{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:46:31Z","timestamp":1773819991229,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"43","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>The Profiled Vehicle Routing Problem (PVRP) extends the classical VRP by incorporating vehicle\u2013client-specific preferences and constraints, reflecting real\u2011world requirements such as zone restrictions and service\u2011level preferences. While recent reinforcement\u2011learning solvers have shown promising performance, they require retraining for each new profile distribution, suffer from poor representation ability, and struggle to generalize to out\u2011of\u2011distribution instances. In this paper, we address these limitations by introducing Unified Solver for Profiled Routing (USPR), a novel framework that natively handles arbitrary profile types. USPR introduces on three key innovations: (i) Profile Embeddings (PE) to encode any combination of profile types; (ii) Multi\u2011Head Profiled Attention (MHPA), an attention mechanism that models rich interactions between vehicles and clients;  (iii) Profile\u2011aware Score Reshaping (PSR), which dynamically adjusts decoder logits using profile scores to improve generalization. Empirical results on diverse PVRP benchmarks demonstrate that USPR achieves state\u2011of\u2011the\u2011art results among learning\u2011based methods while offering significant gains in flexibility and computational efficiency. We make our source code publicly available to foster future research.<\/jats:p>","DOI":"10.1609\/aaai.v40i43.41025","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:43:56Z","timestamp":1773816236000},"page":"36973-36981","source":"Crossref","is-referenced-by-count":0,"title":["USPR: Learning a Unified Solver for Profiled Routing"],"prefix":"10.1609","volume":"40","author":[{"given":"Chuanbo","family":"Hua","sequence":"first","affiliation":[]},{"given":"Federico","family":"Berto","sequence":"additional","affiliation":[]},{"given":"Zhikai","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jiwoo","family":"Son","sequence":"additional","affiliation":[]},{"given":"Changhyun","family":"Kwon","sequence":"additional","affiliation":[]},{"given":"Jinkyoo","family":"Park","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/41025\/44986","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/41025\/44986","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:43:56Z","timestamp":1773816236000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/41025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"43","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i43.41025","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}