View
222
Download
1
Category
Preview:
DESCRIPTION
by Dan Haight
Citation preview
From Ambulances to
Ward Boundaries
Daniel Haight
U of A Centre for
Excellence in Operations
Darkhorse Analytics
Analytics
The Goal
Analytics
<Combining math, data, and
computers to improve insight and
efficiency>
Math
Data Computers
Finance
IT/MIS
Accounting
Computer
Science
Calgary EMS:
Q: What’s going on?
Response time
89%
91%
89%
86%
83%
80%
82%
84%
86%
88%
90%
92%
2000 2001 2002 2003 2004
% Response
< 8min
Data from 2000-2004 – priority 1 calls.
Response time
89% 91% 89% 86%
83%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2000 2001 2002 2003 2004
% Response
< 8min
Data from 2000-2004 – priority 1 calls.
Priority 1 calls12:00am
Priority 1 calls1:00am
Priority 1 calls2:00am
Priority 1 calls3:00am
Priority 1 calls4:00am
Priority 1 calls5:00am
Priority 1 calls6:00am
Priority 1 calls7:00am
Priority 1 calls8:00am
Priority 1 calls9:00am
Priority 1 calls10:00am
Priority 1 calls11:00am
Priority 1 calls12:00pm
Priority 1 calls1:00pm
Priority 1 calls2:00pm
Priority 1 calls3:00pm
Priority 1 calls4:00pm
Priority 1 calls5:00pm
Priority 1 calls6:00pm
Priority 1 calls7:00pm
Priority 1 calls8:00pm
Priority 1 calls9:00pm
Priority 1 calls10:00pm
Priority 1 calls11:00pm
Ward Criteria
• Geographical
– Contiguity
– Compactness
– Natural boundaries
• Socio-political
– Population equality (± 10%)
– Electoral equality (± 25%)
– Groups of interest (community
leagues, socio-demographics)
– Similarity to existing solution
1 2 3 4 5 6 5 4
2 3 4 5 6 5 4 3
3 4 5 6 7 7 5 3
4 5 5 6 8 7 6 5
5 5 6 7 9 7 6 6
5 6 7 9 12 11 9 8
6 5 4 9 10 9 7 5
2 3 5 7 8 9 3 2
1 2 3 4 5 6 5 4
2 3 4 5 6 5 4 3
3 4 5 6 7 7 5 3
4 5 5 6 8 7 6 5
5 5 6 7 9 7 6 6
5 6 7 9 12 11 9 8
6 5 4 9 10 9 7 5
2 3 5 7 8 9 3 2
1 2 3 4 5 6 5 4
2 3 4 5 6 5 4 3
3 4 5 6 7 7 5 3
4 5 5 6 8 7 6 5
5 5 6 7 9 7 6 6
5 6 7 9 12 11 9 8
6 5 4 9 10 9 7 5
2 3 5 7 8 9 3 2
88, 88, 91, 9390 each 90 each
1 2 3 4 5 6 5 4
2 3 4 5 6 5 4 3
3 4 5 6 7 7 5 3
4 5 5 6 8 7 6 5
5 5 6 7 9 7 6 6
5 6 7 9 12 11 9 8
6 5 4 9 10 9 7 5
2 3 5 7 8 9 3 2
360 Population64 Units 4 Districts
Edmonton Journal – Page A1
April 10, 2009
1
2
34
5
67
8
9
1011
12
“Many months of our election planners’ time were saved due to the computer-based approach without sacrificing any of the criteria relevant to the council”
“I would like to emphasize how an OR implementation such as this has had a profound effect on how we carry out one of our critical tasks at the City of Edmonton”
The Supernet
The Problem
Use as few of the blue lines
as possible to connect all
the red dots…
Why use few?
How do you solve it?
8,426,642m
8,248,888m
Original
Solution
Our Solution
Difference in solutions: 14 km
High River High River
Vulcan
Vulcan
Fort Macleod Fort Macleod
Lethbridge Lethbridge
Total kms:
Potential savings: 178 km or 2.1% (Note: Cost is ~ $12/m)
Original Solution Our Solution
8,426,642m 8,248,888m
Alberta Education:
Q: How many teachers
should we hire?
