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A basic, but very reasonable, assum
A basic, but very reasonable, assumption is that if attribute A is absolutely more
important than attribute B and is rated at 9, then B must be absolutely less important
than A and is valued at 1/9.
These pairwise comparisons are carried out for all factors to be considered, usually not
more than 7, and the matrix is completed. The matrix is of a very particular form which
neatly supports the calculations which then ensue (Saaty was a very distinguished
mathematician).
The next step is the calculation of a list of the relative weights, importance, or value, of
the factors, such as cost and operability, which are relevant to the problem in question
(technically, this list is called an eigenvector). If, perhaps, cost is very much more
important than operability, then, on a simple interpretation, the cheap equipment is
called for though, as we shall see, matters are not so straightforward. The final stage is
to calculate a Consistency Ratio (CR) to measure how consistent the judgements have
been relative to large samples of purely random judgements. If the CR is much in
excess of 0.1 the judgements are untrustworthy because they are too close for comfort to
randomness and the exercise is valueless or must be repeated. It is easy to make a
minimum number of judgements after which the rest can be calculated to enforce a
perhaps unrealistically perfect consistency.
The AHP is sometimes sadly misused and the analysis stops with the calculation of the
eigenvector from the pairwise comparisons of relative importance (sometimes without
even computing the CR!) but the AHP’s true subtlety lies in the fact that it is, as its
name says, a Hierarchy process. The first eigenvector has given the relative importance
attached to requirements, such as cost and reliability, but different machines contribute
to differing extents to the satisfaction of those requirements. Thus, subsequent matrices
can be developed to show how X, Y and Z respectively satisfy the needs of the firm.
(The matrices from this lower level in the hierarchy will each have their own
important than attribute B and is rated at 9, then B must be absolutely less important
than A and is valued at 1/9.
These pairwise comparisons are carried out for all factors to be considered, usually not
more than 7, and the matrix is completed. The matrix is of a very particular form which
neatly supports the calculations which then ensue (Saaty was a very distinguished
mathematician).
The next step is the calculation of a list of the relative weights, importance, or value, of
the factors, such as cost and operability, which are relevant to the problem in question
(technically, this list is called an eigenvector). If, perhaps, cost is very much more
important than operability, then, on a simple interpretation, the cheap equipment is
called for though, as we shall see, matters are not so straightforward. The final stage is
to calculate a Consistency Ratio (CR) to measure how consistent the judgements have
been relative to large samples of purely random judgements. If the CR is much in
excess of 0.1 the judgements are untrustworthy because they are too close for comfort to
randomness and the exercise is valueless or must be repeated. It is easy to make a
minimum number of judgements after which the rest can be calculated to enforce a
perhaps unrealistically perfect consistency.
The AHP is sometimes sadly misused and the analysis stops with the calculation of the
eigenvector from the pairwise comparisons of relative importance (sometimes without
even computing the CR!) but the AHP’s true subtlety lies in the fact that it is, as its
name says, a Hierarchy process. The first eigenvector has given the relative importance
attached to requirements, such as cost and reliability, but different machines contribute
to differing extents to the satisfaction of those requirements. Thus, subsequent matrices
can be developed to show how X, Y and Z respectively satisfy the needs of the firm.
