A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations

Claudia Bongiovanni, Mor Kaspi, Jean-François Cordeau and Nikolas Geroliminis

Transportation Research Part E: Logistics and Transportation Review, 2022, vol. 165, issue C

Abstract: This paper contributes to the intersection of operations research and machine learning in the context of autonomous ridesharing. In this work, autonomous ridesharing operations are reproduced through an event-based simulation approach and are modeled as a sequence of static subproblems to be optimized. The optimization framework consists of a novel data-driven metaheuristic within a two phase approach. The first phase consists of a greedy insertion heuristic that assigns new online requests to vehicles. The second phase consists of a local-search based metaheuristic that iteratively revisits previously-made vehicle-trip assignments through intra- and inter-vehicle route exchanges. These exchanges are performed by selecting from a pool of destroy–repair operators using a machine learning approach that is trained offline on a large dataset composed of more than one and a half million examples of previously-solved autonomous ridesharing subproblems.

Keywords: Dial-a-ride problem; Electric autonomous vehicles; Online optimization; Large neighborhood search; Metaheuristics; Machine learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.tre.2022.102835

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