Public Transport

, Volume 2, Issue 1–2, pp 25–49 | Cite as

Socially acceptable annual holiday planning for the crew of a local public transport company in Germany

  • Sigrun DewessEmail author
Original Paper


We consider the problem of socially acceptable annual holiday planning. A new model is developed taking into account legal, company and driver issues. Among others, it includes capacity constraints concerning different qualifications, holiday entitlements and connections between drivers. For each application for leave benefit values depending on family situations (e.g. driver has children of school age), other social criteria and priorities of applications are defined for each possible day of the application.

The problem is solved by a heuristic two-stage algorithm. In the first stage we assume that applications for leave are approved, resolve capacity conflicts and arrange applications for leave to get a feasible solution with a high benefit. In the second stage we try to improve the gained feasible solution. Computational results show, that instances with up to 10,000 drivers can be solved within a reasonable amount of time.


Crew scheduling Holiday planning Vacation scheduling Social scheduling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bowman EH (1959) The schedule-sequencing problem. Oper Res 7(5):621–624 CrossRefGoogle Scholar
  2. Burke EK, De Causmaecker P, Berghe GV, Van Landeghem H (2004) The state of the art of nurse rostering. J Sched 7(6):441–499 CrossRefGoogle Scholar
  3. Ceder A (2007) Public transit planning and operation: theory, modeling and practice. Butterworth-Heinemann, Oxford Google Scholar
  4. Ernst AT, Jiang H, Krishnamoorthy M, Owens B, Sier D (2004) An annotated bibliography of personnel scheduling and rostering. Ann Oper Res 127:21–144 CrossRefGoogle Scholar
  5. Gärtner J, Wahl S, Hörwein K (1998) A technique to take leave into account in shift-rota design. Scand J Work Environ Health 24(3):103–108 Google Scholar
  6. Haase K, Desaulniers G, Desrosiers J (2001) Simultaneous vehicle and crew scheduling in urban mass transit systems. Transp Sci 35(3):286–303 CrossRefGoogle Scholar
  7. Hartog A, Huisman D, Abbink EJW, Kroon LG (2009) Decision support for crew rostering at NS. Public Transp 1(2):121–133 CrossRefGoogle Scholar
  8. Hickman M, Mirchandani P, VoßS (eds) (2008) Computer-aided systems in public transport. Lect notes econ math syst, vol 600. Springer, Berlin Google Scholar
  9. Johnston MD, Minton S (1994) Analyzing a heuristic strategy for constraint-satisfaction and scheduling. In: Fox M, Zweben M (eds) Intelligent scheduling. Morgan Kaufmann Publishers, San Francisco, pp 257–289 Google Scholar
  10. Knust S, Schumacher E (2009) Shift scheduling for tank trucks. Mathematik-Bericht 2009/6, Institut für Mathematik, TU Clausthal, Germany Google Scholar
  11. Koutsopoulos HN, Wilson NHM (1987) Operator workforce planning in the transit industry. Transp Res Part A 21(2):127–138 CrossRefGoogle Scholar
  12. L. De Grano M (2009) Improving fairness in nurse scheduling. In: Shiver JM, Eitel D (eds) Optimizing emergency department throughput: operations management solutions for health care decision makers. Productivity Press, New York, pp 169–182 Google Scholar
  13. L. De Grano M, Medeiros DJ, Eitel D (2009) Accommodating individual preferences in nurse scheduling via auctions and optimization. Health Care Manag Sci 12(3):228–242 CrossRefGoogle Scholar
  14. Laporte G (1999) The art and science of designing rotating schedules. J Oper Res Soc 50(10):1011–1017 Google Scholar
  15. Laporte G, Pesant G (2004) A general multi-shift scheduling system. J Oper Res Soc 55(11):1208–1217 CrossRefGoogle Scholar
  16. Li J, Kwan RSK (2003) A fuzzy genetic algorithm for driver scheduling. Eur J Oper Res 147(2):334–344 CrossRefGoogle Scholar
  17. Maenhout B, Vanhoucke M (2010) A hybrid scatter search heuristic for personalized crew rostering in the airline industry. Eur J Oper Res 206(1):155–167 CrossRefGoogle Scholar
  18. Muscettola N (1992) Scheduling by iterative partition of bottleneck conflicts. Technical Report CMU-RI-TR-92-05, The Robotics Institute, Carnegie Mellon University, USA Google Scholar
  19. Pérez G, de la Maza ES (2006) Crew rostering problem in a public transport company. J Oper Res Soc 57:1173–1179 CrossRefGoogle Scholar
  20. Reinsch A (2007) Fachkonzept zur EDV-gestützten automatisierten Urlaubsplanung und—abstimmung für Fahrdienstmitarbeiter unter Berücksichtigung von verkehrlichen, mitarbeiterbezogenen und dispositiven Faktoren. Unpublished diploma thesis, Dresden: University of Technology, Germany Google Scholar
  21. Smith LD, Bird D, Wiggins A (1979) A computerised system to schedule nurses that recognises staff preferences. Hosp Health Serv Adm 24(4):19–35 Google Scholar
  22. Sohoni MG, Johnson EL, Bailey TG (2006) Operational airline reserve crew planning. J Sched 9(3):203–221 CrossRefGoogle Scholar
  23. Xu J, Sohoni M, McCleery M, Bailey TG (2006) A dynamic neighborhood based tabu search algorithm for real-world flight instructor scheduling problems. Eur J Oper Res 169(3):978–993 CrossRefGoogle Scholar
  24. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Fakultät Verkehrswissenschaften “Friedrich List”, Institut für Wirtschaft und VerkehrTechnische Universität DresdenDresdenGermany

Personalised recommendations