After launching Expert Choice, select the F ile, N ew option, and after selecting a destination folder, enter a file name such as CARS. (Expert Choice add the AHP file extension.)
Next, enter a description for your goal, such as, “Select the best car.”
1. Determine the weights of the alternatives for each criterion.
2. Determine the priorities or weights of the criteria in achieving the goal.
3. Determine the overall weight of each alternative in achieving the goal. This is accomplished by combining the results of the first two stages and is called synthesis.
To complete the first stage, the couple can base their judgments on the following (hypothetical) performance information.
All alternative pairwise comparisons should be based on data.
9.
HYPOTHETICAL DATA FOR CAR PURCHASE EXAMPLE Car Cost Safety* Appearance Honda $22,000 28 Sporty Mazda 28,500 39 Slick Volvo 33,000 52 Dull * Safety Rating from a consumer testing service - the higher the number, the safer the car.
Assuming perfect consistency of judgments, we would expect that the Honda ($22000) is 4 times (that is, moderately to strongly) preferred to the Volvo ($33000).
Notice that no variation is seen across the rows because the judgments are perfectly consistent.
For the third column, judgments totaling 7 were awarded. The Honda received 4 of 7 (57.1%), the Mazda 2 of 7 (28.6%), and the Volvo 1 of 7 (14.3%) of the weight.
Similar comparisons can be made for the other two columns.
Sliding the bar between Honda and Mazda to the left so that it rests on the 2 means that the Honda is two times better than the Mazda when considering cost.
If the Mazda were 2 times better than the Honda, the bar would be slid to the 2 on the right.
The other comparisons are entered in a similar fashion.
One example of natural inconsistency is in a sporting contest.
If team A is twice as likely to beat team B, and if team B is three times as likely to beat team C, this does not necessarily imply that team A is six times as likely to beat team C.
This inconsistency may result because of the way that the teams “match-up” overall.
In Expert Choice sensitivity analysis from the GOAL shows how the weights and the rankings of the alternatives change if some or all of the criteria weights change.
There are five graphical sensitivity analysis modes available: Performance, Dynamic, Gradient, Two-Dimensional, and Difference.
The first three show how a change in a criterion weight affects the final weights of the alternatives.
The last two show how the alternatives perform with respect to any two criteria.
Performance : places all sensitivity information on a single chart with horizontal line graphs for the alternatives linked to vertical bars for the criteria.
Dynamic : two sets of dynamically linked horizontal bar graphs: one for criteria and one for alternatives.
Gradient : a line graph that shows how the weights of the alternatives vary according to the weight assigned to a specific criterion. (Use the X -Axis to change the selected criterion.)
Two-Dimensional : shows how well the alternatives perform with respect to any two criteria.
Difference : a graph that shows the differences between any two alternatives for any criterion.
An important use of sensitivity analysis is to determine how much a given criterion weight must change before there is a change in the rankings of the two highest alternatives.
This type of breakeven analysis can be easily done in Expert Choice.
Choose D ynamic from the Sensitivit y -Graphs option.
Drag the cost criterion bar 30.9% to approximately 45.9%, and see that the Volvo and Honda have the same highest final weight.
The final rankings are relatively insensitive to a change in the cost weight since it had to be increased by almost 50% to get a change in the final rankings.
The sensitivity results are different for the ideal mode.
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