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.
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Notes
- 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.
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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.
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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
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