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Slides Prepared by JOHN S. LOUCKS St. Edward’s University

Slides Prepared by JOHN S. LOUCKS St. Edward’s University. Chapter 15 Multicriteria Decision Problems. Goal Programming Goal Programming: Formulation and Graphical Solution Scoring Models Analytic Hierarchy Process (AHP) Establishing Priorities Using AHP

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Slides Prepared by JOHN S. LOUCKS St. Edward’s University

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  1. Slides Prepared by JOHN S. LOUCKS St. Edward’s University

  2. Chapter 15Multicriteria Decision Problems • Goal Programming • Goal Programming: Formulation and Graphical Solution • Scoring Models • Analytic Hierarchy Process (AHP) • Establishing Priorities Using AHP • Using AHP to Develop an Overall Priority Ranking

  3. Goal Programming • Goal programming may be used to solve linear programs with multiple objectives, with each objective viewed as a "goal". • In goal programming, di+ and di- , deviation variables, are the amounts a targeted goal i is overachieved or underachieved, respectively. • The goals themselves are added to the constraint set with di+ and di- acting as the surplus and slack variables. • One approach to goal programming is to satisfy goals in a priority sequence. Second-priority goals are pursued without reducing the first-priority goals, etc.

  4. Goal Programming • For each priority level, the objective function is to minimize the (weighted) sum of the goal deviations. • Previous "optimal" achievements of goals are added to the constraint set so that they are not degraded while trying to achieve lesser priority goals.

  5. Goal Programming Approach Step 1: Decide the priority level of each goal. Step 2: Decide the weight on each goal. If a priority level has more than one goal, for each goal i decide the weight, wi , to be placed on the deviation(s), di+ and/or di-, from the goal. Step 3: Set up the initial linear program. Min w1d1+ + w2d2- s.t. Functional Constraints, and Goal Constraints Step 4: Solve the current linear program. If there is a lower priority level, go to step 5. Otherwise, a final solution has been reached.

  6. Goal Programming Approach Step 5: Set up the new linear program. Consider the next-lower priority level goals and formulate a new objective function based on these goals. Add a constraint requiring the achievement of the next-higher priority level goals to be maintained. The new linear program might be: Min w3d3+ + w4d4- s.t. Functional Constraints, Goal Constraints, and w1d1+ + w2d2- = k Go to step 4. (Repeat steps 4 and 5 until all priority levels have been examined.)

  7. Example: Conceptual Products Conceptual Products is a computer company that produces the CP400 and the CP500 computers. The computers use different mother boards produced in abundant supply by the company, but use the same cases and disk drives. The CP400 models use two floppy disk drives and no zip disk drives whereas the CP500 models use one floppy disk drive and one zip disk drive.

  8. Example: Conceptual Products The disk drives and cases are bought from vendors. There are 1000 floppy disk drives, 500 zip disk drives, and 600 cases available to Conceptual Products on a weekly basis. It takes one hour to manufacture a CP400 and its profit is $200 and it takes one and one-half hours to manufacture a CP500 and its profit is $500.

  9. Example: Conceptual Products The company has four goals which are given below: Priority 1: Meet a state contract of 200 CP400 machines weekly. (Goal 1) Priority 2: Make at least 500 total computers weekly. (Goal 2) Priority 3: Make at least $250,000 weekly. (Goal 3) Priority 4: Use no more than 400 man-hours per week. (Goal 4)

  10. Example: Conceptual Products • Variables x1 = number of CP400 computers produced weekly x2 = number of CP500 computers produced weekly di- = amount the right hand side of goal i is deficient di+ = amount the right hand side of goal i is exceeded • Functional Constraints Availability of floppy disk drives: 2x1 + x2< 1000 Availability of zip disk drives: x2< 500 Availability of cases: x1 + x2< 600

  11. Example: Conceptual Products • Goals (1) 200 CP400 computers weekly: x1 + d1- - d1+ = 200 (2) 500 total computers weekly: x1 + x2 + d2- - d2+ = 500 (3) $250(in thousands) profit: .2x1 + .5x2 + d3- - d3+ = 250 (4) 400 total man-hours weekly: x1 + 1.5x2 + d4- - d4+ = 400 Non-negativity: x1, x2, di-, di+> 0 for all i

