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>
Finance
Computer Science
Math
Data
Computers
Accounting IT/MIS
Calgary EMS: Q: Whatâ&#x20AC;&#x2122;s going on?
Response time % Response < 8min
92% 90%
91% 89%
89%
88% 86%
86% 84% 83%
82% 80% 2000
Data from 2000-2004 â&#x20AC;&#x201C; priority 1 calls.
2001
2002
2003
2004
Response time % Response < 8min
100%
89%
91%
90%
89%
86%
80%
83%
70% 60% 50% 40% 30% 20%
10% 0%
2000
Data from 2000-2004 â&#x20AC;&#x201C; priority 1 calls.
2001
2002
2003
2004
Priority 1 calls 12:00am
Priority 1 calls 1:00am
Priority 1 calls 2:00am
Priority 1 calls 3:00am
Priority 1 calls 4:00am
Priority 1 calls 5:00am
Priority 1 calls 6:00am
Priority 1 calls 7:00am
Priority 1 calls 8:00am
Priority 1 calls 9:00am
Priority 1 calls 10:00am
Priority 1 calls 11:00am
Priority 1 calls 12:00pm
Priority 1 calls 1:00pm
Priority 1 calls 2:00pm
Priority 1 calls 3:00pm
Priority 1 calls 4:00pm
Priority 1 calls 5:00pm
Priority 1 calls 6:00pm
Priority 1 calls 7:00pm
Priority 1 calls 8:00pm
Priority 1 calls 9:00pm
Priority 1 calls 10:00pm
Priority 1 calls 11:00pm
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
64 Units
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12 11
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3
2
4 Districts
360 Population
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2
90 each
90 each
88, 88, 91, 93
Edmonton Journal â&#x20AC;&#x201C; Page A1 April 10, 2009
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“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 dotsâ&#x20AC;Ś
Why use few?
How do you solve it?
8,426,642m
8,248,888m
Original Solution
Our Solution
High River
High River
Vulcan
Vulcan
Fort Macleod
Fort Macleod Lethbridge
Difference in solutions: 14 km
Lethbridge
Original Solution
Total kms:
8,426,642m
Our Solution
8,248,888m
Potential savings: 178 km or 2.1% (Note: Cost is ~ $12/m)
Alberta Education: Q: How many teachers should we hire?
=
/
=
+
Initial Teachers
Age Staff
Calculate Staff Attrition
Compare Staff and Students
Initial Population
Age Population
Calculate Population Migration & Births
Hire New Staff
Calculate Student Participation
Initial Population 50 45 40 35
Age
30 25 20 15 10 5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
Age Population 50 45 40 35
Age
30 25 20 15 10 5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
Migration 50 45
100 90
40
80
35
70 60
40
Age
30
50
25
30
20
20 10
15
0
50
45
40
35
30
25
20
15
10
5
0
10
Age
5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
Migration 50 45
100 90
40
80
35
70 60
40
Age
30
50
25
30
20
20 10
15
0
50
45
40
35
30
25
20
15
10
5
0
10
Age
5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
Migration 50 45
100 90
40
80
35
70 60
40
Age
30
50
25
30
20
20 10
15
0
50
45
40
35
30
25
20
15
10
5
0
10
Age
5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
Migration 50 45 40 35
Age
30 25 20 15 10 5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
Migration 50 45 40 35
Age
30 25 20 15 10 5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
Migration 50 45 40 35
Age
30 25 20 15 10 5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
New 0 yr-olds 50 45 14%
40
12%
35
X
8%
30
Age
10%
25
6%
20
4%
15
2%
10
0% 50
45
40
35
30
25
20
15
10
5
0
5 0 -30000
