Abstract
The delivery and courier services are entering a period of rapid change due to the recent technological advancements, E-commerce competition and crowdsourcing business models. These revolutions impose new challenges to the well studied vehicle routing problem by demanding (a) more ad-hoc and near real time computation - as opposed to nightly batch jobs - of delivery routes for large number of delivery locations, and (b) the ability to deal with the dynamism due to the changing traffic conditions on road networks. In this paper, we study the Time-Dependent Vehicle Routing Problem (TDVRP) that enables both efficient and accurate solutions for large number of delivery locations on real world road network. Previous Operation Research (OR) approaches are not suitable to address the aforementioned new challenges in delivery business because they all rely on a time-consuming a priori data-preparation phase (i.e., the computation of a cost matrix between every pair of delivery locations at each time interval). Instead, we propose a spatial-search-based framework that utilizes an on-the-fly shortest path computation eliminating the OR data-preparation phase. To further improve the efficiency, we adaptively choose the more promising delivery locations and operators to reduce unnecessary search of the solution space. Our experiments with real road networks and real traffic data and delivery locations show that our algorithm can solve a TDVRP instance with 1000 delivery locations within 20 min, which is 8 times faster than the state-of-the-art approach, while achieving similar accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Reducing the computation cost of each evaluation usually depends more on the problem setting, for example a method that utilizes time window constraints and dynamic programming to reduce evaluation cost was proposed in [6] for the TDVRPTW problem. In this paper, we work on a general TDVRP setting and thus focus on reducing the number of evaluations.
- 2.
Note that in operation research literatures, even an instance with 500 delivery locations is considered large. Algorithms are usually tested on much smaller instances.
References
Cordeau, J.F., Gendreau, M., Laporte, G., Potvin, J.Y., Semet, F.: A guide to vehicle routing heuristics. J. Oper. Res. Soc. 53(5), 512–522 (2002)
The Wall Street Journal: At UPS, the algorithm is the driver (2015). http://www.wsj.com/articles/at-ups-the-algorithm-is-the-driver-1424136536
Google Express. https://www.google.com/shopping/express/
Demiryurek, U., Banaei-Kashani, F., Shahabi, C., Ranganathan, A.: Online computation of fastest path in time-dependent spatial networks. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 92–111. Springer, Heidelberg (2011)
Malandraki, C., Daskin, M.S.: Time dependent vehicle routing problems: formulations, properties and heuristic algorithms. Transp. Sci. 26(3), 185–200 (1992)
Hashimoto, H., Yagiura, M., Ibaraki, T.: An iterated local search algorithm for the time-dependent vehicle routing problem with time windows. Discrete Optim. 5(2), 434–456 (2008)
Li, F., Golden, B., Wasil, E.: Very large-scale vehicle routing: new test problems, algorithms, and results. Comput. Oper. Res. 32(5), 1165–1179 (2005)
Groer, C.: Parallel and serial algorithms for vehicle routing problems. ProQuest (2008)
Clarke, G., Wright, J.W.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12, 568–581 (1964)
Demiryurek, U., Banaei-Kashani, F., Shahabi, C.: Efficient K-nearest neighbor search in time-dependent spatial networks. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part I. LNCS, vol. 6261, pp. 432–449. Springer, Heidelberg (2010)
Laporte, G., Gendreau, M., Potvin, J.Y., Semet, F.: Classical and modern heuristics for the vehicle routing problem. Int. Trans. Oper. Res. 7(4–5), 285–300 (2000)
Potvin, J.Y., Kervahut, T., Garcia, B.L., Rousseau, J.M.: The vehicle routing problem with time windows part I: tabu search. INFORMS J. Comput. 8(2), 158–164 (1996)
Baldacci, R., Hadjiconstantinou, E., Mingozzi, A.: An exact algorithm for the capacitated vehicle routing problem based on a two-commodity network flow formulation. Oper. Res. 52(5), 723–738 (2004)
Gillett, B.E., Miller, L.R.: A heuristic algorithm for the vehicle-dispatch problem. Oper. Res. 22(2), 340–349 (1974)
Longo, H., de Aragão, M.P., Uchoa, E.: Solving capacitated arc routing problems using a transformation to the CVRP. Comput. Oper. Res. 33(6), 1823–1837 (2006)
Letchford, A.N., Nasiri, S.D., Oukil, A.: Pricing routines for vehicle routing with time windows on road networks. Comput. Oper. Res. 51, 331–337 (2014)
Ichoua, S., Gendreau, M., Potvin, J.Y.: Vehicle dispatching with time-dependent travel times. Eur. J. Oper. Res. 144, 379–396 (2003)
Kok, A., Hans, E., Schutten, J.: Vehicle routing under time-dependent travel times: the impact of congestion avoidance. Comput. Oper. Res. 39(5), 910–918 (2012)
Acknowledgements
This research has been funded in part by NSF grants IIS-1115153 and IIS-1320149, the USC Integrated Media Systems Center (IMSC), METRANS Transportation Center under grants from Caltrans, and unrestricted cash gifts from Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the sponsors such as the National Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, Y., Deng, D., Demiryurek, U., Shahabi, C., Ravada, S. (2015). Towards Fast and Accurate Solutions to Vehicle Routing in a Large-Scale and Dynamic Environment. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-22363-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22362-9
Online ISBN: 978-3-319-22363-6
eBook Packages: Computer ScienceComputer Science (R0)