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Towards Fast and Accurate Solutions to Vehicle Routing in a Large-Scale and Dynamic Environment

  • Yaguang LiEmail author
  • Dingxiong Deng
  • Ugur Demiryurek
  • Cyrus Shahabi
  • Siva Ravada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)

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.

Keywords

Local Search Road Network Travel Cost Vehicle Rout Problem Delivery Location 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yaguang Li
    • 1
    Email author
  • Dingxiong Deng
    • 1
  • Ugur Demiryurek
    • 1
  • Cyrus Shahabi
    • 1
  • Siva Ravada
    • 2
  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesCalifornia
  2. 2.OracleUSA

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