1. 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

2. 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

3. 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

4. 29 Depots Duties are created in Utrecht (Ut) Rosters are created locally in the depots Focus: Duties NS Reizigers (Netherlands Railways) Ut

5. Crew management Efficiency Acceptance Robustness Punctuality Find: - Balance - Trade-off

6. Minimum transfer time Pre- and post times Meal break rule Maximum duty length - Route knowledge - Rolling stock knowledge Duty examples

7. Rostering Rules Maximum percentage night duties Maximum percentage long duties (>9 hrs) Maximum average duty length

8. 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

9. 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

10. Involved parties Union A Union BDepot B Depot A Works Council Management STRIKES

11. Union A Union BDepot B Depot A Works Council Management Involved parties

12. Development of alternative model Union A Union BDepot B Depot A Works Council Management

13. Participative approach (BOTTOM UP) Alternative 1 Alternative n ‘Sharing Sweet & Sour’........ Selection by Works Council Acceptance by Management 2-day conferences Hundreds of optimization runs with TURNI

14. 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

15. Sharing Sweet&Sour

17. ---------------------------------------------------------- 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 | Sharing Sweet&Sour: variation statistics

18. 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

19. (Extended) Set Covering model subject to

20. 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

21. 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

22. Generation of the duties subject to the new rules would have been impossible without TURNI Total amount of work: + 3.2% Total # duties:+ 1.2% Initial savings:  2.0%  $ 7 million $ 7 mill. $ 4.8 mill. Real savings:  $ 4.8 million as variation was larger than agreed earlier in order to increase personnel acceptance Efficiency improvement 2003 - 2004

23. 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)

24. 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

25. 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

26. Union A Union BDepot B Depot A Works Council Management Conclusions

27. Union A Union BDepot B Depot A Works Council Management Conclusions O

28. Union A Union BDepot B Depot A Works Council Management Conclusions O --

29. Union A Union BDepot B Depot A Works Council Management Conclusions O R