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BoneRoute: An Adaptive Memory-Based Method for Effective Fleet Management

Author

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  • C.D. Tarantilis
  • C.T. Kiranoudis

Abstract

This paper presents an adaptive memory-based method for solving the Capacitated Vehicle Routing Problem (CVRP), called BoneRoute. The CVRP deals with the problem of finding the optimal sequence of deliveries conducted by a fleet of homogeneous vehicles, based at one depot, to serve a set of customers. The computational performance of the BoneRoute was found to be very efficient, producing high quality solutions over two sets of well known case studies examined. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • C.D. Tarantilis & C.T. Kiranoudis, 2002. "BoneRoute: An Adaptive Memory-Based Method for Effective Fleet Management," Annals of Operations Research, Springer, vol. 115(1), pages 227-241, September.
    • Handle: RePEc:spr:annopr:v:115:y:2002:i:1:p:227-241:10.1023/a:1021157406318
    DOI: 10.1023/A:1021157406318
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    Citations

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    Cited by:

    1. Oscar Dominguez & Angel A. Juan & Barry Barrios & Javier Faulin & Alba Agustin, 2016. "Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet," Annals of Operations Research, Springer, vol. 236(2), pages 383-404, January.
    2. Gilbert Laporte, 2009. "Fifty Years of Vehicle Routing," Transportation Science, INFORMS, vol. 43(4), pages 408-416, November.
    3. Gilbert Laporte, 2007. "What you should know about the vehicle routing problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 811-819, December.
    4. C D Tarantilis & E E Zachariadis & C T Kiranoudis, 2008. "A guided tabu search for the heterogeneous vehicle routeing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(12), pages 1659-1673, December.
    5. Dieter, Peter & Caron, Matthew & Schryen, Guido, 2023. "Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework," European Journal of Operational Research, Elsevier, vol. 311(1), pages 283-300.
    6. Oscar Dominguez & Angel Juan & Barry Barrios & Javier Faulin & Alba Agustin, 2016. "Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet," Annals of Operations Research, Springer, vol. 236(2), pages 383-404, January.
    7. C D Tarantilis & G Ioannou & C T Kiranoudis & G P Prastacos, 2005. "Solving the open vehicle routeing problem via a single parameter metaheuristic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 588-596, May.
    8. Yu, Junfang & Dong, Yuanyuan, 2013. "Maximizing profit for vehicle routing under time and weight constraints," International Journal of Production Economics, Elsevier, vol. 145(2), pages 573-583.
    9. A A Juan & J Faulin & J Jorba & D Riera & D Masip & B Barrios, 2011. "On the use of Monte Carlo simulation, cache and splitting techniques to improve the Clarke and Wright savings heuristics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1085-1097, June.
    10. Yildiz, Hakan & Ravi, R. & Fairey, Wayne, 2010. "Integrated optimization of customer and supplier logistics at Robert Bosch LLC," European Journal of Operational Research, Elsevier, vol. 207(1), pages 456-464, November.
    11. Szeto, W.Y. & Wu, Yongzhong & Ho, Sin C., 2011. "An artificial bee colony algorithm for the capacitated vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 215(1), pages 126-135, November.
    12. Subramanian, Anand & Penna, Puca Huachi Vaz & Uchoa, Eduardo & Ochi, Luiz Satoru, 2012. "A hybrid algorithm for the Heterogeneous Fleet Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 221(2), pages 285-295.
    13. Quirion-Blais, Olivier & Chen, Lu, 2021. "A case-based reasoning approach to solve the vehicle routing problem with time windows and drivers’ experience," Omega, Elsevier, vol. 102(C).
    14. Guido Perboli & Ferdinando Pezzella & Roberto Tadei, 2008. "EVE-OPT: a hybrid algorithm for the capacitated vehicle routing problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 68(2), pages 361-382, October.
    15. Tarantilis, C.D. & Kiranoudis, C.T., 2007. "A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector," European Journal of Operational Research, Elsevier, vol. 179(3), pages 806-822, June.
    16. Bolduc, Marie-Claude & Laporte, Gilbert & Renaud, Jacques & Boctor, Fayez F., 2010. "A tabu search heuristic for the split delivery vehicle routing problem with production and demand calendars," European Journal of Operational Research, Elsevier, vol. 202(1), pages 122-130, April.
    17. Zhang, Zhenzhen & Wei, Lijun & Lim, Andrew, 2015. "An evolutionary local search for the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 20-35.

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