The optimisation of rosters to
meet public demand
Presentation to the Criminal Justice SIG
18 November 2015
David Wrigley
[email protected]
ORvis consulting
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Structure
•
•
•
•
•
Scope of work
Summary Results
Benefits
Lessons learned
Questions
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The Client
• Public sector organisation operating 24 / 7
• Multiple sites
• Workload is driven by public activity outside
of client control, varying moment by moment
• Staff come from several groups, each with
own skills base and contractual terms
• Moving towards unification of contracts and
cross-training
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Study requirement
• Recommending approaches/methods for
optimising the rosters …
• … taking account of staff wellbeing, operational
efficiency, flexibility/robustness to disruption,
customer service
• Putting forward options for software tools to aid
rostering which may include automating part, or
all of the rostering process
• Demonstrating compliance with working rules
and regulations ….. including types of contract
and availability for duties
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Study tasks
• Scoping study
– May 2014 – October 2014
• Software trial
– December 2014 – September 2015
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Scoping study
• Three major sites selected as case studies
• Interviews with staff, managers, rostering
teams, unions
• Developed measures of merit of a roster
• Brief review of off-the-shelf software
• Review of roster optimisation methods
• Workshop with senior managers and rostering
staff
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Summary findings from Scoping
Study
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•
•
•
•
•
•
•
Some staff are exhausted by current rosters
Proposed set of Measures of Merit of a roster
Baseline performance
Meta heuristics is the most appropriate optimisation
technique
Software is available off-the-shelf
Use MoM as benefit function for optimisation
Demonstration of example software tool
Outline for new rostering approach
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Software Trial
• One site
• Using existing time management software
• Catalogue of rostering rules, principles and
constraints
• Data collection and cleansing
• Exploration of software and dry runs
• Trials against live rostering using new process
• Use MoM derived in Scoping Study phase to
compare rosters
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Working with a Black Box
• Cannot be certain what it is actually doing
• Technical contact with supplier …
• Dry runs ….mislead myself about its
capabilities and workings
• A lot of time spent trying to understand it and
control it
• “It does what it says on the tin”
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Rostering targets
Number of staff allocated
30
25
20
Time varying staff requirement
15
Minimum staff target
10
Staff allocated
5
0
0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22
Time of day
Manual rostering against minimum staffing target per shift
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What we were trying to achieve
Effectiveness: reduce under-coverage
Efficiency: reduce over-coverage
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Metrics
•
•
•
•
Efficiency – over coverage
Effectiveness – under coverage
Work life balance
Compliance with HSE Guidelines for rosters
including night shifts
• Plus an overall Figure of Merit combining
these
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Target staffing
Allocated staff
Figure of Merit
Effectiveness
Efficiency
Work life balance
HSE Guidelines
8870.4
8137
92%
73
11%
3%
0.69
0.55
Aut o
m
exis ated ros
i ting
teri n
gstan
dard
shift
s
Auto
mat e
d ros
leng
te
t hs,
start r ing - fix
30 m
e
t im e
ins
s var d shift
i ed b
y +Auto
mat e
d
shif t
leng roster ing
ths
-v
star t
time 6-10 ho ariable
s
urs,
flexib
le
Rosters assessed
against minimum
staffing requirement
per shift
Man
ua
Exis l roster in
t ing
st a n g
dar d
sh
if ts
Rostering against minimum staffing
target per shift
8870.4
7988
90%
81
11%
1%
0.87
0.61
8870.4
8566
97%
87
4%
1%
0.81
0.59
8870.4
8776
99%
90
2%
1%
0.80
0.59
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Target staffing
Allocated staff
Figure of Merit
Effectiveness
Efficiency
Work life balance
HSE Guidelines
7327
8137
111%
51
13%
24%
0.69
0.55
Aut o
m
exis ated ros
i ting
teri n
gstan
dard
shift
s
Auto
mat e
d ros
leng
te
t hs,
start r ing - fix
30 m
e
t im e
ins
s var d shift
i ed b
y +Auto
mat e
d
shif t
leng roster ing
ths
-v
flexib
le st 6-8.75 h ariable
art ti
mes ours,
Auto
mat e
d
shif t
leng roster ing
ths
-v
star t
time 6-10 ho ariable
s
urs,
flexib
le
Rosters assessed
against time varying
staffing requirement
Man
ua
Exis l roster in
t ing
st an g
dar d
sh
if ts
Against time varying staffing requirement
7327
5489
75%
54
32%
6%
1.00
0.63
7327
6553
89%
70
18%
7%
0.98
0.63
7327
7024
96%
73
12%
8%
0.89
0.61
7327
7086
97%
76
11%
8%
0.89
0.61
Manual rostering against minimum staffing target per shift
Automated rostering against time varying staff requirement
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Automated shift design
Shifts generated automatically given wide degree of
flexibility in shift length and start time
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Target staffing
Allocated staff
Figure of Merit
Effectiveness
Efficiency
Work life balance
HSE Guidelines
7327
5489
75%
54
32%
6%
1.00
0.63
7327
6553
89%
70
18%
7%
0.98
0.63
7327
6737
92%
70
16%
8%
0.96
0.62
7327
7086
97%
76
11%
8%
0.89
0.61
sted
stan
dard
shift
s
Adju
A-S
ros
leng t er br oa
t hs 6
d
- 10h shif t t im
es,
rs
Rosters assessed
against time varying
staffing requirement
Auto
ma
exisi t ed sch
e
ting
st an duling dard
shif ts
Auto
mat e
d
shif t
leng scheduli
n
ths,
by +
start g - f ixed
- 30
t ime
mins
s va
ried
A-S
ros
fixed t er br oa
d sh
l eng
if t t im
t hs
es,
Adjusted standard shifts
7327
6379
87%
68
20%
7%
0.95
0.63
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Potential benefits
• Improved efficiency of staff allocation
• Reduced staff effort to construct rosters
• Improved staff well being through improved
work life balance
• Faster response to changing circumstances
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Rostering is difficult
• Once you build in a complex set of contractual
rules, the working time directive, legislation,
trade union agreements and various formal
and informal arrangement ….
• …it is often difficult to find a staff member to
fill a shift
• Automation is the best way to tackle this …
• … but there is no guarantee of a perfect
solution
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Automation is possible and
beneficial
• They said it couldn’t be done
• We tried it and it worked
• And we got better results against the selected
metrics on all counts
• This was the simple problem: one duty at one
location
• Confident that it will work even better for
multiple duties at multiple locations
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Questions?
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