Facility Location
COB 300C – Fall 2002
Facility Location
 Facility Location is the placement of facility with respect to
customers, suppliers and other interacting facilities. It should
consider:
–
–
–
–
Operating costs
Customer convenience
Transportation costs
Access to key related services
such as banking and
educational opportunities
– Strategic factors
Location as a Strategic Decision
1. Long-term commitment
2. Linked to customer base
3. Regional facility supplies specific area
4. Product facility supplies globally
5. Combination of regional and product facilities
Regional or Global
Factors Affecting the
Location Decision
 Strategic nature of
decision
 Quantitative factors
 Government incentives
 Qualitative factors
Including the
Qualitative Factors
 Integrate qualitative
factors
– Determine which
factors are relevant to
the problem
– Weigh each factor
– Rate each site for each
factor
Examples of Indianapolis and
Lexington (Slide 1 of 2)
Recreational activities
University research
facilities
Union activities
Banking services
Available labor pool
Weight
Indianapolis
Raw Score
Lexington
Raw Score
20
8
7
40
40
80
60
8
4
7
7
8
7
6
5
Examples of Indianapolis and
Lexington (Slide 2 of 2)
Indianapolis
Weighted score
Recreational activities
University research
facilities
Union activities
Banking services
Available labor pool
Total
Lexington
Weighted score
160
140
320
160
560
420
320
280
480
300
1,620
1,520
Analyzing Spatial Relationships


