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El siguiente material se reproduce con fines estrictamente acadmicos para estudiantes, profesores y colaboradores de la Universidad ICESI, de acuerdo con el Artculo 32 de la Ley 23 de 1982. Y con el Artculo 22 de la Decisin 351 de la Comisin del Acuerdo de Cartagena. ARTCULO 32: Es permitido utilizar obras literarias o artsticas o parte de ellas, a ttulo de ilustracin en obras destinadas a la enseanza, por medio de publicaciones, emisiones o radiodifusiones o grabaciones sonoras o visuales, dentro de los lmites justificados por el fin propuesto o comunicar con propsito de enseanza la obra radiodifundida para fines escolares educativos, universitarios y de formacin personal sin fines de lucro, con la obligacin de mencionar el nombre del autor y el ttulo de las as utilizadas. Artculo 22 de la Decisin 351 de la Comisin del Acuerdo Cartagena. ARTCULO 22: Sin prejuicio de lo dispuesto en el Captulo V y en el Artculo anterior, ser lcito realizar, sin la autorizacin del autor y sin el pago de remuneracin alguna, los siguientes actos: b) Reproducir por medio reprogrficos para la enseanza o para la realizacin de exmenes en instituciones educativas, en la medida justificada por el fin que se persiga, artculos lcitamente publicados en peridicos o colecciones peridicas, o breves extractos de obras lcitamente publicadas, a condicin que tal utilizacin se haga conforme a los usos honrados y que la misma no sea objeto de venta o transaccin a ttulo oneroso, ni tenga directa o indirectamente fines de lucro;....
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n Ivi 9 mu pi :8
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DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
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AN OVERVIEW OF THE ANALYSIS
below:
Location of office
Addison 8ilton Village Carlisle Walk Denver Street Elton Street Filton Village Gorton
(A) (8) (C) (D) (E) (F) (G)
Annual rent ($)
30000 15000 5000 12000 30000 15000 10000
to 6.
as possible, he would also Addison is in a
but it is to rent. lt is staff to work in. In
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DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
tree can be
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CONSTRUCTING A VALUE TREE
Rent Electricity Cleaning Closeness Visibility Image Size Comlort Car to customers parking
3.1 A value tree for the office location DroDlem
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36 DECISIONS INVOlVING MUlTIPlE OBJECTIVES: SMART
whether it is an accurate and 2 sug-
that are concern to
a
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MEASURING HOW WELL THE OPTIONS PERFORM ON EACH ATIRIBUTE
tree went
Table 3.1 Costs associated with the seven offices
Annual Annual
OHica Annual rant ($) costs ($) costs ($) Total cost
30000 3000 2000 35000 Bilton 15000 2000 800 17800 Carlsle Walk 5000 1 000 700 6700 Denver Street 12000 1 000 1 100 14100 Elton 30000 2500 2300 34800 Filton Village 15000 1 000 2600 18600 Gorton 10000 1 100 900 12000
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DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
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MEASURING HOW WELL THE OPTIONS PERFORM ON EACH ATIRIBUTE
3.2 A vaJue scale for office
Value 100 I---L~~~~~
90-----~_E~lwto~nLS~t~re~e~t~
80
70-~--~JF~ilwto~n~V~illilla~e~
60
50
40
30~~--t=fD~env~e~r}S~t~re~eitJ 20 1-~~:2IU::>g:
101-~~~~~~ O L_-r-r.;;;r~W;k1
as an increase
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DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
Table 3.2 ~ Values and
Attribute
Closeness 32 26 23
Size 10 Comfort 6
3
Benefits
A B e
100 20 80 60 80 70
100 10 o 75 30 o o 100 10
90 30 100
80.8 39.4 47.4
can
strength VLU'-~O is shown below:
Addison Bilton Village Carlisle Walk Oenver Street Elton Street Filton Village Gorton
(A) (B) (C) (O) (E) (F)
Office
D
70 50 30 55 30 90
52.3
Floor area
1000 550 400 800
1500 400 700
in area froID 500 to 1000
an increase aUractive. To
E
40 60 90
100 60 70
64.8
on aa-lOO we can r>v,'"""",,,, as follows.
