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A unified approach is presented as the principle for a potential framework for evaluating driver schedules in public transport. There are two reasons for the need for such a framework. On the one hand, constructing optimal driver schedules is a challenging problem, and practical solutions cannot be compared to the optimum, which brings about the need to analyse the results with respect to an appropriate estimated value. On the other hand, the specific constraints and rules in different countries and for different companies make it difficult to model solutions in a unified way. Our new approach provides a solution to the above problems by employing an efficient general methodology from both the data modelling and process modelling point of view. By introducing the concept of reducible working time, our approach gives a realistic evaluation framework with an efficient solution method. The applicability of our approach is demonstrated through real-world cases.
Czasopismo
Rocznik
Tom
Strony
163--174
Opis fizyczny
Bibliogr. 23 poz.
Twórcy
autor
- University of Szeged; Árpád tér 2, H-6720 Szeged, Hungary
autor
- University of Primorska; Glagoljaška 8, SI-6000 Koper, Slovenia
autor
- InnoRenew CoE; Livade 6, SI-6310 Izola, Slovenia
autor
- University of Szeged; Boldogasszony sgt 6, H-6725 Szeged, Hungary
autor
- University of Szeged; Boldogasszony sgt 6, H-6725 Szeged, Hungary
Bibliografia
- 1. Ibarra-Rojas, O.J. & Delgado, F. & Giesen, R. & Muñoz, J.C. Planning, operation, and control of bus transport systems: A literature review. Transportation Research Part B: Methodological. 2015. Vol. 77. P. 38-75.
- 2. Bunte, S. & Kliewer, N. An overview on vehicle scheduling models, Journal of Public Transport. 2009. Vol. 1. No. 4. P. 299-317.
- 3. Ernst, A.T. & Jiang, H. & Krishnamoorthy, M. & Sier, D. Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research. 2004. Vol. 153. No. 1. P. 3-27.
- 4. Mesquita, M. & Paias, A. & Respício, A. Branching approaches for integrated vehicle and crew scheduling. Public Transport. 2009. Vol. 1. P. 21-37.
- 5. Steinzen, I. & Gintner, V. & Suhl, L. & Kliewer, N. A time-space network approach for the integrated vehicle-and crew-scheduling problem with multiple depots. Transportation Science. 2010. Vol. 44. No. 3. P. 367-382.
- 6. Békési, J. & Nagy, A. Combined vehicle and driver scheduling with fuel consumption and parking constraints: a case study. Acta Polytechnica Hungarica. 2020. Vol. 17. No. 7. P. 45-65.
- 7. Borndörfer, R. & Schulz, C. & Seidl, S. & Weider, S. Integration of duty scheduling and rostering to increase driver satisfaction. Public Transport. 2017. Vol. 9. P. 177-191.
- 8. Árgilán, V. & Balogh, J. & Békési, J. & Dávid, B. & Krész, M. & Tóth, A. Driver scheduling based on “driver-friendly” vehicle schedules. Operations Research Proceedings. 2011. P. 323-328.
- 9. Vdovychenko, V. Formation of methodological levels of assessing city public passenger transport efficiency. Eastern-European Journal of Enterprise Technologies. 2016. No. 3(81). P. 44-51.
- 10. Asvin, G. Vehicle scheduling and routing with drivers’ working hours. Transportation Science. 2009. Vol. 43. No. 1. 2009. P. 17-26.
- 11. CEN- EN12896-1:2016. Public transport - Reference data model - Part 1: Common concepts. Comité Européen de Normalisation. 132 p.
- 12. Nurmi, K. & Kyngäs, J & Post, G. Driver rostering for bus transit companies. Engineering Letters. 2011. Vol. 19. No. 2. P. 125-132.
- 13. De Bruecker, P. & Van den Bergh, J. & Beliën, J. & Demeulemeester, E. Workforce planning incorporating skills: State of the art. European Journal of Operational Research, 2015. Vol. 243. No. 1. P. 1-16.
- 14. Garey, M.R. & Johnson, D.S. Computers and Interactability: A Guide to the Theory of NP-Completness. Freeman, San Fransisco. 1979.
- 15. Demirović, E. & Musliu, N. & Winter, F. Modeling and solving staff scheduling with partial weighted maxSAT. Annals of Operations Research. 2019. Vol. 275. P. 79-99.
- 16. Tóth, A. & Krész, M. A flexible optimization framework for driver scheduling, Proceedings of the 11th International Symposium on Operational Research SOR ’11. 2011. P. 341-346.
- 17. Kletzander, L. & Musliu, N. Solving the general employee scheduling problem. Computers & Operations Research. 2020. Vol. 113. P. 1-13.
- 18. Schiewe, A. & Albert, S. & Schiewe, P. & Schöbel, A. & Spühler, F. LinTim: An integrated environment for mathematical public transport optimization. Documentation for version 2020.12. TU Kaiserslautern, 2020. Available at: https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-62025.
- 19. Ahuja, R.K. & Magnanti, T.L. & Orlin, J.B. Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Inc. 1993.
- 20. Bertossi, A.A. & Carraresi, P. & Gallo, G. On some matching problems arising in vehicle scheduling models. Networks. 1987. Vol. 17. P. 271-281.
- 21. Löbel, A. Vehicle scheduling in public transit and lagrangean pricing. Management Science. 1998. Vol. 44. No. 12. P. 1637-1650.
- 22. Tóth, A. & Krész, M. An efficient solution approach for real-world driver scheduling problems in urban bus transportation. Central European Journal of Operations Research. 2013. Vol. 21. No. 1. P. 75-94.
- 23. Kliewer, N. & Mellouli, T. A time-space network based exact optimization model for multi-depot bus scheduling. European Journal of Operational Research. 2006. Vol. 175. P. 1616-1627.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d77b853f-df90-4631-86a4-62973e54b6e8