{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:43:05Z","timestamp":1773510185936,"version":"3.50.1"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T00:00:00Z","timestamp":1548374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2019,1,25]]},"abstract":"<jats:p>With the emergence of Electrical Vehicles (EVs), there is a growing investment in power infrastructure to provide charging stations. In an EV parking lot, typically not all vehicles can be charged simultaneously, and thus some scheduling must be performed, taking into account the time the users are willing to spend in the system.<\/jats:p>\n          <jats:p>In this paper, we analyze the performance of several common scheduling policies through a fluid model. We show that in overload, the amount of unfinished work is the same for all policies, but these can distribute the work performed unfairly across users. We also introduce a new policy called Least Laxity Ratio that achieves a suitable notion of fairness across jobs, and validate its performance by simulation.<\/jats:p>","DOI":"10.1145\/3308897.3308927","type":"journal-article","created":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T14:01:39Z","timestamp":1548684099000},"page":"62-67","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Achieving fairness for EV charging in overload"],"prefix":"10.1145","volume":"46","author":[{"given":"Martin","family":"Zeballos","sequence":"first","affiliation":[{"name":"Universidad ORT Uruguay, Montevideo, Uruguay"}]},{"given":"Andres","family":"Ferragut","sequence":"additional","affiliation":[{"name":"Universidad ORT Uruguay, Montevideo, Uruguay"}]},{"given":"Fernando","family":"Paganini","sequence":"additional","affiliation":[{"name":"Universidad ORT Uruguay, Montevideo, Uruguay"}]}],"member":"320","published-online":{"date-parts":[[2019,1,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2014.0690"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3152042.3152054"},{"key":"e_1_2_1_3_1","volume-title":"A stochastic resource-sharing network for electric vehicle charging. arXiv preprint arXiv:1711.05561","author":"Aveklouris A.","year":"2017","unstructured":"A. Aveklouris , M. Vlasiou , and B. Zwart . A stochastic resource-sharing network for electric vehicle charging. arXiv preprint arXiv:1711.05561 , 2017 . A. Aveklouris, M. Vlasiou, and B. Zwart. A stochastic resource-sharing network for electric vehicle charging. arXiv preprint arXiv:1711.05561, 2017."},{"key":"e_1_2_1_4_1","first-page":"9","volume-title":"Power and Energy Society General Meeting","author":"Chen S.","year":"2012","unstructured":"S. Chen , Y. Ji , and L. Tong . Large scale charging of electric vehicles . In Power and Energy Society General Meeting , 2012 IEEE, pages 1{ 9 . IEEE, 2012. S. Chen, Y. Ji, and L. Tong. Large scale charging of electric vehicles. In Power and Energy Society General Meeting, 2012 IEEE, pages 1{9. IEEE, 2012."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoap\/1015345295"},{"key":"e_1_2_1_6_1","volume-title":"AMS","author":"Evans L.","year":"1998","unstructured":"L. Evans . Partial Di erential Equations . AMS , 1998 . L. 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Ramanan , and S. Shreve . Heavy traffic analysis for edf queues with reneging . The Annals of App. Prob. , 21 ( 2 ):484{ 545 , 2011 . L. Kruk, J. Lehoczky, K. Ramanan, and S. Shreve. Heavy traffic analysis for edf queues with reneging. The Annals of App. Prob., 21(2):484{545, 2011.","journal-title":"The Annals of App. Prob."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077839.3077864"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/48014.48019"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.11.042"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2011.2151888"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2011.2172454"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMWRT.1990.128221"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2016.2541305"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2015.7447030"}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3308897.3308927","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3308897.3308927","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:58:03Z","timestamp":1750208283000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3308897.3308927"}},"subtitle":["a fluid approach"],"short-title":[],"issued":{"date-parts":[[2019,1,25]]},"references-count":17,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,1,25]]}},"alternative-id":["10.1145\/3308897.3308927"],"URL":"https:\/\/doi.org\/10.1145\/3308897.3308927","relation":{},"ISSN":["0163-5999"],"issn-type":[{"value":"0163-5999","type":"print"}],"subject":[],"published":{"date-parts":[[2019,1,25]]},"assertion":[{"value":"2019-01-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}