From Ambulances to Ward Boundaries
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From Ambulances to Ward Boundaries Daniel Haight U of A Centre for Excellence in Operations Darkhorse Analytics
description
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 . Finance. Computer Science. Computers. Data. - PowerPoint PPT Presentation
Transcript of From Ambulances to Ward Boundaries
From Ambulances to Ward Boundaries
Daniel HaightU 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%
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30%
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50%
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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
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88, 88, 91, 9390 each 90 each
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360 Population64 Units 4 Districts
Edmonton Journal – 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 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
VulcanVulcan
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
CalculateStaff Attrition
Initial Teachers
Initial Population
Age Population
CalculatePopulation Migration & Births
Compare Staff and Students
Hire New Staff
CalculateStudent Participation
Initial Population
-30000 -20000 -10000 0 10000 20000 300000
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Population
Age Population
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Migration
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Migration
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Migration
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New 0 yr-olds
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School Aged
Age
Population
Estimate Participation
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Age
Estimate Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
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Age
Population
Age
Apply Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
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Population
30,000 20,000 10,000 0 10,000 20,000 30,000
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Population
Apply Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
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Population
Apply Participation
30,000 20,000 10,000 0 10,000 20,000 30,000
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Student Count
Age
Students
Apply Participation
Age Staff
CalculateStaff Attrition
Initial Teachers
Initial Population
Age Population
CalculatePopulation Migration & Births
Compare Staff and Students
Hire New Staff
CalculateStudent Participation
Teacher Workforce
1,000 500 0 500 1,00021
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31
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61
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Age
Teachers
Age Workforce
1,000 500 0 500 1,00021
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Age
Teachers
Apply Attrition
1,000 500 0 500 1,00021
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61
66
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0%
10%
20%
30%
40%
50%
60%
21 26 31 36 41 46 51 56 61 66
Based on Age Specific Probabilities
Age
Teachers
Age
Prob
abili
ty o
f Attr
ition
Apply Attrition
1,000 500 0 500 1,00021
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
Age
Age
Prob
abili
ty o
f Attr
ition
Calculate Hires
1,000 500 0 500 1,00021
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
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/ Student to Staff Ratio
Required Staff=Required Hires=
AgeA
ge
Apply Hires
1,000 500 0 500 1,00021
26
31
36
41
46
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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
Age
Prob
abili
ty
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
Age
Prob
abili
ty
1,000 500 0 500 1,00021
26
31
36
41
46
51
56
61
66
71
Apply Hires
1,000 500 0 500 1,00021
26
31
36
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46
51
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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
Prob
abili
ty
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
Accounting
Finance
Marketing
HRM OM
BusEcLaw
Female
Male
4500047000490005100053000550005700059000
Starting Salaries
Salaries