  12. Example: Conceptual Products • Objective Functions Priority 1: Minimize the amount the state contract is not met: Min d1- Priority 2: Minimize the number under 500 computers produced weekly: Min d2- Priority 3: Minimize the amount under $250,000 earned weekly: Min d3- Priority 4: Minimize the man-hours over 400 used weekly: Min d4+

  13. Example: Conceptual Products • Formulation Summary Min P1(d1-) + P2(d2-) + P3(d3-) + P4(d4+) s.t. 2x1 +x2< 1000 +x2< 500 x1 +x2< 600 x1 +d1- -d1+ = 200 x1 +x2 +d2- -d2+ = 500 .2x1+ .5x2 +d3- -d3+ = 250 x1+1.5x2 +d4- -d4+ = 400 x1, x2, d1-, d1+, d2-, d2+, d3-, d3+, d4-, d4+> 0

  14. Example: Conceptual Products • Graphical Solution, Iteration 1 To solve graphically, first graph the functional constraints. Then graph the first goal: x1 = 200. Note on the next slide that there is a set of points that exceed x1 = 200 (where d1- = 0).

  15. Example: Conceptual Products • Functional Constraints and Goal 1 Graphed x2 1000 800 600 400 200 200 400 600 800 1000 1200 2x1 + x2< 1000 Goal 1: x1> 200 x2 < 500 x1 + x2< 600 Points Satisfying Goal 1 x1

  16. Example: Conceptual Products • Graphical Solution, Iteration 2 Now add Goal 1 as x1> 200 and graph Goal 2: x1 + x2 = 500. Note on the next slide that there is still a set of points satisfying the first goal that also satisfies this second goal (where d2- = 0).

  17. Example: Conceptual Products • Goal 1 (Constraint) and Goal 2 Graphed x2 1000 800 600 400 200 200 400 600 800 1000 1200 2x1 + x2< 1000 Goal 1: x1> 200 x2 < 500 x1 + x2< 600 Points Satisfying Both Goals 1 and 2 Goal 2: x1 + x2> 500 x1

  18. Example: Conceptual Products • Graphical Solution, Iteration 3 Now add Goal 2 as x1 + x2> 500 and Goal 3: .2x1 + .5x2 = 250. Note on the next slide that no points satisfy the previous functional constraints and goals and satisfy this constraint. Thus, to Min d3-, this minimum value is achieved when we Max .2x1 + .5x2. Note that this occurs at x1 = 200 and x2 = 400, so that .2x1 + .5x2 = 240 or d3- = 10.

  19. Example: Conceptual Products • Goal 2 (Constraint) and Goal 3 Graphed x2 1000 800 600 400 200 200 400 600 800 1000 1200 2x1 + x2< 1000 Goal 1: x1> 200 x2 < 500 x1 + x2< 600 (200,400) Points Satisfying Both Goals 1 and 2 Goal 2: x1 + x2> 500 Goal 3: .2x1 + .5x2 = 250 x1

  20. A Scoring Model for Job Selection • A graduating college student with a double major in Finance and Accounting has received the following three job offers: • financial analyst for an investment firm in Chicago • accountant for a manufacturing firm in Denver • auditor for a CPA firm in Houston

  21. A Scoring Model for Job Selection • The student made the following comments: • “The financial analyst position provides the best opportunity for my long-run career advancement.” • “I would prefer living in Denver rather than in Chicago or Houston.” • “I like the management style and philosophy at the Houston CPA firm the best.” • Clearly, this is a multicriteria decision problem.

  22. A Scoring Model for Job Selection • Considering only the long-run career advancement criterion: • The financial analyst position in Chicago is the best decision alternative. • Considering only the location criterion: • The accountant position in Denver is the best decision alternative. • Considering only the style criterion: • The auditor position in Houston is the best alternative.

  23. A Scoring Model for Job Selection • Steps Required to Develop a Scoring Model Step 1: List the decision-making criteria. Step 2: Assign a weight to each criterion. Step 3: Rate how well each decision alternative satisfies each criterion. Step 4: Compute the score for each decision alternative. Step 5: Order the decision alternatives from highest score to lowest score. The alternative with the highest score is the recommended alternative.

