Automated Driver Scheduling for Vehicle Delivery

  • Shashika R. MuramudaligeEmail author
  • H. M. N. Dilum Bandara
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 222)


Vehicle delivery is a major business where third-party drivers are hired to deliver vehicles when they are relocated, sold, or while returning rental cars. This is a complex process due to the wide variation in collection/delivery locations, time bounds, types of vehicles, special skills required by drivers, and impact due to traffic and weather. We propose an automated driver scheduling solution to maximize the number of vehicle deliveries and customer satisfaction while minimizing the delivery cost. Proposed solution consists of a rule checker and a scheduler. Rule checker enforces constraints such as deadlines, license types, skills, and working hours. Scheduler uses simulated annealing to assign as many jobs as possible while minimizing the overall cost. Using a workload derived from an actual vehicle delivery company, we demonstrate that the proposed solution has good coverage of jobs while minimizing the cost and having flexibility to tolerate breakdowns, excessive traffic, and bad weather.


Scheduling Simulated annealing Vehicle Delivery 



This research is supported in part by the Senate Research Grant of the University of Moratuwa under award number SRC/LT/2016/14.


  1. 1.
    Bertossi, A.A., Carraresi, P., Gallo, G.: On some matching problems arising in vehicle scheduling models. Networks 17, 271–281 (1987)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wren, A., Fores, S., Kwan, A.S.K., Kwan, R.S.K., Parker, M., Proll, L.G.: A flexible system for scheduling drivers. J. Sched. 6(5), 437–455 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Fores, S., Proll, L.G., Wren, A.: An improved ILP system for driver scheduling. In: Wilson, N.H.M. (ed.) Computer-Aided Transit Scheduling, pp. 43–62. Springer, Heidelberg (1999). Scholar
  4. 4.
    Laurent, B., Hao, J.K.: Simultaneous vehicle and driver scheduling: a case study in a Limousine Rental Company (2007)CrossRefGoogle Scholar
  5. 5.
    Hong, L., Ying, W., Shi, L., Sujian, L.: A column generation based hyper-heuristic to the bus driver scheduling problem. In: Discrete Dynamics in Nature and Society, pp. 1–10 (2015)Google Scholar
  6. 6.
    Davis, D.L., Gillenwater, E.L., Johnson, J.D.: An artificial neural systems framework for delivery truck scheduling. In: 23rd Annual Hawaii International Conference on System Sciences, vol. 3 (1990)Google Scholar
  7. 7.
    Maghrebi, M., Sammut, C., Waller, S.T.: Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques. Eng. Constr. Architectural Manag. 22(5), 573–590 (2015)CrossRefGoogle Scholar
  8. 8.
    Mayssa, K., Souhail, D., Diala, D., Abderrahman, E.M.: Truck driver scheduling problem: literature review. IFAC-PapersOnLine 49–12, 1950–1955 (2016)Google Scholar
  9. 9.
    Yang, X., Sylvain, G., Florian, M.: Generalized simulated annealing, computational optimization in engineering - paradigms and applications. InTech (2017).
  10. 10.
    Google distance matrix API. Accessed 5 Oct 2017
  11. 11.
    Anily, S., Federgruen, A.: Simulated annealing methods with general acceptance probabilities. J. Appl. Probab. 24(3), 657–667 (1987)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Nourani, Y., Andresen, B.: A comparison of simulated annealing cooling strategies. J. Phys. A: Math. Gen. 31, 8373–8385 (1998)CrossRefGoogle Scholar
  13. 13.
    Income inequality and dualism. Accessed 5 Oct 2017

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Shashika R. Muramudalige
    • 1
    Email author
  • H. M. N. Dilum Bandara
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of MoratuwaMoratuwaSri Lanka

Personalised recommendations