Impact of Competition on Quality of Service in Demand Responsive Transit

  • Ferdi Grootenboers
  • Mathijs de Weerdt
  • Mahdi Zargayouna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6251)


Demand responsive transportation has the potential to provide efficient public door-to-door transport with a high quality. In currently implemented systems in the Netherlands, however, we observe a decrease in the quality of service (QoS), expressed in longer travel times for the customers. Currently, generally one transport company is responsible for transporting all customers located in a specified geographic zone. In general it is known that when multiple companies compete on costs, the price for customers decreases. In this paper, we investigate whether a similar result can be achieved when competing on quality instead. To arrive at some first conclusions, we set up a multiagent environment to simulate the assignment of rides to companies through an auction on QoS, and the insertion of allocated rides in the companies’ schedules using online optimization. Our results reveal that this set-up improves the quality of the service offered to the customers at moderately higher costs.


Dial-a-ride multi-company quality of service auction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cordeau, J.F.: A branch-and-cut algorithm for the dial-a-ride problem. Operations Research 54, 573–586 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York (1972)CrossRefGoogle Scholar
  3. 3.
    Ropke, S., Cordeau, J.F., Laporte, G.: Models and branch-and-cut algorithms for pickup and delivery problems with time windows. Networks 49, 258–272 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Jaw, J.J., Odoni, A.R., Psaraftis, H.N., Wilson, N.H.M.: A heuristic algorithm for the multi-vehicle advance request dial-a-ride problem with time windows. Transportation Research Part B: Methodological 20, 243–257 (1986)CrossRefGoogle Scholar
  5. 5.
    Mahr, T., Srour, J., de Weerdt, M.M., Zuidwijk, R.: Can agents measure up? a comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty. Transportation Research: Part C 18, 99–119 (2010)CrossRefGoogle Scholar
  6. 6.
    Mes, M.: Sequential Auctions for Full Truckload Allocation. PhD thesis, Universiteit Twente, Enschede, The Netherlands (2008)Google Scholar
  7. 7.
    Solomon, M.: Algorithms for the vehicle routing and scheduling with time window constraints. Operations Research 15, 254–265 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Metropolis, N., Ulam, S.: The monte carlo method. Journal of the American Statistical Association 44, 335–341 (1949)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Bellifemine, F., Poggi, A., Rimassa, G.: JADE - a FIPA-compliant agent framework. In: Proceedings of the Practical Applications of Intelligent Agents (1999)Google Scholar
  10. 10.
    Achterberg, T.: SCIP - a framework to integrate constraint and mixed integer programming. Technical Report 4-19, Zuse Institute Berlin (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ferdi Grootenboers
    • 1
  • Mathijs de Weerdt
    • 1
  • Mahdi Zargayouna
    • 2
  1. 1.Delft University of TechnologyDelftThe Netherlands
  2. 2.INRETS Institute, Gretia laboratory, Building “Descartes II”Noisy le Grand CedexFrance

Personalised recommendations