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Data mining-based dispatching system for solving the local pickup and delivery problem

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  • Weiwei Chen
  • Jie Song
  • Leyuan Shi
  • Liang Pi
  • Peter Sun

Abstract

The Local Pickup and Delivery Problem (LPDP) has drawn much attention, and optimization models and algorithms have been developed to address this problem. However, for real world applications, the large-scale and dynamic nature of the problem causes difficulties in getting good solutions within acceptable time through standard optimization approaches. Meanwhile, actual dispatching solutions made by field experts in transportation companies contain embedded dispatching rules. This paper introduces a Data Mining-based Dispatching System (DMDS) to first learn dispatching rules from historical data and then generate dispatch solutions, which are shown to be as good as those generated by expert dispatchers in the intermodal freight industry. Three additional benefits of DMDS are: (1) it provides a simulation platform for strategic decision making and analysis; (2) the learned dispatching rules are valuable to combine with an optimization algorithm to improve the solution quality for LPDPs; (3) by adding optimized solutions to the training data, DMDS is capable to generate better-than-actuals solutions very quickly. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Weiwei Chen & Jie Song & Leyuan Shi & Liang Pi & Peter Sun, 2013. "Data mining-based dispatching system for solving the local pickup and delivery problem," Annals of Operations Research, Springer, vol. 203(1), pages 351-370, March.
    • Handle: RePEc:spr:annopr:v:203:y:2013:i:1:p:351-370:10.1007/s10479-012-1118-1
    DOI: 10.1007/s10479-012-1118-1
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    References listed on IDEAS

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    1. Tao Wu & Zhe Liang & Canrong Zhang, 2018. "Analytics Branching and Selection for the Capacitated Multi-Item Lot Sizing Problem with Nonidentical Machines," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 236-258, May.
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    4. Hongyan Dai & Peng Liu, 2020. "Workforce planning for O2O delivery systems with crowdsourced drivers," Annals of Operations Research, Springer, vol. 291(1), pages 219-245, August.

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