  24. A Scoring Model for Job Selection • Mathematical Model Sj= S wi rij i where: rij= rating for criterion i and decision alternative j Sj = score for decision alternative j

  25. A Scoring Model for Job Selection • Step 1: List the criteria (important factors). • Career advancement • Location • Management • Salary • Prestige • Job Security • Enjoyable work

  26. A Scoring Model for Job Selection • Five-Point Scale Chosen for Step 2 ImportanceWeight Very unimportant 1 Somewhat unimportant 2 Average importance 3 Somewhat important 4 Very important 5

  27. A Scoring Model for Job Selection • Step 2: Assign a weight to each criterion. CriterionImportanceWeight Career advancement Very important 5 Location Average importance 3 Management Somewhat important 4 Salary Average importance 3 Prestige Somewhat unimportant 2 Job security Somewhat important 4 Enjoyable work Very important 5

  28. A Scoring Model for Job Selection • Nine-Point Scale Chosen for Step 3 Level of SatisfactionRating Extremely low 1 Very low 2 Low 3 Slightly low 4 Average 5 Slightly high 6 High 7 Very high 8 Extremely high 9

  29. A Scoring Model for Job Selection • Step 3: Rate how well each decision alternative satisfies each criterion. Decision Alternative Analyst Accountant Auditor CriterionChicagoDenverHouston Career advancement 8 6 4 Location 3 8 7 Management 5 6 9 Salary 6 7 5 Prestige 7 5 4 Job security 4 7 6 Enjoyable work 8 6 5

  30. A Scoring Model for Job Selection • Step 4: Compute the score for each decision alternative. Decision Alternative 1 - Analyst in Chicago Criterion Weight (wi ) Rating (ri1) wiri1 Career advancement 5 x 8 = 40 Location 3 3 9 Management 4 5 20 Salary 3 6 18 Prestige 2 7 14 Job security 4 4 16 Enjoyable work 5 8 40 Score 157

  31. A Scoring Model for Job Selection • Step 4: Compute the score for each decision alternative. Sj= Swi rij i S1 = 5(8)+3(3)+4(5)+3(6)+2(7)+4(4)+5(8) = 157 S2 = 5(6)+3(8)+4(6)+3(7)+2(5)+4(7)+5(6) = 167 S3 = 5(4)+3(7)+4(9)+3(5)+2(4)+4(6)+5(5) = 149

  32. A Scoring Model for Job Selection • Step 4: Compute the score for each decision alternative. Decision Alternative Analyst Accountant Auditor CriterionChicagoDenverHouston Career advancement 40 30 20 Location 9 24 21 Management 20 24 36 Salary 18 21 15 Prestige 14 10 8 Job security 16 28 24 Enjoyable work 40 3025 Score 157 167 149

  33. A Scoring Model for Job Selection • Step 5: Order the decision alternatives from highest score to lowest score. The alternative with the highest score is the recommended alternative. • The accountant position in Denver has the highest score and is the recommended decision alternative. • Note that the analyst position in Chicago ranks first in 4 of 7 criteria compared to only 2 of 7 for the accountant position in Denver. • But when the weights of the criteria are considered, the Denver position is superior to the Chicago job.

  34. A Scoring Model for Job Selection • Partial Spreadsheet Showing Steps 1 - 3

  35. A Scoring Model for Job Selection • Partial Spreadsheet Showing Formulas for Step 4

  36. A Scoring Model for Job Selection • Partial Spreadsheet Showing Results of Step 4

  37. Analytic Hierarchy Process The Analytic Hierarchy Process (AHP), is a procedure designed to quantify managerial judgments of the relative importance of each of several conflicting criteria used in the decision making process.

  38. Analytic Hierarchy Process • Step 1: List the Overall Goal, Criteria, and Decision Alternatives • Step 2: Develop a Pairwise Comparison Matrix Rate the relative importance between each pair of decision alternatives. The matrix lists the alternatives horizontally and vertically and has the numerical ratings comparing the horizontal (first) alternative with the vertical (second) alternative. Ratings are given as follows: . . . continued ------- For each criterion, perform steps 2 through 5 -------

  39. Analytic Hierarchy Process • Step 2: Pairwise Comparison Matrix (continued) Compared to the second alternative, the first alternative is: Numerical rating extremely preferred 9 very strongly preferred 7 strongly preferred 5 moderately preferred 3 equally preferred 1

  40. Analytic Hierarchy Process • Step 2: Pairwise Comparison Matrix (continued) Intermediate numeric ratings of 8, 6, 4, 2 can be assigned. A reciprocal rating (i.e. 1/9, 1/8, etc.) is assigned when the second alternative is preferred to the first. The value of 1 is always assigned when comparing an alternative with itself.