F G
o 60 o 100
70 20 o 50
80 50 o 80
20.9 60.2
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MEASURING HOW WELL THE OPTIONS PERFORM ON EACH ATTRIBUTE 41
The owner judges that, the it is. largest Street, an area t so we can 1500 a value of 100. In
v(1500) = lOO, where v(1500) means Walk and FiIton Village) both
the office areas that faH between rptprt'pt! areas. We could ask the owner to rate the areas
under consideration the methods of the section. beca use areas involving awkward numbers are involved, it be easier to what 1S known as a methods can function, but one of the most widely 1S
requires owner to identify an office area halfway between the preferred are a (400 and the most area (1500 fe). Note area does not necessarily have to to of one of lnitially, owner suggests that the midpoint area would that an 400 to 1000 is just as aUractive as an increase 1000 to 1500 sorne he rejects this increases from smaller areas will, he reasons, reduce so be much more attrac-tive than increases areas, which would only lead to minor
"H,D1"C\,rf other candidates for the midpoint position (for as Finally, he that 700
identified the midpoint the to the 'quarter-points'. first of these will be the area that a value between the least area (400 and the midpoint area (700 fe). He decides that is 500 , so v(500) = Similarly, we ask him to identify an area has a value halfway between the midpoint area (700 fe) the best area (1500 fe). He judges this to be 1000 f which implies that v(1000) = We now have the for five fIoor areas, and this enables us to plot the function for office is shown in 3.3. value function can now be used to estimate
the actual areas of offices consideration. example, the Bilton Village office has an area 550 the curve suggests that the area is about
A applied to the 'closeness to by the variable 'distance fram town , and the
value function is shown in Note that the the distance from the town center, the lower the value will curve also suggests a move fram O to 2 miles fram the town center is more damagng to business than a move fram 6 to 8 values identified for the seven in terms
to are shown in
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3.3
DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
100
75 --- - - ---
(j) :; ~ 50 - -
Oflice floor area
a value function for office floor area
100
75
(j) :; 50 (ti >
25
00 ----L 2 4 6 Distance Irom town center (miles)
8
3.4 - A value function for distance fmm customers
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DETERMINING THE WEIGHTS OF THE ATIRIBUTES
can to
Floor area
X 400 y 402
to the would
Office Fioor area
X O Y 100 Weights 5
problems. see
Distance from customers
O miles 15 miles
as follows:
Distance from customers
100 O 1
100 500
( or swing) from in another
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DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
attributes
'closeness to this change has would next choose to move to and so on,
until all the owner' s are as follows:
(1) to customers
We can now assessed as foUows. location to the most
100. a swing from
most distant location customers some decides that swing in 'visibility' is
80(X) as as the swing in 'doseness to customers', so visibility is a weight of a swing from the worst 'image' to best is considered to
as important as a swing from worst to the best location 'doseness to customers', so s assgned a weight of 70. procedure is for all the other attributes, illustrates the
obtained sum to 310, and it is conventional to to 100 (this will later stages of the analysis to
is by dividing weight the sum of the weights multiplying by 100:
Attribute
Closeness to customers Visibility
Size Comfort
facilities
100 80 70 30 20
310
Normalized w"".n"'I"'" (to nearest whole number)
32 26 23 10 6 3
100
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AGGREGATING THE BENEFITS USING THE ADDITIVE MODEL
100 Bes!
80 Bes!
O Worst
Derivation of is considered to be
to customers
Wors!
Bes!
Worst
For
1"'" Worst 1 Bes! Worst Best Wors! from the worst to the best location for worst to the best location for closeness
in the 'working the appropriate lower-level weights, so
Agg
for workng conditions is 9
model We now have (1) a measure how well on weights enable us to compare the values allocated to one allocated to the The next is to how well
combining the six value scores allocated to do this, we will assume the model is
this simply adding an weighted value scores to measure of its overall benefits. model is the most widely
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46 DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
Attribute Addison values Value x
to customers 100 32 3200 Visibility 60 26 1560
100 23 2300 Size 75 10 750 Comfort O 6 O
facilities 90 3 8080
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TRADING BENEFITS AGAINST COSTS
100
A "-',
J!l "
'$ x E e (!)
xD J::J 50 e
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DECISIONS INVOLVING MULTIPLE OBJECTIVES: SMART
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SENSITIVITY ANALYSIS
100
90
Weight placed on turnover
3.7 - for on turnover
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INVOLVING MULTIPLE OBJECTIVES: SMART