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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 Square30 000 Bilton Village15 000 Carlisle Walk 5 000 Denver Street12 000 Elton Street30 000 Filton Village15 000 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 Square30 000 3000 200035 000 Bilton Village15 000 2000 80017 800 Carlisle Walk 5 000 1000 700 6 700 Denver Street12 000 1000 110014 100 Elton Street30 000 2500 230034 800 Filton Village15 000 1000 260018 600 Gorton Square10 000 1100 90012 000

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 Best100 80 0Worst 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 10 3 310 100

15 15 Calculating aggregate benefits for each location Addison Square AttributeValues Weight Value  weight Closeness to customers100323200 Visibility 60261560 Image100232300 Size 7510 750 Comfort 0 6 0 Car-parking facilities 90 3 270 8080 so aggregate benefits = 8080/100 = 80.8

16 16 Table 3.2 - Values and weights for the office location problem ____________________________________________________________________ Attribute WeightOffice ABCDEFG _____________________________________________________________________________________ Closeness 32 100 20 807040 060 Visibility 26 60 80 705060 0 100 Image 23100 10 030907020 Size 10 75 30 055 100 050 Comfort 6 0 100 1030 608050 Car parking 3 90 301009070 080 Aggregate benefits80.839.447.452.364.820.9 60.2 _____________________________________________________________________________________

17 17 Trading benefits against costs

18 18 Sensitivity analysis


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