  41. Analytic Hierarchy Process • Step 3: Develop a Normalized Matrix Divide each number in a column of the pairwise comparison matrix by its column sum. • Step 4: Develop the Priority Vector Average each row of the normalized matrix. These row averages form the priority vector of alternative preferences with respect to the particular criterion. The values in this vector sum to 1.

  42. Analytic Hierarchy Process • Step 5: Calculate a Consistency Ratio The consistency of the subjective input in the pairwise comparison matrix can be measured by calculating a consistency ratio. A consistency ratio of less than .1 is good. For ratios which are greater than .1, the subjective input should be re-evaluated. • Step 6: Develop a Priority Matrix After steps 2 through 5 has been performed for all criteria, the results of step 4 are summarized in a priority matrix by listing the decision alternatives horizontally and the criteria vertically. The column entries are the priority vectors for each criterion.

  43. Analytic Hierarchy Process • Step 7: Develop a Criteria Pairwise Development Matrix This is done in the same manner as that used to construct alternative pairwise comparison matrices by using subjective ratings (step 2). Similarly, normalize the matrix (step 3) and develop a criteria priority vector (step 4). • Step 8: Develop an Overall Priority Vector Multiply the criteria priority vector (from step 7) by the priority matrix (from step 6).

  44. Determining the Consistency Ratio • Step 1: For each row of the pairwise comparison matrix, determine a weighted sum by summing the multiples of the entries by the priority of its corresponding (column) alternative. • Step 2: For each row, divide its weighted sum by the priority of its corresponding (row) alternative. • Step 3: Determine the average, max, of the results of step 2.

  45. Determining the Consistency Ratio • Step 4: Compute the consistency index, CI, of the n alternatives by: CI = (max - n)/(n - 1). • Step 5: Determine the random index, RI, as follows: Number of Random Number of Random Alternative (n)Index (RI)Alternative (n)Index (RI) 3 0.58 6 1.24 4 0.90 7 1.32 5 1.12 8 1.41 • Step 6: Compute the consistency ratio: CR = CR/RI.

  46. Example: Gill Glass Designer Gill Glass must decide which of three manufacturers will develop his "signature" toothbrushes. Three factors seem important to Gill: (1) his costs; (2) reliability of the product; and, (3) delivery time of the orders. The three manufacturers are Cornell Industries, Brush Pik, and Picobuy. Cornell Industries will sell toothbrushes to Gill Glass for $100 per gross, Brush Pik for $80 per gross, and Picobuy for $144 per gross. Gill has decided that in terms of price, Brush Pik is moderately preferred to Cornell and very strongly preferred to Picobuy. In turn Cornell is strongly to very strongly preferred to Picobuy.

  47. Example: Gill Glass • Hierarchy for the Manufacturer Selection Problem Overall Goal Select the Best Toothbrush Manufacturer Criteria Cost Reliability Delivery Time Cornell Brush Pik Picobuy Cornell Brush Pik Picobuy Cornell Brush Pik Picobuy Decision Alternatives

  48. Example: Gill Glass • Forming the Pairwise Comparison Matrix For Cost • Since Brush Pik is moderately preferred to Cornell, Cornell's entry in the Brush Pik row is 3 and Brush Pik's entry in the Cornell row is 1/3. • Since Brush Pik is very strongly preferred to Picobuy, Picobuy's entry in the Brush Pik row is 7 and Brush Pik's entry in the Picobuy row is 1/7. • Since Cornell is strongly to very strongly preferred to Picobuy, Picobuy's entry in the Cornell row is 6 and Cornell's entry in the Picobuy row is 1/6.

  49. Example: Gill Glass • Pairwise Comparison Matrix for Cost Cornell Brush Pik Picobuy Cornell 1 1/3 6 Brush Pik 3 1 7 Picobuy 1/6 1/7 1

  50. Example: Gill Glass • Normalized Matrix for Cost Divide each entry in the pairwise comparison matrix by its corresponding column sum. For example, for Cornell the column sum = 1 + 3 + 1/6 = 25/6. This gives: Cornell Brush Pik Picobuy Cornell 6/25 7/31 6/14 Brush Pik 18/25 21/31 7/14 Picobuy 1/25 3/31 1/14

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