{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T17:43:42Z","timestamp":1776879822358,"version":"3.51.2"},"reference-count":22,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","funder":[{"name":"Anhui Center for Applied Mathematics, NSFC Major Research Plan-Interpretable and General Purpose Next-generation Artificial Intelligence","award":["92270001"],"award-info":[{"award-number":["92270001"]}]},{"name":"Anhui Center for Applied Mathematics, NSFC Major Research Plan-Interpretable and General Purpose Next-generation Artificial Intelligence","award":["92270205"],"award-info":[{"award-number":["92270205"]}]},{"name":"Major Project of Science & Technology of Anhui Province","award":["202203a05020050"],"award-info":[{"award-number":["202203a05020050"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2024,5,30]]},"abstract":"<jats:p> Considerable advances have been made recently in applying reinforcement learning (RL) to packing problems. However, most of these methods lack scalability and cannot be applied in dynamic environments. To address this research gap, we propose a hybrid algorithm called GraphPack to solve the strip packing problem. Two graph neural networks are designed to fully incorporate the problem\u2019s structure and enhance generalization performance. SkylineNet encodes the geometry of free space as the context feature, while PackNet, supporting the symmetry of rectangles, chooses the next rectangle to pack from the remaining rectangles at each timestep. We conduct fixed-scale, variable rectangle number and variable strip width experiments to test our method. The experimental results show that our method outperforms classical heuristic methods as well as previous RL methods. Notably, our method exhibits strong generalization ability and produces stable results even when the number of rectangles or strip width differs from that during training. <\/jats:p>","DOI":"10.1142\/s0218126624501391","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T08:32:47Z","timestamp":1698395567000},"source":"Crossref","is-referenced-by-count":2,"title":["GraphPack: A Reinforcement Learning Algorithm for Strip Packing Problem Using Graph Neural Network"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5869-1890","authenticated-orcid":false,"given":"Yang","family":"Xu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, No. 96 JinZhai Road, Hefei 230026, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9454-9146","authenticated-orcid":false,"given":"Zhouwang","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, No. 96 JinZhai Road, Hefei 230026, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"S0218126624501391BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2021.105521"},{"key":"S0218126624501391BIB002","doi-asserted-by":"publisher","DOI":"10.1137\/0209062"},{"key":"S0218126624501391BIB003","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.15.3.310.16082"},{"key":"S0218126624501391BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2008.08.020"},{"key":"S0218126624501391BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-015-1971-9"},{"key":"S0218126624501391BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.07.031"},{"key":"S0218126624501391BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2009.05.008"},{"key":"S0218126624501391BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3186891"},{"key":"S0218126624501391BIB009","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04226-6"},{"key":"S0218126624501391BIB010","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-0233-9_7"},{"key":"S0218126624501391BIB011","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-005-2368-2"},{"key":"S0218126624501391BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3188302"},{"key":"S0218126624501391BIB016","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417764"},{"key":"S0218126624501391BIB018","doi-asserted-by":"publisher","DOI":"10.1109\/RO-MAN46459.2019.8956393"},{"key":"S0218126624501391BIB020","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2181\/1\/012002"},{"key":"S0218126624501391BIB021","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3045905"},{"key":"S0218126624501391BIB022","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2021.10.032"},{"key":"S0218126624501391BIB025","first-page":"1024","volume-title":"Advances in Neural Information Processing Systems","author":"Hamilton W."},{"key":"S0218126624501391BIB026","first-page":"1","volume-title":"Int. Conf. Learning Representations","author":"Xu K.","year":"2018"},{"key":"S0218126624501391BIB028","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"S0218126624501391BIB029","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani A.","year":"2017"},{"key":"S0218126624501391BIB030","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"}],"container-title":["Journal of Circuits, Systems and Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218126624501391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T06:22:16Z","timestamp":1722320536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218126624501391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":22,"journal-issue":{"issue":"08","published-print":{"date-parts":[[2024,5,30]]}},"alternative-id":["10.1142\/S0218126624501391"],"URL":"https:\/\/doi.org\/10.1142\/s0218126624501391","relation":{},"ISSN":["0218-1266","1793-6454"],"issn-type":[{"value":"0218-1266","type":"print"},{"value":"1793-6454","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,23]]},"article-number":"2450139"}}