-20000
-10000
0
Population
10000
20000
30000
New 0 yr-olds 50
50
45
45
40
40
35
35
0%
2%
4%
6%
8%
10%
12%
14%
25
X
30
Age
30
25
20
20
15
15
10
10
5
5
0
0 -30000
-20000
-10000
0
Population
10000
20000
30000
School Aged 50 45 40 35
Age
30 25 20
20 18 16
15
14
School Aged
12
10
10 8 6
5
4 2
0
0
-30000
-20000
-10000
0
10000
20000
30000
-30000
-20000
-10000
0
10000
20000
30000
Population
Estimate Participation 20 18 16
100%
14
80% 60%
Age
12 10
40%
8
20%
6 4
0%
20
18
16
14
12
10
8
6
4
2
0
2
Age
0 30,000
20,000
10,000
0
Population
10,000
20,000
30,000
Estimate Participation 20 18 16
100%
14
80% 60%
Age
12 10
40%
8
20%
6 4
0%
20
18
16
14
12
10
8
6
4
2
0
2
Age
0 30,000
20,000
10,000
0
Population
10,000
20,000
30,000
Apply Participation 20 18 16
100%
14
X
60%
12
Age
80%
10
40%
8
20%
6 4
0%
20
18
16
14
12
10
8
6
4
2
0
2 0 30,000
20,000
10,000
0
Population
10,000
20,000
30,000
Apply Participation 20
20
18
18
16
16
14
14
0%
20%
40%
60%
80%
100%
10
X
12
Age
12
10
8
8
6
6
4
4
2
2
0
0 30,000
20,000
10,000
0
Population
10,000
20,000
30,000
Apply Participation 20 18 16 14
Age
12 10 8 6 4 2 0 30,000
20,000
10,000
0
Population
10,000
20,000
30,000
Apply Participation 20 18 16 14
Student Count
Age
12 10 8 6 4 2 0 30,000
20,000
10,000
0
Students
10,000
20,000
30,000
Initial Teachers
Age Staff
Calculate Staff Attrition
Compare Staff and Students
Initial Population
Age Population
Calculate Population Migration & Births
Hire New Staff
Calculate Student Participation
Teacher Workforce 71 66 61 56
Age
51 46 41 36 31 26 21 1,000
500
0 Teachers
500
1,000
Age Workforce 71 66 61 56
Age
51 46 41 36 31 26 21 1,000
500
0 Teachers
500
1,000
Apply Attrition Based on Age Specific Probabilities 71 66 61 56 50%
51 40%
Age
Probability of Attrition
60%
46
30%
41
20%
36
10%
31 26
0% 21
26
31
36
41
Age
46
51
56
61
66
21 1,000
500
0 Teachers
500
1,000
Apply Attrition Based on Age Specific Probabilities 71 66 61 56 50%
51 40%
Age
Probability of Attrition
60%
46
30%
41
20%
36
10%
31 26
0% 21
26
31
36
41
Age
46
51
56
61
66
21 1,000
500
0
500
1,000
Remaining Staff
Calculate Hires
30,000
20,000
10,000
20
71
18
66
16
61
14
56
12
51
10
46
8
41
6
36
4
31
2
26
0
21 0
10,000
= Required Staff
20,000
30,000
1,000
500
Age
Age
Students / Student to Staff Ratio
0
500
1,000
- Remaining Staff = Required Hires
Apply Hires 71
X
66
Hire Age/Gender Probability
61
5.0%
56
4.5%
51
Age
Probability
4.0% 3.5%
46
3.0% 2.5%
41
2.0% 1.5%
36
1.0%
31
0.5%
26
0.0% 21
26
31
36
41
46
Age
51
56
61
66
71
21 1,000
500
0
500
Required Hires
1,000
Apply Hires Required Hires 71
X
66
Hire Age/Gender Probability
61
5.0%
56
4.5%
51
Age
Probability
4.0% 3.5%
46
3.0% 2.5%
41
2.0% 1.5%
36
1.0%
31
0.5%
26
0.0% 21
26
31
36
41
46
Age
51
56
61
66
71
21 1,000
500
0
500
1,000
Apply Hires Required Hires 71
X
66
Hire Age/Gender Probability
61
5.0%
56
4.5%
Probability
4.0%
51
3.5%
46
3.0% 2.5%
41
2.0% 1.5%
36
1.0%
31
0.5%
26
0.0% 21
26
31
36
41
46
Age
51
56
61
66
71
21 1,000
500
0
500
1,000
Repeat
Lessons Learned • Process integration is key • It replaces supports decisionmaking • Interactivity fosters buy-in • Analytics is hard (IT, Stats, Communication) • Talent is rare
Starting Salaries 59000 57000 55000 53000 51000
49000 47000 45000
Accounting Finance Marketing HRM OM BusEcLaw Female Male