Night makes you beautiful: an optimization approach to overnight joint beautification and relocation in e-scooter sharing
| Autor: | Carrese, Stefano, d’Andreagiovanni, Fabio, Giacchetti, Tommaso, Nardin, Antonella, Zamberlan, Leonardo |
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| Přispěvatelé: | Università degli Studi Roma Tre, Centre National de la Recherche Scientifique (CNRS), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | 3rd Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS2020) 3rd Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS2020), Jul 2020, Luxembourg, Luxembourg |
| Popis: | International audience; Electric scooter (e-scooter) sharing has recently known a wide success around the world, thanks to its ease of use and parking. However, it soon became apparent that many of its users tend to park without caring about road rules, abandoning e-scooters in locations and positions that compromise urban decorum and interfere with pedestrians. Many municipalities have thus taken actions, such as bans and fines, against e-scooter sharing companies. In this work, we address the problem of optimally managing the actions of a set of agents hired by a sharing company expressly for repositioning e-scooters to guarantee urban decorum. We call these agents beautificators, since their fundamental task is to reposition scooters over short distances (even just a few meters), so to fix inappropriate and disordered parking made by users. Specifically, we propose a new Integer Linear Programming model for representing the problem of jointly scheduling and choosing the actions operated overnight by beautificators and relocators (for fleet balancing) in a service area. We also propose a matheuristic for its solution (a genetic algorithm combined with exact optimization-based neighborhood searches). Computational tests on realistic instances show that our new optimization approach can return solutions of higher quality than a state-of-the-art solver. |
| Databáze: | OpenAIRE |
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