Skip to main content

School Bus Routing in Rural School Districts

  • Conference paper

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 600))

Abstract

The Commonwealth of Pennsylvania has the nation’s largest rural population and the Commonwealth plays an important role in providing transportation for students to travel to their respective schools. State and local governments reimburse school districts for student transportation costs in Pennsylvania. Effective policies for governing the transportation of students can result in large cost savings for the respective governments and reduced travel time for the students. This paper presents heuristics to solve a complex rural school bus routing problem using digitized road networks that can lead to cost savings for both State and local governments. The school bus routing problem addressed and solved in this paper is a mixed-fleet, multi-depot, site-dependent, split-delivery problem with side constraints. Computation of real road distances for the rural school district between pickup points, depots and schools, consisting of 4200 road segments, was done using digitized road networks obtained from the U. S. Census Bureau. Heuristic algorithms were designed and implemented to solve a school bus routing problem with real life data obtained from a rural school district. Feasible solutions to the complex rural school bus routing problem, consisting of 13 depots, 5 schools, 71 pickup points and 583 students, were obtained in less than 10 minutes of CPU time.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bodin, L. and Berman, L. (1979). Routing and scheduling of school buses by computer. Transportation Science, 13, 113–129.

    Google Scholar 

  • Bodin, L., Golden, B. L., Assad, A. A., and Ball, M. O. (1983). Routing and scheduling of vehicles and crews. Computers & Operations Research, 10, 63–211.

    Article  Google Scholar 

  • Chao, I.-M., Golden, B. L., and Wasil, E. (1993). A new heuristic for the multi-depot vehicle routing problem that improves upon best-known solutions. American Journal of Mathematical & Management Sciences, 13, 371–401.

    Google Scholar 

  • Chao, I.-M., Golden, B. L., and Wasil, E. (1999). A computational study of a new heuristic for the site-dependent vehicle routing problem. INFORMS Journal on Computing, 37, 319–336.

    Google Scholar 

  • Chao, I.-M., Golden, B., and Wasil, E. (2004). A computational study of a new heuristic for the site-dependent vehicle routing problem. INFOR, 37(3), 319–336.

    Google Scholar 

  • Christofides, N. and Eilon, S. (1969). An algorithm for the vehicle-dispatching problem. Operational Research Quarterly, 20, 309–318.

    Article  Google Scholar 

  • Cordeau, J., Gendreau, M., and Laporte, G. (1997). A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks, 30, 105–119.

    Article  Google Scholar 

  • Cordeau, J.-F. and Laporte, G. (2001). A tabu search algorithm for the site dependent vehicle routing problem with time windows. INFOR, 39, 292–298.

    Google Scholar 

  • Cordeau, J.-F., Gendreau, M., Laporte, G., Potvin, J.-Y., and Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research Society, 53, 512–522.

    Article  Google Scholar 

  • Desrochers, M. and Verhoog, T.W. (1991). A new heuristic for the fleet size and mix vehicle routing problem. Computers & Operations Research, 3, 263–274.

    Article  Google Scholar 

  • Dror, M. and Trudeau, P. (1989). Savings by split delivery. Transportation Science, 23, 141–145.

    Google Scholar 

  • Dror, M., Laporte, G., and Trudeau, P. (1994). Vehicle routing with split deliveries. Discrete Applied Mathematics, 50, 239–254.

    Article  Google Scholar 

  • Fisher, M. L. (1995). Vehicle routing. In M. Ball, T. Magnanti, C. Monma, and G. Nemhauser, editors, Network Routing. Handbooks on Operations Research and Management Science, pages 1–33. North-Holland, Amsterdam.

    Google Scholar 

  • Gendreau, M., Laporte, G., Musaraganyi, C., and Taillard, E. (1999). A tabu search heuristic for the heterogeneous fleet mix vehicle routing problem. Computers & Operations Research, 26, 1153–1173.

    Article  Google Scholar 

  • Gheysens, F., Golden, B. L., and Assad, A. A. (1984). A comparison of techniques for solving the fleet size and mix vehicle routing problems. OR Spektrum, 6, 207–216.

    Article  Google Scholar 

  • Golden, B., Assad, A. A., Levy, L., and Gheysens, F. (1984). The fleet size and mix vehicle routing problem. Computers & Operations Research, 11, 49–66.

