How to convince crew planners to use an automatic
rostering tool (ACA)
Crew Management Study Group 2006 Conference
Honolulu, April 9 - 12, 2006
Shortening the crew rostering process makes the network-planning
more flexible and creates additional cash flow
Market changes / booking trend
Old world:
O
P
Roster publication
S
Flight-schedule-optimization
network-planning
crew rostering
Time To Market
New world:
Flight-schedule-optimization
network-planning
O
P
Roster publication
S
crew rostering
Time To Market
3 weeks
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 1
O
P
S
Crew rostering at Lufthansa is each month a challenge to find a
balance between company requirements and individual interests
Legality
Irregularities
• Flight plan changes
(e.g. fleet changes)
Text
• JAR- and LBA-regularities
• MTV, BVB, OM-A
Crewmember
• Requests/Bids
• Early roster information
• Notification ofText
illness
• Personnel restrictions
• Capacitiy changes between
different home bases
• Roster stability
COC/CAB
guidelines
LH-efficiency
• QualificationsText
• Later delivery ofText
flight schedule
• Quality demands
• Economic efficiency
• Additional regulations
• Operational stability
• Special agreements to the
flight schedule
• Producing on time
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 2
• Individual roster stability
To speed up the process and to obey all objectives the crew
rostering process has to change
New world
Objective oriented process
Old World
Manual, rule oriented process
• Manual sequential process
•
•
•
•
•
•
-> Long running time of rostering
Static rules (must rules)
In reality planner reacts more flexible
as documented (scope of interpretation)
-> Hard to implement in software
Employee satisfaction will override profitability
Planner reacts due to a clear decision –
making process (sophisticated crew
assignment system = CAS user)
Production of one solution is the result of a
well-defined chain of decisions
Planner can explain the solution to
crewmember (->excuse)
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 3
• Rostering has high management attention
•
•
•
•
•
•
-> High demands on transparency and
measurability
Net mgmt. forces to minimize „time to market“
Parallel process (Use optimization tool ACA)
-> Short running time of rostering
Hard and soft rules (constraints and objective
function elements)
Controlling claims simulations
-> Production of several solutions
Finding the best solution, i.e. what is a good
roster?
-> Definition/calibration of an objective function
Planner becomes operations research
specialist (sophisiticated CAS + ACA user)
Former manual and new automatic crew rostering process have
the same starting point and a definable end point
 Old world
without optimizer
Create
pre-roster
Manual planning day by day
according to well.defined chain of decisions
Quality
check
ca. 3,5 weeks
Same starting point
 New world
ACA
reference
runs
… Create
pre-roster
Definable end point
with optimizer ACA
ACA
production
runs
< 2 weeks
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 4
Quality
check
Manual and automatic rostering were compared with measuring
time need and quality
Planner 1
• User of standard rostering system
Preroster
Preroster
Finished
roster 1
Manual crew rostering
Same starting point
Compare time need
Compare quality
Planner 2
• User of standard rostering system
• User of optimizer ACA
7 CAB groups
5 COC groups
Preroster
Automatic crew rostering
Zeit
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 5
Finished
roster 2
Acceptance test as 12 real matches
between two planners
Planner 1
Planner 2
• User of CAS
• User of CAS
• User of ACA
CAS
_:_
Measuring quality
with objective function
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 6
ACA
CAS
Measuring the quality with the official acceptance objective
function means only to compare two numbers
day
CRM1
CRM2
CRM3
day
CRM1
CRM2
CRM3
Optimizer ACA
Generator
Solver
Number of
roster
Generates a lot
of solutions
3 cases possible
x<y (new world better)
x=y(old and new is the same)
x>y (old world is better)
Best
roster
Solution with
points = x
Picks out the best solution
(lowest points according to
the objective function)
Compare numbers
Manual solution can also be
evaluated with objective function
Manual plan
Generation of one solution
day
CRM1
CRM2
CRM3
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 7
Solution with
points = y
In all cases the planner with the optimizer was
able to produce better rosters in shorter time
CAS
0 : 12
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 8
ACA
CAS
Overview of CAB results
Result
objective function
CAS
Points (OPPs)
Old world
FB DUS Aug04
ACA
CAS
Points (OPPs)
New world
12.597.327 (18)
293.992 (0)
FB FRA NG IK Aug04
1.275.565 (0)
1.166.205 (0)
FB FRA NB Gem Aug04
1.273.005 (0)
952.758 (0)
41.358.431 (69)
2.603.397 (38)
FB FRA NG IK Sep04
9.071.263 (5)
717.050 (0)
FB FRA NG Gem Sep04
1.398.269 (0)
1.068.476 (0)
1.461.927 (12)
533.739 (1)
FB DUS Sep04
FB DUS Okt04
Time need:
2-7 days
OPPs = Number of Open Positions
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 9
Time need:
2-4 hours
Detailed comparison of objective function result manual and
automatic roster for a flight attendant planning group Aug04
Size:
835 crewmembers
FB FRA NB Gem Aug04
Sum points
Additional flying hours
CAS
CAS
ACA
Manual roster
ACA roster
Points
Points
1.273.005
952.758
KPI2
480.971
475.972
LSW hours lower limit
KPI4
424.832
278.524
LSW hours higher limit
KPI5
80.000
0
BZW hours lower limit
KPI6
197.431
149.471
BZW hours higher limit
KPI7
4.770
0
Destination diversity
KPI14
101
0
Consecutive days-off
KPI15
39.540
35.630
Days-off above claim
KPI18
45.360
13.160
Aircraft diversity
KPI21
0
0
Open position points
0
0
Overlapping open positions
0
0
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 10
Comparison of days-off corridor between manual and automatic
roster for a flight attendant planning group Aug04
Automatic
Number of
crewmember
Manual
Number of
days-off above claim
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 11
Comparison of flying hours corridor between manual and
automatic roster for a flight attendant planning group Aug04
Automatic roster:
Sharper and higher peak at lower
flying hour level
„fair distribution of workload“
Automatic
Number of
crewmember
Manual
Number of
flying hours
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 12
Comparison of flying hours corridor between manual and
automatic roster for a flight attendant planning group Sep04
Automatic
Number of
crewmember
Manual
Number of
flying hours
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 13
Due to measurable results we (IT department) were able to
convince the planning department
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 14
Overview of ACA usage in March 2006 für planning month April
2006
Overview ACA use
60
COC
CAB
KONT
Week-end
Week-end
30
Week-end
Week-end
Number of ACA runs
40
20
Roster publication
Bidding phase
50
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Day of month (March 2006)
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
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Any questions?
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 16
All elements of an objective function have to be calibrated
against each other
The objective function consists of
 Roster points (quality of a single roster)
– Number of additional flying hours and number of days-off above claim
– Deviation from target corridor (flying hours, duty days)
– Destination / aircraft diversity
– Number of consecutive days-off
 Open position points
– Number of duty days which couldn’t be assigned
n
POINTS     ROSTERPOIN TSi    OPEN _ POSITION _ POINTS
i 1
ROSTERPOIN TSi    ADD _ FLYING _ HOURS i    FREE _ DAYS _ ABOVE _ CLAIM i  
Adobe Acrobat 7.0
Document
Adobe Acrobat 7.0
Document
Example ACA roster
Example manual roster
AGIFORS Crew Management Study Group 2006 Conference
April 9 - 12, 2006, Honolulu
Page 17