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.
|Title of host publication||Proceedings of the IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020|
|Place of Publication||Piscataway, NJ, USA|
|Number of pages||6|
|Publication status||Published - 2020|
|Event||23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece|
Duration: 20 Sep 2020 → 23 Sep 2020
|Conference||23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020|
|Period||20/09/20 → 23/09/20|
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