Anticipatory Vehicle Routing for Same-Day Pick-up and Delivery using Historical Data Clustering

Jelmer van Lochem, Maximilian Kronmueller, Pim van 't Hof, Javier Alonso-Mora

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


In this paper we address the problem of same-day pick-up and delivery where a set of tasks are known a priori and a set of tasks are revealed during operation. The vehicle routes are precomputed based on the known and predicted requests and adjusted online as new requests are revealed. We propose a novel anticipatory insertion method which incorporates a set of predicted requests to beneficially adjust the routes of a fleet of vehicles in real-time. Requests are predicted based on historical data, which is clustered in advance. We exploit inherent patterns of the demand, which are captured by historical data and include them in a dynamic vehicle routing solver based on heuristics and adaptive large neighborhood search. The proposed method is evaluated using numerical simulations on a variety of real-world problems with up to 1655 requests per day. Their degree of dynamism ranges from 0.70 to 0.93. These instances represent dynamic multi-depot pickup and delivery problems with time windows. The method has shown to require less driven kilometers than comparable methods.
Original languageEnglish
Title of host publication2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Publication statusPublished - 2020
Externally publishedYes


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