授许多专门课程，包括六西格玛，精益六西格玛，和QFD。
在中国，近100所学校开设AHP，许多博士生选择以层次分析法为研究的主体和论文。在该国已出版了超过900篇关于这个问题名的论文，并至少有一个中国学术期刊专门介绍AHP。
应用
过程中使用层次分析法可以概括为:
1.建立一个包含层次结构、达到这种目标的替代品、评价替代品的标准的问题模型。
2.基于成对比较的基础，作出一系列判决，确定层次结构的优先次序的要素。例如，当比较潜在的房地产购买时，投资者可能会说他们喜欢的位置多与价格和价格多与时机。
3.综合这些判断产生了一套整体优先等级。结合投资者的判断位置，价格和时间特性为优先级A ， B ， C和D，纳入总体优先事项作为每个属性。
4.一致性的检查判断。
5.在这一过程结果的基础上作出最后决定结果。
批评
层次分析法现已列入最运筹学和管理学的教科书，并在许多大学教授，是广泛用于认真调查其理论基础的组织。虽然一般的共识是，在技术上是有效的和实际有用的，这种方法也有其批评之处。
20世纪90年代初，在批评和支持者之间发生一系列关于发表在管理科学与期刊业务研究学会的AHP辩论。这些辩论似乎已经解决赞成层次分析法。
偶尔批评，仍然会出现。 1997年文件审查可能的漏洞进行了口头上(对数值)规模常用AHP中成对比较. 同年一些人声称，无害修改AHP模型可以在没有秩序存在引进秩序。 2006年的文件发现，除了标准的，所有的替代品也同样可以改变的优先选择。深入讨论了学术批评的AHP发表在2001年的运筹学。
大多数批评涉及的现象，叫做逆序，讨论下一节。
逆序
许多人听到逆序和假定，认为其中存在着需要坚持决策某种证明原理。这种假设导致了层次分析法和其他决策技巧的很多误解。实际上，逆序是一个有许多相互矛盾的思想和看法的复杂问题，对此.本节提供了一种情况简化的解释。
就标准和属性而言，决策涉及替代品排名。这是一些决定的理论的一个公理，当新的替代品被添加到一个决策问题，旧的排序不一定变。但是，在现实世界中，增加了新的替代品可以改变的旧的排名。这些排名倒退往往并不经常发生，但有可能所用的方法作出决定发生了实质性的逻辑性影响，潜在的基本假设理论的各种决定，等等。
一个简单例子来证明这一逆序现象:
考虑小城镇一位漂亮的姑娘。她下周有一个聚会，而且她希望买给客人留下深刻印象的一件衣服。她去了该镇的唯一衣服店，来到了礼服架前面。有5 个这样的衣服，经过长时间的考虑她采用以下需求排列它们的顺列:
排序风格颜色价格
1 A 蓝色 109
2 A 绿色 109
3 B 红色 119
4 C 黄色 99
5 D 灰白149
色
现在想象，她进入后面的房间，看到商店的整个库存的服装。她看见B ， C
和D风格的衣服是唯一，但也有4个绿色的风格A服装和八个蓝色的风格A服装。用决策科学的语言来说，这些衣服是现有替代品的副本。在我们小城镇的一个店的情况下，将有一个合理的机会是一个或多个聚会的客人将购买和佩戴这一个副本。
当了解到这些新的替代品，我们的时尚的女孩可能会换另一种排序。如果客户穿同样的衣服，她将会很尴尬，她认为，她的排序可能会变成这样: 排旧风颜价
序的排序格色格
1 3 B 红119
色
2 4 C 黄99
色
3 5 D 灰149
白色
4 2 A 绿109
色
5 1 A 蓝109
色
请注意，排名两个风格A礼服扭转(因为蓝色服装有更多的副本，而绿色为
1 ) 。不仅如此，风格A已经是最喜欢的风格中最不可取的。逆序发生。公
理的决定理论受到了侵犯。学者和研究人员可以哭“犯规” ，或非难的方式使女孩作出了她的选择，但无可否认，在世界上的我们的例子中，排名已经扭转。毫无疑问，扭转是由于引入更多的替代办法，与现存的并没有什么不同。
以上只是逆序的一个例子。逆序也有可能会发生更多的替代品添加/取消，并不是原来的替代品的副本(例如，红色和黄色服装的完全不同的风格)。另一个逆序例子发生在2000年美国总统大选。纳德是一个“无关”替代品，他是民主党共和党的候选人，然而，因为他吸引了更多的选票是从那些谁会投民主党，而不是共和党，他的存在造成行列的扭转。换句话说，如果纳德没有在比赛中，人们普遍认为戈尔将会赢得。
有两所学校思考逆序。一位坚持认为在任何情况下，新的替代品在没有介绍额外的属性不应导致逆序。另有两种情况，即逆序是不合理的以及它们是可以预期。目前版本的层次分析法可以容纳这两个学校——理想模式保留职级，而其分布模式的行列允许改变。选择任一种模式是根据手头上的问题。