{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T21:16:02Z","timestamp":1783113362429,"version":"3.54.6"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62406293"],"award-info":[{"award-number":["62406293"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013066","name":"Key Scientific Research Project of Colleges and Universities in Henan Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013066","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013142","name":"Key Research and Development Project of Hainan Province","doi-asserted-by":"publisher","award":["231111211900"],"award-info":[{"award-number":["231111211900"]}],"id":[{"id":"10.13039\/501100013142","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.114925","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T12:05:38Z","timestamp":1776945938000},"page":"114925","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Deep reinforcement learning driven by offset-attention mechanism for intelligent manufacturing cloud service composition and optimal selection"],"prefix":"10.1016","volume":"177","author":[{"given":"Shuxiao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxuan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jizhe","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6890-0492","authenticated-orcid":false,"given":"Cuixia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114925_b1","unstructured":"Baker, B., Kanitscheider, I., Markov, T., Wu, Y., Powell, G., McGrew, B., Mordatch, I., 2019. Emergent tool use from multi-agent autocurricula. In: Proceedings of the 8th International Conference on Learning Representations."},{"issue":"1","key":"10.1016\/j.engappai.2026.114925_b2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s00170-015-7350-5","article-title":"A TQCS-based service selection and scheduling strategy in cloud manufacturing","volume":"82","author":"Cao","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.engappai.2026.114925_b3","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.cie.2015.12.018","article-title":"A flexible QoS-aware web service composition method by multi-objective optimization in cloud manufacturing","volume":"99","author":"Chen","year":"2016","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.engappai.2026.114925_b4","first-page":"6281","article-title":"Learning to perform local rewriting for combinatorial optimization","volume":"32","author":"Chen","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114925_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2023.109053","article-title":"Cloud\u2013edge collaboration task scheduling in cloud manufacturing: An attention-based deep reinforcement learning approach","volume":"177","author":"Chen","year":"2023","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.engappai.2026.114925_b6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rcim.2016.05.007","article-title":"Modeling of manufacturing service supply\u2013demand matching hypernetwork in service-oriented manufacturing systems","volume":"45","author":"Cheng","year":"2017","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.engappai.2026.114925_b7","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.asoc.2015.11.012","article-title":"Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition","volume":"39","author":"Cremene","year":"2016","journal-title":"Appl. Soft Comput."},{"issue":"11","key":"10.1016\/j.engappai.2026.114925_b8","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.5654","article-title":"Task scheduling based on deep reinforcement learning in a cloud manufacturing environment","volume":"32","author":"Dong","year":"2020","journal-title":"Concurr. Comput.: Pr. Exp."},{"key":"10.1016\/j.engappai.2026.114925_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109530","article-title":"Bi-objective service composition and optimal selection for cloud manufacturing with QoS and robustness criteria","volume":"128","author":"Gao","year":"2022","journal-title":"Appl. Soft Comput."},{"issue":"8","key":"10.1016\/j.engappai.2026.114925_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksuci.2024.102177","article-title":"EETS: An energy-efficient task scheduler in cloud computing based on improved DQN algorithm","volume":"36","author":"Hou","year":"2024","journal-title":"J. King Saud Univ. - Comput. Inf. Sci."},{"key":"10.1016\/j.engappai.2026.114925_b11","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.jmsy.2022.05.008","article-title":"Tackling temporal-dynamic service composition in cloud manufacturing systems: a tensor factorization-based two-stage approach","volume":"63","author":"Hu","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114925_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.108902","article-title":"A variable-length encoding genetic algorithm for incremental service composition in uncertain environments for cloud manufacturing","volume":"123","author":"Jiang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.114925_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.108053","article-title":"Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition","volume":"114","author":"Jin","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.114925_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2024.102814","article-title":"Cloud-edge collaboration composition and scheduling for flexible manufacturing service with a multi-population co-evolutionary algorithm","volume":"90","author":"Jing","year":"2024","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.engappai.2026.114925_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123970","article-title":"Deep