= /
+=
Age
Staff
Calculate
Staff
Attrition
Initial
Teachers
Initial
PopulationAge
Population
Calculate
Population
Migration
& Births
Compare Staff and Students
Hire New Staff
Calculate
Student
Participation
Initial Population
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
Ag
e
Population
Age Population
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
Ag
e
Population
Migration
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
0
10
20
30
40
50
60
70
80
90
100
0 5 10
15
20
25
30
35
40
45
50
Age
Ag
e
Population
Migration
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
0
10
20
30
40
50
60
70
80
90
100
0 5 10
15
20
25
30
35
40
45
50
Age
Ag
e
Population
Migration
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
0
10
20
30
40
50
60
70
80
90
100
0 5 10
15
20
25
30
35
40
45
50
Age
Ag
e
Population
Migration
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
Ag
e
Population
Migration
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
Ag
e
Population
Migration
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
Ag
e
Population
New 0 yr-olds
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
0%
2%
4%
6%
8%
10%
12%
14%
0 5 10
15
20
25
30
35
40
45
50
X
Ag
e
Population
New 0 yr-olds
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
X0%
2%
4%
6%
8%
10%
12%
14%
0
5
10
15
20
25
30
35
40
45
50
Ag
e
Population
-30000 -20000 -10000 0 10000 20000 30000
0
5
10
15
20
25
30
35
40
45
50
-30000 -20000 -10000 0 10000 20000 30000
0
2
4
6
8
10
12
14
16
18
20
School Aged
School
Aged
Ag
e
Population
Estimate
Participation
0%
20%
40%
60%
80%
100%
0 2 4 6 8 10
12
14
16
18
20
30,000 20,000 10,000 0 10,000 20,000 30,000
0
2
4
6
8
10
12
14
16
18
20
Ag
e
Population
Age
Estimate
Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
0
2
4
6
8
10
12
14
16
18
20
0%
20%
40%
60%
80%
100%
0 2 4 6 8 10
12
14
16
18
20
Ag
e
Population
Age
Apply Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
0
2
4
6
8
10
12
14
16
18
20
0%
20%
40%
60%
80%
100%
0 2 4 6 8 10
12
14
16
18
20
X
Ag
e
Population
30,000 20,000 10,000 0 10,000 20,000 30,000
0
2
4
6
8
10
12
14
16
18
20
X0
%
20
%
40
%
60
%
80
%
10
0%
0
2
4
6
8
10
12
14
16
18
20
Ag
e
Population
Apply Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
0
2
4
6
8
10
12
14
16
18
20
Ag
e
Population
Apply Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
0
2
4
6
8
10
12
14
16
18
20
Student Count
Ag
e
Students
Apply Participation
Age
Staff
Calculate
Staff
Attrition
Initial
Teachers
Initial
PopulationAge
Population
Calculate
Population
Migration
& Births
Compare Staff and Students
Hire New Staff
Calculate
Student
Participation
Teacher Workforce
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
Ag
e
Teachers
Age Workforce
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
Ag
e
Teachers
Apply Attrition
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
0%
10%
20%
30%
40%
50%
60%
21 26 31 36 41 46 51 56 61 66
Based on Age Specific Probabilities
Ag
e
Teachers
Age
Pro
bab
ilit
y o
f A
ttri
tio
n
Apply Attrition
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
0%
10%
20%
30%
40%
50%
60%
21 26 31 36 41 46 51 56 61 66
Based on Age Specific Probabilities
Remaining Staff
Ag
e
Age
Pro
bab
ilit
y o
f A
ttri
tio
n
Calculate Hires
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
Students
Remaining Staff-
30,000 20,000 10,000 0 10,000 20,000 30,000
0
2
4
6
8
10
12
14
16
18
20
/ Student to Staff Ratio
Required Staff=
Required Hires=
Ag
eAg
e
Apply Hires
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
21 26 31 36 41 46 51 56 61 66 71
Required Hires
X
Hire Age/Gender Probability
Age
Ag
e
Pro
bab
ilit
y
Apply Hires
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
21 26 31 36 41 46 51 56 61 66 71
Required Hires
X
Hire Age/Gender Probability
Age
Ag
e
Pro
bab
ilit
y
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
Apply Hires
1,000 500 0 500 1,000
21
26
31
36
41
46
51
56
61
66
71
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
21 26 31 36 41 46 51 56 61 66 71
Required Hires
X
Hire Age/Gender Probability
Age
Pro
bab
ilit
y
Repeat
Lessons Learned
• Process integration is key
• It replaces supports decision-
making
• Interactivity fosters buy-in
• Analytics is hard (IT, Stats,
Communication)
• Talent is rare
45000
47000
49000
51000
53000
55000
57000
59000
Starting Salaries
Accounting
Finance
Marketing
HRM
OM
BusEcLaw
Female
Male
Recommended