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Jason Chen, Ph.D. Professor of MIS School of Business

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Presentation on theme: "Jason Chen, Ph.D. Professor of MIS School of Business"— Presentation transcript:

1 Analytical Hierarchy Process (AHP): A Multi-Objective Decision Making Technique
Jason Chen, Ph.D. Professor of MIS School of Business Gonzaga University Spokane, WA 99258

2 Analytical Hierarchy Process
In many situations one may not be able to assign weights to the different decision factors. Therefore one must rely on a technique that will allow the estimation of the weights. What is a solution? One such process, The Analytical Hierarchy Process (AHP), involves pairwise comparisons between the various factors.

3 Analytical Hierarchy Process (cont.)
The process is started by the decision maker creating the value tree associated with the problem. The proceed by carrying out pairwise comparisons, both between Alternatives on each factor, and Factors at a given node.

4 Application Case of AHP
Jane is about to graduate from college and is trying to determine which job offer to accept. She plans to choose between three offers by determining how well each offer meets the following criteria (objectives): High starting salary Quality of life in city where job is located Interest of work Nearness of job to family

5 Assumptions Jane has hard time in prioritizing those criteria. In other words, she needs to find one way to decide the weights for those criteria. AHP provides such a function.

6 Determine the problem What job offer will give Jane possibly highest satisfaction? Structure the hierarchy by putting the top objective (satisfaction with job), criteria, and alternatives as follows.

7 Structure of the Problem
Job A Job B Job C

8 Structure of the Problem
Job A Job B Job C Web site:

9 The Principle of the AHP …
The principle of the AHP relies on the pairwise comparison. This comparison is carried out using a scale from 1 to 9 as follows: 1 Equally preferred 2 Equally to Moderately preferred 3 Moderately preferred 4 Moderately to Strongly preferred 5 Strongly preferred 6 Strongly to Very Strongly preferred 7 Very Strongly preferred 8 Very to Extremely Strongly preferred 9 Extremely preferred

10 A pairwise comparison matrix for the criteria level
Satisfaction with a Job Web site:

11 Using the same steps of 3 and 4 to determine the score of each alternative on each criterion. Take the first criterion Salary as an example. One pairwise matrix is constructed as follows:

12

13 double click click

14 Result from double clicking “salary”

15 Result from double clicking “life quality”

16 Result from double clicking “interest”

17 Result from double clicking “nearness to family”

18 click

19

20 Result from “Analysis of Composite Priorities … “
click

21 Result from “Analysis of Composite Priorities … “ – with Values

22 Result as Text

23

24

25

26

27 Your Turn -- Try It! Http://www.hipre.hut.fi File name: mbus633.jmd
(BiEMBA.jmd)

28 Double click

29 Edit the element’s Name
Select this

30 Steps of creating “Links”
Left click

31 Complete the remaining links
Steps of creating “Links” (cont.) Right click Complete the remaining links

32 Steps of creating “Links” (cont.)

33 Steps of assigning weights to different decision factors
Goal level (satisfaction with job) Criteria level (salary, life quality, interest, and nearness to family)

34 Steps of assigning weights to different decision factors
Double left click

35 Steps of assigning weights to different decision factors (cont.)

36 Steps of assigning weights to different decision factors (cont.)

37 Steps of assigning weights to different decision factors (cont.)

38 Steps of assigning weights to different decision factors (cont.)

39 Steps of assigning weights to different decision factors (cont.)

40 Steps of assigning weights to different decision factors (cont.)

41 Steps of assigning weights to different decision factors (cont.)

42 Steps of assigning weights to different decision factors (cont.)
Double left click

43 Steps of assigning weights to different decision factors (cont.)

44 Steps of assigning weights to different decision factors (cont.)

45 Perform Analysis

46 Perform Analysis (cont.)

47 Perform Analysis (cont.)

48 Perform Analysis (cont.)

49

50 Save your work

51 Have a fun! Http://www.hipre.hut.fi File name: mbus633.jmd
(BiEMBA.jmd)


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