Transcript Regels en Conflictsignalering

Reinventing Crew Scheduling
At Netherlands Railways
Erwin Abbink,
NS Reizigers bv, The Netherlands (NL)
Matteo Fischetti,
University of Padua, Italy
Double Click sas, Italy
Leo Kroon,
Erasmus University Rotterdam, NL
NS Reizigers bv, NL
Gerrit Timmer,
Free University of Amsterdam, NL
ORTEC International bv, NL
Michiel Vromans,
Erasmus University Rotterdam, NL
Contents
1. Introduction
2. History
3. Development of an alternative model
4. Sharing Sweet&Sour
5. TURNI and solution methods
6. Efficiency improvements
7. Conclusions
NS Reizigers (Netherlands Railways)
• Main Dutch operator of passenger trains
• 5,000 timetabled trains per weekday
• 1 million passenger trips per weekday
• 112 million train kilometers per year
• 3,000+ drivers and 3,500+ conductors
• 29 crew depots
• 2,600+ carriages
NS Reizigers (Netherlands Railways)
• 29 Depots
• Duties are created in Utrecht (Ut)
• Rosters are created
locally in the depots
Ut
• Focus: Duties
0
50km
Crew management
Robustness
Punctuality
Efficiency
Find:
- Balance
- Trade-off
Acceptance
Duty examples
Maximum
duty length
Minimum
transfer
time
Pre- and
post times
Meal
break rule
- Route knowledge
- Rolling stock knowledge
Rostering Rules
Maximum
average duty
length
Maximum
percentage long
duties (>9 hrs)
Maximum
percentage
night duties
History
• June 10th 2001: introduction of the “Church Circles”
• Aim of the management (TOP DOWN):
• Improve robustness / punctuality and service
• How:
• Less different trains / routes per duty
• Train change only during meal break
• Better knowledge of local situation
History
• Drivers and conductors were quite unhappy
• Decrease in quality / variation of their work
• Unfair division of aggression work over depots
• “Secret agenda of management”
• Punctuality down
• Motivation down / Sickness up
Involved parties
STRIKES
Depot A
Union A
STRIKES
Union B
Works
Council
Management
Depot B
Involved parties
Depot A
Union A
Works
Council
Union B
Management
Depot B
Development of alternative model
Depot A
Union A
Works
Council
Union B
Management
Depot B
Participative approach (BOTTOM UP)
Alternative 1
2-day conferences
‘Sharing Sweet & Sour’
.
.
.
.
Alternative n
Hundreds of optimization runs with TURNI
Selection by
Works Council
Acceptance by
Management
Sharing Sweet&Sour
Additional variation rules:
• Max Repetitions In Duties (RID)
• Max percentage of aggression work per depot
• Max standard deviation on aggression
• Min percentage of preferred trains per depot
• Max standard deviation on preferred trains
• Min number of routes per depot
• Min average number of routes
• Max percentage of Rolling Stock cluster per depot
Sharing Sweet&Sour
Gn
Sharing Sweet&Sour
Lw
Emn
Hdr
Hnk
Hn
Amr
Ekz
Kpn
Zl
Lls
Bh
Hlm
Asd
Hgl
Dv
Zvt
Wp
Ndb
Brn
Es
Hfdo
Apd
Ledn
Amf
Amfs
Apn
Gvc
Ed
Ut
ZL
Gv
Zp
Gd
Ah
Rhn
Gdg
Rth
Gdm
Hlds
Nm
Rtd
Ddr
Rsd
Ht
Ehv
Vl
Br
Vs
Rm
Sharing Sweet&Sour: variation statistics
---------------------------------------------------------Crew
|
| RID |% Pref.|%Aggres| RS |
Nasty RS
Depot |Routes | Avg | Train | Train |Clust|
(1)
(2)
---------------------------------------------------------Ah
|
19 | 2.4 | 63.9 |
9.6 |
7 |
0.8
15.2
Amf
|
22 | 2.4 | 46.0 | 29.5 |
6 | 29.7
25.3
Amr
|
22 | 2.5 | 67.9 | 42.6 |
6 | 26.6
3.9
Asd
|
40 | 2.5 | 58.0 | 29.9 |
6 | 36.4
8.9