reinforcement learning for dynamic distributed job shop scheduling problem with transfers","volume":"251","author":"Lei","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114925_b16","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.jclepro.2019.06.265","article-title":"Makerchain: A blockchain with chemical signature for self-organizing process in social manufacturing","volume":"234","author":"Leng","year":"2019","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.engappai.2026.114925_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103179","article-title":"Federated learning-empowered smart manufacturing and product lifecycle management: A review","volume":"65","author":"Leng","year":"2025","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.engappai.2026.114925_b18","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.jmsy.2025.07.011","article-title":"Diffusion model-driven smart design and manufacturing: Prospects and challenges","volume":"82","author":"Leng","year":"2025","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114925_b19","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.jmsy.2025.02.006","article-title":"Resilient manufacturing: A review of disruptions, assessment, and pathways","volume":"79","author":"Leng","year":"2025","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114925_b20","series-title":"Review of cloud service composition for intelligent manufacturing","author":"Li","year":"2024"},{"issue":"7","key":"10.1016\/j.engappai.2026.114925_b21","doi-asserted-by":"crossref","first-page":"2386","DOI":"10.1109\/TSMC.2018.2814686","article-title":"QoS-aware service composition in cloud manufacturing: A gale\u2013Shapley algorithm-based approach","volume":"50","author":"Li","year":"2018","journal-title":"IEEE Trans. Syst. Man, Cybern.: Syst."},{"key":"10.1016\/j.engappai.2026.114925_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2020.101991","article-title":"Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning","volume":"67","author":"Liang","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.engappai.2026.114925_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2022.108006","article-title":"A three-tier programming model for service composition and optimal selection in cloud manufacturing","volume":"167","author":"Lim","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.engappai.2026.114925_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2023.109128","article-title":"A similarity-enhanced hybrid group recommendation approach in cloud manufacturing systems","volume":"178","author":"Liu","year":"2023","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.engappai.2026.114925_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2022.102323","article-title":"Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach","volume":"76","author":"Liu","year":"2022","journal-title":"Robot. Comput.-Integr. Manuf."},{"issue":"15\u201316","key":"10.1016\/j.engappai.2026.114925_b26","doi-asserted-by":"crossref","first-page":"4854","DOI":"10.1080\/00207543.2018.1449978","article-title":"Scheduling in cloud manufacturing: state-of-the-art and research challenges","volume":"57","author":"Liu","year":"2019","journal-title":"Int. J. Prod. Res."},{"issue":"9","key":"10.1016\/j.engappai.2026.114925_b27","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1007\/s00170-016-8992-7","article-title":"QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups","volume":"88","author":"Liu","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.engappai.2026.114925_b28","doi-asserted-by":"crossref","unstructured":"Mao, H., Alizadeh, M., Menache, I., Kandula, S., 2016. Resource management with deep reinforcement learning. In: Proceedings of the 15th ACM Workshop on Hot Topics in Networks. pp. 50\u201356.","DOI":"10.1145\/3005745.3005750"},{"issue":"9","key":"10.1016\/j.engappai.2026.114925_b29","doi-asserted-by":"crossref","first-page":"4455","DOI":"10.1007\/s00170-018-1925-x","article-title":"Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing","volume":"96","author":"Que","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1","key":"10.1016\/j.engappai.2026.114925_b30","first-page":"9","article-title":"A generalization of the paired t-test","volume":"31","author":"Rosner","year":"1982","journal-title":"J. R. Stat. Soc. Ser. C. Appl. Stat."},{"key":"10.1016\/j.engappai.2026.114925_b31","article-title":"Job shop smart manufacturing scheduling by deep reinforcement learning","volume":"38","author":"Serrano-Ruiz","year":"2024","journal-title":"J. Ind. Inf. Integr."},{"key":"10.1016\/j.engappai.2026.114925_b32","series-title":"V-mpo: On-policy maximum a posteriori policy optimization for discrete and continuous control","author":"Song","year":"2019"},{"key":"10.1016\/j.engappai.2026.114925_b33","doi-asserted-by":"crossref","first-page":"42568","DOI":"10.1109\/ACCESS.2021.3062457","article-title":"A novel hierarchical soft actor-critic algorithm for multi-logistics robots task allocation","volume":"9","author":"Tang","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114925_b34","article-title":"A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization","author":"Tang","year":"2024","journal-title":"J. Global Optim."},{"issue":"4","key":"10.1016\/j.engappai.2026.114925_b35","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1109\/TII.2012.2232936","article-title":"FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system","volume":"9","author":"Tao","year":"2012","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"10.1016\/j.engappai.2026.114925_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2020.101933","article-title":"A two-layer social network model for manufacturing service composition based on synergy: A case study on an aircraft structural part","volume":"65","author":"Tong","year":"2020","journal-title":"Robot. Comput.