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This feature allows us to model the fleet operation from a non-cooperative point of view. In this work, we study parcel delivery in a city with limited resources (roads and charging stations). We designed and implemented a system composed of a multi-agent planner and a game-theoretic coordination algorithm: a Best-Response Fleet Planner. The system allows for the self-organization of the transportation system by coordinating a fleet of self-interested electric vehicles. The system\u2019s operation is optimized together with resource usage while preserving the agents\u2019 private interests, allowing each agent to plan its actions. The results show that our system has higher scalability than similar approaches, allowing it to function for a considerable number of agents in settings that feature congestion and conflicts. Additionally, overall solution quality is improved compared to other coordination systems, reducing congestion and avoiding unnecessary waiting times.<\/jats:p>","DOI":"10.1007\/s00521-023-08631-9","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T17:41:03Z","timestamp":1684258863000},"page":"17599-17618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Best-response planning for urban fleet coordination"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-0641","authenticated-orcid":false,"given":"Pasqual","family":"Mart\u00ed","sequence":"first","affiliation":[]},{"given":"Jaume","family":"Jord\u00e1n","sequence":"additional","affiliation":[]},{"given":"Vicente","family":"Julian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"issue":"6615","key":"8631_CR1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.abo4940","volume":"378","author":"Z Rao","year":"2022","unstructured":"Rao Z et al (2022) Machine learning enabled high-entropy alloy discovery. 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