Abstract
This paper proposes a framework for generating relevant sets of instances for Green-Vehicle Routing Problems (G-VRP). In the G-VRP, electric vehicles with limited autonomy can recharge at Alternative Fuel Stations (AFSs) to keep visiting customers. To the best of our knowledge the G-VRP scientific literature accounts with only two sets of instances. Our instance generation framework is based on solving a maximum leaf spanning tree problem to address the location of AFSs, and it guarantees that the generated instances are feasible (as opposed to the procedure previously proposed). Two G-VRP variants are considered, (i) where consecutive AFSs visits are not allowed, and (ii) where consecutive AFSs visits are allowed. The results are analyzed and discussed, and conclusions on the benefits of the contributions are presented.
Supported by FAPESP (proc. 2015/11937-9, and 2018/25950-5), and CNPq (proc. 134616/2018-9, 314384/2018-9, and 435520/2018-0).
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Andrade, M.D., Usberti, F.L. (2021). Instance Generation Framework for Green Vehicle Routing. In: Masone, A., Dal Sasso, V., Morandi, V. (eds) Optimization and Data Science: Trends and Applications. AIRO Springer Series, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-86286-2_6
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