(The matrices from this lower level in the hierarchy will each have their own
0/5000
一个基本的但是很合理的假设是，如果 A 的属性是绝对更多
重要比属性 B 和额定在 9，那么 B 必须绝对较少重要
比 A 并价值在 1/9。
所有的因素要考虑，通常不进行这些成对比较
完成超过 7 和矩阵。矩阵是很特别的形式，
整齐地支持计算，然后紧随其后 (Saaty 是非常尊敬
数学家）。
下一步是计算的相对权重，重要性或值，列表的
的因素，如成本和可操作性，有关这个问题的
（从技术上讲，此列表称为特征向量）。如果或许，成本是非常更多
比可操作性、 重要然后，在一个简单的解释，廉价的设备是
呼吁虽然，正如我们将看到，事情并不这么简单。最后一步是
来计算一致性比率 (CR) 来衡量如何一致判决有
已与大样本的纯粹随机的判决。如果 CR 是很多在
0 的过剩。1 判决是不可信的因为他们是太近的安慰，
随机性和行使是毫无价值或必须重复。它是容易使
作出的判决后，其余部分可以计算出强制执行最低数量
也许不切实际地完美的一致性。
AHP 有时可悲的是被误用和分析停止与计算的
从相对重要性的成对比较的特征向量 （有时无
甚至计算 CR!) 但层次分析法的真正精妙在于，它是作为其
名称说，一个层次结构的过程。第一个特征向量已给予的相对重要性
附加要求，如成本和可靠性，但不同的机器贡献
到满意的那些要求的不同程度。因此，随后矩阵
可以开发以显示如何 X、 Y 和 Z 分别满足需要的公司。
(The matrices from this lower level in the hierarchy will each have their own
重要比属性 B 和额定在 9，那么 B 必须绝对较少重要
比 A 并价值在 1/9。
所有的因素要考虑，通常不进行这些成对比较
完成超过 7 和矩阵。矩阵是很特别的形式，
整齐地支持计算，然后紧随其后 (Saaty 是非常尊敬
数学家）。
下一步是计算的相对权重，重要性或值，列表的
的因素，如成本和可操作性，有关这个问题的
（从技术上讲，此列表称为特征向量）。如果或许，成本是非常更多
比可操作性、 重要然后，在一个简单的解释，廉价的设备是
呼吁虽然，正如我们将看到，事情并不这么简单。最后一步是
来计算一致性比率 (CR) 来衡量如何一致判决有
已与大样本的纯粹随机的判决。如果 CR 是很多在
0 的过剩。1 判决是不可信的因为他们是太近的安慰，
随机性和行使是毫无价值或必须重复。它是容易使
作出的判决后，其余部分可以计算出强制执行最低数量
也许不切实际地完美的一致性。
AHP 有时可悲的是被误用和分析停止与计算的
从相对重要性的成对比较的特征向量 （有时无
甚至计算 CR!) 但层次分析法的真正精妙在于，它是作为其
名称说，一个层次结构的过程。第一个特征向量已给予的相对重要性
附加要求，如成本和可靠性，但不同的机器贡献
到满意的那些要求的不同程度。因此，随后矩阵
可以开发以显示如何 X、 Y 和 Z 分别满足需要的公司。
(The matrices from this lower level in the hierarchy will each have their own
正在翻譯中..
A basic, but very reasonable, assumption is that if attribute A is absolutely more
important than attribute B and is rated at 9, then B must be absolutely less important
than A and is valued at 1/9.
These pairwise comparisons are carried out for all factors to be considered, usually not
more than 7, and the matrix is completed. The matrix is of a very particular form which
neatly supports the calculations which then ensue (Saaty was a very distinguished
mathematician).
The next step is the calculation of a list of the relative weights, importance, or value, of
the factors, such as cost and operability, which are relevant to the problem in question
(technically, this list is called an eigenvector). If, perhaps, cost is very much more
important than operability, then, on a simple interpretation, the cheap equipment is
called for though, as we shall see, matters are not so straightforward. The final stage is
to calculate a Consistency Ratio (CR) to measure how consistent the judgements have
been relative to large samples of purely random judgements. If the CR is much in
excess of 0.1 the judgements are untrustworthy because they are too close for comfort to
randomness and the exercise is valueless or must be repeated. It is easy to make a
minimum number of judgements after which the rest can be calculated to enforce a
perhaps unrealistically perfect consistency.
The AHP is sometimes sadly misused and the analysis stops with the calculation of the
eigenvector from the pairwise comparisons of relative importance (sometimes without
even computing the CR!) but the AHP’s true subtlety lies in the fact that it is, as its
name says, a Hierarchy process. The first eigenvector has given the relative importance
attached to requirements, such as cost and reliability, but different machines contribute
to differing extents to the satisfaction of those requirements. Thus, subsequent matrices
can be developed to show how X, Y and Z respectively satisfy the needs of the firm.
(The matrices from this lower level in the hierarchy will each have their own
important than attribute B and is rated at 9, then B must be absolutely less important
than A and is valued at 1/9.
These pairwise comparisons are carried out for all factors to be considered, usually not
more than 7, and the matrix is completed. The matrix is of a very particular form which
neatly supports the calculations which then ensue (Saaty was a very distinguished
mathematician).