-Integr. Manuf."},{"issue":"7782","key":"10.1016\/j.engappai.2026.114925_b37","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1038\/s41586-019-1724-z","article-title":"Grandmaster level in StarCraft II using multi-agent reinforcement learning","volume":"575","author":"Vinyals","year":"2019","journal-title":"Nature"},{"issue":"14","key":"10.1016\/j.engappai.2026.114925_b38","doi-asserted-by":"crossref","first-page":"6256","DOI":"10.3390\/su16146256","article-title":"Selecting resilient strategies for cost optimization in prefabricated building supply chains based on the non-dominated sorting genetic algorithm-2161: Facing diverse disruption scenarios","volume":"16","author":"Wang","year":"2024","journal-title":"Sustainability"},{"key":"10.1016\/j.engappai.2026.114925_b39","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.jmsy.2022.08.013","article-title":"Solving task scheduling problems in cloud manufacturing via attention mechanism and deep reinforcement learning","volume":"65","author":"Wang","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114925_b40","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1016\/j.procir.2018.03.212","article-title":"Optimization of global production scheduling with deep reinforcement learning","volume":"72","author":"Waschneck","year":"2018","journal-title":"Procedia CIRP"},{"key":"10.1016\/j.engappai.2026.114925_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110940","article-title":"A DRL-based RAQ-GERT dynamic resource allocation algorithm considering utility for multibeam satellite system","volume":"257","author":"Wu","year":"2025","journal-title":"Comput. Netw."},{"key":"10.1016\/j.engappai.2026.114925_b42","article-title":"Human-centric artificial intelligence towards industry 5.0: retrospect and prospect","author":"Yan","year":"2025","journal-title":"J. Ind. Inf. Integr."},{"issue":"6","key":"10.1016\/j.engappai.2026.114925_b43","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1080\/0951192X.2019.1575982","article-title":"A service satisfaction-based trust evaluation model for cloud manufacturing","volume":"32","author":"Yang","year":"2019","journal-title":"Int. J. Comput. Integr. Manuf."},{"issue":"16","key":"10.1016\/j.engappai.2026.114925_b44","doi-asserted-by":"crossref","first-page":"4936","DOI":"10.1080\/00207543.2021.1943037","article-title":"Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing","volume":"60","author":"Yang","year":"2022","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.engappai.2026.114925_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2024.101768","article-title":"MFWOA: Multifactorial whale optimization algorithm","volume":"91","author":"Ye","year":"2024","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.engappai.2026.114925_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2019.101840","article-title":"Service composition model and method in cloud manufacturing","volume":"61","author":"Yuan","year":"2020","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.engappai.2026.114925_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.robot.2024.104678","article-title":"A novel hybrid swarm intelligence algorithm for solving TSP and desired-path-based online obstacle avoidance strategy for AUV","volume":"177","author":"Zhang","year":"2024","journal-title":"Robot. Auton. Syst."},{"key":"10.1016\/j.engappai.2026.114925_b48","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.jmsy.2017.11.008","article-title":"An augmented Lagrangian coordination method for optimal allocation of cloud manufacturing services","volume":"48","author":"Zhang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114925_b49","series-title":"Deep reinforcement learning for list-wise recommendations","author":"Zhao","year":"2018"},{"key":"10.1016\/j.engappai.2026.114925_b50","article-title":"Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing","volume":"226\u2013227","author":"Zhou","year":"2024","journal-title":"Comput. Commun."},{"issue":"9","key":"10.1016\/j.engappai.2026.114925_b51","doi-asserted-by":"crossref","first-page":"3515","DOI":"10.1007\/s00170-017-0008-8","article-title":"Hybrid teaching\u2013learning-based optimization of correlation-aware service composition in cloud manufacturing","volume":"91","author":"Zhou","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.engappai.2026.114925_b52","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.procir.2020.05.163","article-title":"Deep reinforcement learning-based dynamic scheduling in smart manufacturing","volume":"93","author":"Zhou","year":"2020","journal-title":"Procedia CIRP"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626012078?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626012078?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:18:09Z","timestamp":1783109889000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626012078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":52,"alternative-id":["S0952197626012078"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114925","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep reinforcement learning driven by offset-attention mechanism for intelligent manufacturing cloud service composition and optimal selection","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114925","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114925"}}