Load-Distance Method
measures proximity to
customers, suppliers,
interacting facilities
Transportation Problem
relates to the cost of
transporting materials to
and from multiple
facilities
Distance from Facility
to Customer
Health Care Unit
Location Problem
Locating a Health Care Center Using the “Load-Distance
Method” (Slide 1 of 3)
Population coordinates
Zip
Code
ai
10111
10112
10113
10114
10115
10116
10117
30,000
25,000
11,000
8,000
18,000
24,000
12,000
Total
128,000
xi
yi
3
2
1.5
3
3.5
4.5
5.25
2
4
5.5
7
5
3.5
6.25
(ai )(xi)
(ai)(yi)
90,000
50,000
16,500
24,000
63,000
108,000
63,000
60,000
100,000
60,500
56,000
90,000
84,000
75,000
414,500
525,500
Locating a Health Care Center Using the “Load-Distance
Method” (Slide 2 of 3)
n
n
xf 
 a x 
i
i 1
i
n
 (a )
i 1
i
yf 
 a  y 
i
i 1
i
n
 (a )
i 1
i
where
xf = Distance along the x axis from the origin to the center of gravity
yf = Distance along the y axis from the origin to the center of gravity
ai = The activity level (load) from the i th location to the proposed facility
Xi = the coordinate on the x axis for the i th customer location
yi = the coordinate on the y axis for the i th customer location
Locating a Health Care Center Using the “Load-Distance
Method” (Slide 3 of 3)
The coordinates of the center of gravity are:
xf =
414,500
128,000
= 3.24
yf =
525,500
128,000
= 4.11
Transportation Problem
 Cost of moving materials
between multiple
destinations
 Vogel’s Approximation
Method
 To evaluate two locations,
solve the transportation
problem for each location
OR
Transportation Example
New facility capacity = 5000 units/month
We must choose either Des Moines, Iowa or
Montgomery, Alabama
Transportation costs per unit for Des
Moines and Montgomery to each customer
location are provided
We are interested in total transportation
cost for Des Moines versus Montgomery
Transportation Example (cont’d)
Supply
Lexington - 12,420
Milan - 9,380
DesMoines - 5,000 (proposed)
or
Montgomery - 5,000 (proposed)
Transportation Example (cont’d)
Demand
Baton Rouge - 6,740
Bismarck - 8,400
Tampa - 5,050
Youngstown - 5,670
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
12420
17
15
17
9
0
9380
17
11
19
14
0
5000
Lexington
Milan
DesMoines
6740
DEMAND
8400
5050
5670
940
26800
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
12420
12
17
15
17
9
0
9380
9
17
11
19
14
0
5000
11
Lexington
Milan
DesMoines
6740
8400
5050
5670
940
26800
DEMAND
3
4
1
3
0
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
12420
12
940
Lexington
17
15
17
9
0
9380
9
17
11
19
14
0
5000
11
Milan
DesMoines
6740
8400
5050
5670
940
26800
DEMAND
3
4
1
3
0
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
17
15
17
9
0
12420
11480
9380
17
11
19
14
0
5000
940
Lexington
2
3
6
Milan
DesMoines
6740
8400
5050
5670
940
0
DEMAND
3
4
1
3
26800
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
9
0
12420
11480
9380
14
0
5000
940
Lexington
17
15
17
2
3
6
5670
Milan
17
11
19
DesMoines
6740
8400
5050
5670
940
0
DEMAND
3
4
1
3
26800
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
940
Lexington
17
15
17
9
0
14
0
5670
Milan
17
11
19
12420
11480
9380
3710
5000
DesMoines
6740
8400
5050
5670
0
DEMAND
3
4
1
940
0
26800
2
2
6
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
940
Lexington
17
15
17
9
0
14
0
5670
Milan
17
11
5000
8400
DesMoines
6740
19
5050
5670
0
DEMAND
3
4
1
940
0
12420
11480
9380
3710
5000
26800
2
2
6
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
18
16
12
0
940
Lexington
17
15
17
9
0
14
0
5670
Milan
17
11
5000
8400
3400
DesMoines
6740
DEMAND
3
19
5050
3
5670
0
1
940
0
12420
11480
9380
3710
5000
0
26800
2
2
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
16
12
6740
0
940
17
15
17
9
0
14
0
5670
Milan
17
11
5000
8400
3400
DesMoines
6740
DEMAND
3
19
5050
3
5670
0
1
940
0
12420
11480
9380
3710
5000
0
26800
2
2
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
16
6740
0
940
17
15
17
9
0
14
0
5670
Milan
17
DesMoines
DEMAND
12
6740
0
11
5000
8400
3400
19
5050
3
5670
0
1
940
0
12420
4740
9380
3710
5000
0
26800
2
2
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
12
6740
15
17
3400
Milan
17
DesMoines
6740
0
0
940
17
DEMAND
16
9
0
14
0
5670
11
5000
8400
3400
19
5050
3
5670
0
1
940
0
12420
4740
9380
3710
5000
0
26800
2
2
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
12
6740
15
17
3400
Milan
17
DesMoines
6740
0
0
940
17
DEMAND
16
0
14
0
5670
11
5000
8400
0
9
19
5050
5670
0
1
940
0
12420
4740
9380
310
5000
0
26800
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
6740
15
17
17
6740
0
9
0
14
0
5670
11
5000
8400
0
0
940
3400
Milan
DesMoines
12
4740
17
DEMAND
16
19
5050
5670
0
1
940
0
12420
4740
9380
310
5000
0
26800
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
6740
15
17
17
6740
0
9
0
14
0
5670
11
5000
8400
0
0
940
3400
Milan
DesMoines
12
4740
17
DEMAND
16
19
5050
310
5670
0
940
0
12420
0
9380
310
5000
0
26800
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
6740
15
3400
Milan
17
DEMAND
6740
0
12
4740
17
DesMoines
16
5000
8400
0
940
17
310
11
9
0
14
0
5670
19
5050
310
0
5670
0
940
0
12420
0
9380
310
5000
0
26800
DESTINATION
Baton Rouge
Bismarck
Tampa
Youngstown
Dummy
CAPACITY
SOURCE
14
Lexington
18
6740
15
3400
Milan
17
DEMAND
6740
0
12
4740
17
DesMoines
16
5000
8400
0
940
17
310
11
9
0
14
0
5670
19
5050
0
0
5670
0
940
0
12420
0
9380
0
5000
0
26800
Total Transportation Cost for Des
Moines
6740($14)+4740($16)+940($0)+3400($15)+
310($17)+5670($9)+5000($11) = $332,500
For Next Class Figure Transportation
Cost for Montgomery
Which is best choice based on Vogel’s
Approximation?
Are there other factors to consider?
Montgomery’s Transportation Costs:
–
–
–
–
Montgomery to Baton Rouge: $9 per unit
Montgomery to Bismarck: $19 per unit
Montgomery to Tampa: $12 per unit
Montgomery to Youngstown $15 per unit
Location Example - Load
Distance
Location of a warehouse in Germany
Method: Load-Distance Method (a.k.a.:
center-of-gravity method)
Customer locations (coordinates) and
demands in units per year are given
CITY
Hamburg
Cologne
Stuttgart
Munich
Dresden
Berlin
Demand ai (units/yr.)
42,000
22,000
37,000
66,000
45,000
113,000
Xi
Yi
3.25
1
2.5
4.25
5.75
5.5
7
4.5
2
1.25
4.5
6
CITY
Hamburg
Cologne
Stuttgart
Munich
Dresden
Berlin
Demand ai (units/yr.)
42,000
22,000
37,000
66,000
45,000
113,000
Xi
Yi
(ai)(xi)
(ai)(yi)
3.25
1
2.5
4.25
5.75
5.5
7
4.5
2
1.25
4.5
6
136,500
22,000
92,500
280,500
258,750
621,500
294,000
99,000
74,000
82,500
202,500
678,000
CITY
Demand ai (units/yr.)
Hamburg
Cologne
Stuttgart
Munich
Dresden
Berlin
42,000
22,000
37,000
66,000
45,000
113,000
TOTAL
325,000
Xi
Yi
(ai)(xi)
(ai)(yi)
3.25
1
2.5
4.25
5.75
5.5
7
4.5
2
1.25
4.5
6
136,500
22,000
92,500
280,500
258,750
621,500
294,000
99,000
74,000
82,500
202,500
678,000
1,411,750
1,430,000
n
 (a )(x )
i
xf =
i
i=1
n
a
1,411,750

 4.34
325,000
i
i=1
n
 (a )(y )
i
yf =
i
i=1
n
a
i
i=1
1,430,000

 4.40
325,000
Show map here
Location?
Central Germany
Other Factors to Consider?
operating costs
required investment
government incentives
qualitative factors
overall strategy of organization
Location Decision Affects Other
Operating Decisions
 Alternative to on-site
expansion
 On-site expansion is
problematic
– Material handling and
storage
– Complex production flow
– Strained communication
– New technology delayed
– Use of old equipment
– Layering of expanded
responsibilities
International Dimensions of
Location Decision
 Reasons for locating in
foreign countries
– Comparative
Advantage
– Closeness to market
– Political relationships
– Availability of
resources
Location Analysis for
Service Operations

Concepts and techniques
discussed so far apply to
service operations

Service issues:
» Minimize response time:
Emergency medical
services
» Provide minimum
coverage: Fire Protection
» Mobile location: Police or
security units