The next step is the calculation of a list of the relative weights, importance, or value, of
the factors, such as cost and operability, which are relevant to the problem in question
(technically, this list is called an eigenvector). If, perhaps, cost is very much more
important than operability, then, on a simple interpretation, the cheap equipment is
called for though, as we shall see, matters are not so straightforward. The final stage is
to calculate a Consistency Ratio (CR) to measure how consistent the judgements have
been relative to large samples of purely random judgements. If the CR is much in
excess of 0.1 the judgements are untrustworthy because they are too close for comfort to
randomness and the exercise is valueless or must be repeated. It is easy to make a
minimum number of judgements after which the rest can be calculated to enforce a
perhaps unrealistically perfect consistency.
The AHP is sometimes sadly misused and the analysis stops with the calculation of the
eigenvector from the pairwise comparisons of relative importance (sometimes without
even computing the CR!) but the AHP’s true subtlety lies in the fact that it is, as its
name says, a Hierarchy process. The first eigenvector has given the relative importance
attached to requirements, such as cost and reliability, but different machines contribute
to differing extents to the satisfaction of those requirements. Thus, subsequent matrices
can be developed to show how X, Y and Z respectively satisfy the needs of the firm.
(The matrices from this lower level in the hierarchy will each have their own
正在翻譯中..
一个基本的，但非常合理的假设是，如果属性，一个是绝对地
比属性B重要的额定为9，则B必须比完全不重要
和价值在1 / 9。
这些成对比较都是要考虑的因素进行了
，通常不超过7，和矩阵完成。矩阵是一个非常特殊的形式，
整齐地支持计算随之而来（萨蒂是一个非常杰出的数学家
）。下一步是一系列的相对权重，计算的重要性，或价值，
的因素，如成本和可操作性，这是有问题的
有关的问题（技术上，这个列表被称为一个特征向量）。如果，也许，成本是非常
重要的可操作性，然后，在一个简单的解释，廉价的设备
称尽管，正如我们将看到的，事情不是那么简单。最后阶段是
来计算一个一致性比率（CR）来衡量一致的判断有
是相对于纯粹的大样本随机的判断。如果Cr在超过0多
。1判断是不可信的因为他们太近，以
随机性和运动是毫无价值的舒适和必须重复。它很容易使一
最低数量的判断后，其余的可以计算执行
也许不完全一致。
AHP有时是可悲的滥用和分析站的
计算从相对重要性的两两比较的特征向量（有时甚至没有
计算CR！）但分析真实的精妙之处在于事实上，为
名说，一个层次。第一特征向量给出了相对重要性
附加要求，如成本和可靠性，但不同的机器有助于
在不同程度上能够满足这些要求的。因此，随后的矩阵
可以显示X，Y和Z分别满足公司的需求。
（从低级别的矩阵将各有自己的
比属性B重要的额定为9，则B必须比完全不重要
和价值在1 / 9。
这些成对比较都是要考虑的因素进行了
，通常不超过7，和矩阵完成。矩阵是一个非常特殊的形式，
整齐地支持计算随之而来（萨蒂是一个非常杰出的数学家
）。下一步是一系列的相对权重，计算的重要性，或价值，
的因素，如成本和可操作性，这是有问题的
有关的问题（技术上，这个列表被称为一个特征向量）。如果，也许，成本是非常
重要的可操作性，然后，在一个简单的解释，廉价的设备
称尽管，正如我们将看到的，事情不是那么简单。最后阶段是
来计算一个一致性比率（CR）来衡量一致的判断有
是相对于纯粹的大样本随机的判断。如果Cr在超过0多
。1判断是不可信的因为他们太近，以
随机性和运动是毫无价值的舒适和必须重复。它很容易使一
最低数量的判断后，其余的可以计算执行
也许不完全一致。
AHP有时是可悲的滥用和分析站的
计算从相对重要性的两两比较的特征向量（有时甚至没有
计算CR！）但分析真实的精妙之处在于事实上，为
名说，一个层次。第一特征向量给出了相对重要性
附加要求，如成本和可靠性，但不同的机器有助于
在不同程度上能够满足这些要求的。因此，随后的矩阵
可以显示X，Y和Z分别满足公司的需求。
（从低级别的矩阵将各有自己的
正在翻譯中..
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