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1 Decisions Involving Multiple Objectives: SMART.

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Presentation on theme: "1 Decisions Involving Multiple Objectives: SMART."— Presentation transcript:

1 1 Decisions Involving Multiple Objectives: SMART

2 2 Objectives and Attributes An objective = an indication of preferred direction of movement, i.e. ‘minimize’ or ‘maximize’ An attribute is used to measure performance in relation to an objective

3 3 An office location problem Location of officeAnnual rent ($) Addison Square Bilton Village Carlisle Walk Denver Street Elton Street Filton Village Gorton Square10 000

4 4 Main stages of SMART 1Identify decision maker(s) 2Identify alternative courses of action 3 Identify the relevant attributes 4 Assess the performance of the alternatives on each attribute 5 Determine a weight for each attribute 6 For each alternative, take a weighted average of the values assigned to that alternative 7 Make a provisional decision 8 Perform sensitivity analysis

5 5 Value tree Costs Benefits Turnover Working conditions Rent Electricity Cleaning Closeness Visibility Image to customers Size Comfort Car parking

6 6 Is the value tree an accurate and useful representation of the decision maker’s concerns? 1.Completeness 2.Operationality 3. Decomposability 4. Absence of redundancy 5. Minimum size

7 7 Costs associated with the seven offices AnnualAnnual OfficeAnnualcleaningelectricity Total rent ($)costs ($)costs ($) costs ($) Addison Square Bilton Village Carlisle Walk Denver Street Elton Street Filton Village Gorton Square

8 8 Direct rating for ‘Office Image’ -Ranking from most preferred to least preferred. 1.Addison Square 2.Elton Street 3.Filton Village 4.Denver Street 5.Gorton Square 6.Bilton Village 7.Carlisle Walk

9 9 Direct rating - Assigning values

10 10 Using a value function to assign values

11 11 Values for the office location problem

12 12 Closeness to customers VisibilityImageSizeComfortCar parking Best Worst Best Worst Best Worst Best Worst Best Worst 70 Determining swing weights

13 13 For example... A swing from the worst ‘image’ to the best ‘image’ is considered to be 70% as important as a swing from the worst to the best location for ‘closeness to customers’...so ‘image’ is assigned a weight of 70.

14 14 Normalizing weights Normalized weights AttributeOriginal weights (rounded) Closeness to customers10032 Visibility 8026 Image 7023 Size 3010 Comfort 20 6 Car-parking facilities

15 15 Calculating aggregate benefits for each location Addison Square AttributeValues Weight Value  weight Closeness to customers Visibility Image Size Comfort Car-parking facilities so aggregate benefits = 8080/100 = 80.8

16 16 Table Values and weights for the office location problem ____________________________________________________________________ Attribute WeightOffice ABCDEFG _____________________________________________________________________________________ Closeness Visibility Image Size Comfort Car parking Aggregate benefits _____________________________________________________________________________________

17 17 Trading benefits against costs

18 18 Sensitivity analysis


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