Ddr
|
13 | 2.6 | 35.0 | 24.9 |
4 | 15.9
46.6
Ehv
|
17 | 2.6 | 38.5 |
4.1 |
6 |
0.3
58.7
Ekz
|
23 | 2.9 | 28.9 | 27.4 |
6 | 32.0
8.8
Es
|
16 | 2.7 | 47.9 |
6.7 |
6 |
0.0
6.1
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Llso
|
21 | 2.3 | 44.0 | 44.4 |
5 | 44.4
15.6
Mt
|
11 | 2.7 | 42.3 |
2.0 |
3 |
0.0
54.6
Nm
|
18 | 2.3 | 44.0 |
7.6 |
7 |
0.4
35.6
Rtd
|
32 | 2.3 | 32.2 | 20.5 |
6 | 14.6
32.2
Ut
|
44 | 2.5 | 27.1 | 14.6 |
6 |
8.6
51.8
Vl
|
14 | 2.8 | 40.3 |
2.6 |
6 |
0.0
38.4
Vs
|
17 | 2.5 | 64.1 | 15.3 |
4 |
8.3
26.4
Zl
|
26 | 2.3 | 54.4 |
3.0 |
7 | 13.0
5.8
---------------------------------------------------------Global | 21.2 | 2.4 | 12.5 | 12.8 |
TURNI: crew scheduling system by Double Click sas
• TURNI based on a set covering model
with many additional nasty “depot” constraints
• Typical instance of NS Reizigers (drivers):
- 14,000 timetabled trips
- 1,000 duties from 29 crew depots
• Extensive customizations for NS Reizigers
(Extended) Set Covering model
D
min  k d xd
subject to
d 1
D
 xd  1
 t  1,..., T
dDt
D
 bc,d xd  uc
 c  1,..., C
xd {0,1}
 d  1,..., D
d 1
 1 if potential duty d is selected,
xd  
 0 otherwise.
Solution techniques
• Dynamic column generation
• Column generation based on dynamic programming
• Lagrangian optimization instead of LP
• Fast heuristics using Lagrangian dual information
• Intensification through variable fixing / local branching
• Solution refinement through matching model
Railways vs. Airlines
• Large instances in comparison with airlines:
- more activities / legs per instance (14,000)
- more activities / legs per duty / pairing (avg. 14)
• Crew qualifications cannot be used to partition instances
• More complex and detailed rules:
- rolling stock circulation
- complex variation rules
- depot constraints
Efficiency improvement 2003 - 2004
• Total amount of work: + 3.2%
• Total # duties:
+ 1.2%
• Initial savings:
 2.0%  $ 7 million
• Real savings:  $ 4.8 million as variation was larger than agreed
earlier in order to increase personnel acceptance
• Generation of the duties
subject to the new rules
would have been impossible
without TURNI
Efficiency improvement 2005
• Reduction of the transfer time from 25 to 20 minutes
(only a minor negative effect on robustness / punctuality)
• Efficiency improvement of 2.5% or $8 million per year
• Total savings: $7 + $8 = $15 million per year
• Using TURNI, changing the duty structures is easy
(both for analysis and for production planning)
Conclusions
• Application of TURNI led to:
- a new production model (Sharing Sweet&Sour)
- more efficient duties (4.5%)
• Unpunctuality decreased by 25% (Punctuality 80% → 85%)
• Motivation of personnel up
• Sickness rate down by 50%
• BOTTOM UP > TOP DOWN
Conclusions
• Initially nobody believed in the existence of a solution for
the conflicts between all parties
• Combination of a participative approach together with
expertise in Operations Research led to the success
• Operations Research led to quantitative and objective
results accepted both by personnel and management
Conclusions
Depot A
Union A
Works
Council
Union B
Management
Depot B
Conclusions
DepotOA
Union A
Works
Council
Union B
O
Management
Depot B
Conclusions
DepotOA
Union A
Works
Council
Union B
--
Management
Depot B
Conclusions
DepotOA
Union A
Works
Council
Union B
R
Management
Depot B