Abstract
We look at multi-depot pickup and dropoff problems (MDPDPs) where a fleet of vehicles services commuting requests. We thus investigate minimising the fleet total/maximum travel, waiting, tour, and arrival times. For these objectives, we give a template that implements genetic algorithms (GAs) and evaluate their performance on new instances. The results indicate that there is a trade-off between minimising the waiting times and minimising the tour times, and minimising the arrival times lies somehow in the middle of it. Also, we measure how often commuters share rides, i.e. the sharing rate. For example, minimising the waiting times achieves the greatest sharing rate but the longest vehicle travel time compared to minimising any of the other times. Finally, the GAs reduce the number of running vehicles.
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Aleksandrov, M.D. (2021). Minimising Fleet Times in Multi-depot Pickup and Dropoff Problems. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_16
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