    Article  Google Scholar 

  • Horowitz, E. and Sahni, S. (1988). Fundamentals of Computer Algorithms. Computer Science Press, Maryland.

    Google Scholar 

  • Laporte, G. (1992). The vehicle routing problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59, 345–358.

    Article  Google Scholar 

  • Laporte, G. and Osman, I. H. (1995). Routing problems: A bibliography. Annals of Operations Research, 61, 227–262.

    Article  Google Scholar 

  • Laporte, G., Nobert, Y., and Arpin, A. (1988). Optimal solutions to capacitated multi-depot vehicle routing problems. Congressus Numerantium, 44, 283–292.

    Google Scholar 

  • Lin, S. (1965). Computer solutions of the traveling salesman problem. Bell Systems Technical Journal, 44, 2245–2269.

    Google Scholar 

  • Lin, S. and Klingman, D. (1973). An effective solution to the traveling salesman problem. Operations Research, 20, 498–516.

    Google Scholar 

  • Nag, B., Golden, B. L., and Assad, A. A. (1998). Vehicle routing with site dependencies. In B. Golden and A. Assad, editors, Vehicle Routing: Methods and Studies, pages 149–159. North-Holland, Amsterdam.

    Google Scholar 

  • Osman, I. and Christofides, N. (1994). Capacitated clustering problems by hybrid simulated annealing and tabu search. International Transactions in Operational Research, 1, 317–336.

    Article  Google Scholar 

  • Renaud, J., Laporte, G., and Boctor, F. (1996). A tabu search heuristic for the multidepot vehicle routing problem. Computers & Operations Research, 23, 229–235.

    Article  Google Scholar 

  • Salhi, S. and Rand, G. K. (1993). Incorporating vehicle routing into the vehicle fleet composition problem. European Journal of Operational Research, 66, 313–330.

    Article  Google Scholar 

  • Salhi, S. and Sari, M. (1997). A multi-level composite heuristic for the multi-depot vehicle fleet mix problem. European Journal of Operational Research, 103, 95–112.

    Article  Google Scholar 

  • Serna, C. and Bonrostro, J. (2001). Minimax vehicle routing problems: Application to school transport in the province of Burgos. In S. Voss and J. Daduna, editors, Computer-Aided Scheduling of Public Transport, pages 297–317. Springer, Berlin.

    Google Scholar 

  • Thangiah, S. R. (1996). Genetic algorithms for vehicle routing problems with time windows. In L. Chambers, editor, Applications Handbook of Genetic Algorithms, pages 253–277. CRC Press, Boca Raton.

    Google Scholar 

  • Thangiah, S. R. and Nygaard, K. (1992). School bus routing using genetic algorithms. In G. Biswas, editor, Proceedings of the Applications of Artificial Intelligence X: Knowledge-Based Systems, pages 387–398. IEEE Press.

    Google Scholar 

  • Thangiah, S. R. and Petrovic, P. (1998). Introduction to genetic heuristics and vehicle routing problems with complex constraints. In D. Woodruff, editor, Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search, pages 253–286. Kluwer Academic.

    Google Scholar 

  • Thangiah, S. R. and Salhi, S. (2001). Genetic clustering: An adaptive heuristic for the multi-depot vehicle routing problem. Applied Artificial Intelligence, 15, 361–383.

    Article  Google Scholar 

  • Thangiah, S. R., Osman, I. H., and Vinayagamoorthy, R. (1993). Algorithms for vehicle routing problems with time deadlines. American Journal of Mathematical & Management Sciences, 13, 322–355.

    Google Scholar 

  • Thangiah, S. R., Potvin, J.-Y., and Sun, T. (1996). Heuristic approaches to vehicle routing with backhauls and time windows. Computers & Operations Research, 23, 1043–1057.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Thangiah, S.R., Fergany, A., Wilson, B., Pitluga, A., Mennell, W. (2008). School Bus Routing in Rural School Districts. In: Hickman, M., Mirchandani, P., Voß, S. (eds) Computer-aided Systems in Public Transport. Lecture Notes in Economics and Mathematical Systems, vol 600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73312-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73312-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73311-9

  • Online ISBN: 978-3-540-73312-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics