{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T03:46:53Z","timestamp":1776570413456,"version":"3.51.2"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100017599","name":"Science and Technology Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2025C1023"],"award-info":[{"award-number":["2025C1023"]}],"id":[{"id":"10.13039\/501100017599","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104689","type":"journal-article","created":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T00:58:49Z","timestamp":1776560329000},"page":"104689","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["Embodied multi-agent scheduling for workshop operations with partial global plans"],"prefix":"10.1016","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3141-7509","authenticated-orcid":false,"given":"Wenda","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ge","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Yiping","family":"Feng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104689_b1","series-title":"Scheduling","author":"Pinedo","year":"2012"},{"issue":"17","key":"10.1016\/j.aei.2026.104689_b2","doi-asserted-by":"crossref","first-page":"5860","DOI":"10.1080\/00207543.2022.2118387","article-title":"A three-step approach for decision support in operational production planning of complex manufacturing systems","volume":"61","author":"Christ","year":"2023","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2026.104689_b3","first-page":"1","article-title":"Smart scheduling for next generation manufacturing systems: A systematic literature review","author":"Chorghe","year":"2024","journal-title":"J. Intell. Manuf."},{"issue":"1\u20132","key":"10.1016\/j.aei.2026.104689_b4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/00207543.2023.2288722","article-title":"Production planning with flexible manufacturing systems under demand uncertainty","volume":"62","author":"Elyasi","year":"2024","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2026.104689_b5","series-title":"2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication","first-page":"203","article-title":"Optimal integration of metaheuristic techniques with correction-based approaches for profit maximization in combinatorial production planning: A comparative analysis","author":"Mukherjee","year":"2024"},{"issue":"1\u20133","key":"10.1016\/j.aei.2026.104689_b6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/0166-218X(94)90204-6","article-title":"A branch and bound algorithm for the job-shop scheduling problem","volume":"49","author":"Brucker","year":"1994","journal-title":"Discrete Appl. Math."},{"issue":"4","key":"10.1016\/j.aei.2026.104689_b7","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1109\/JAS.2019.1911540","article-title":"A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems","volume":"6","author":"Gao","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"issue":"14","key":"10.1016\/j.aei.2026.104689_b8","doi-asserted-by":"crossref","first-page":"7684","DOI":"10.3390\/su13147684","article-title":"A critical analysis of job shop scheduling in context of industry 4.0","volume":"13","author":"Liaqait","year":"2021","journal-title":"Sustainability"},{"issue":"11","key":"10.1016\/j.aei.2026.104689_b9","doi-asserted-by":"crossref","first-page":"3285","DOI":"10.1080\/00207543.2020.1859634","article-title":"Order release planning with predictive lead times: A machine learning approach","volume":"59","author":"Schneckenreither","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2026.104689_b10","doi-asserted-by":"crossref","first-page":"74977","DOI":"10.1109\/ACCESS.2022.3191426","article-title":"Scheduling under uncertainty for industry 4.0 and 5.0","volume":"10","author":"Bakon","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104689_b11","series-title":"International Conference on Production Research","first-page":"461","article-title":"Industrial rescheduling approaches: Where are we and what is missing?","author":"Espinaco","year":"2022"},{"issue":"3","key":"10.1016\/j.aei.2026.104689_b12","doi-asserted-by":"crossref","first-page":"129","DOI":"10.2507\/IJSIMM11(3)2.201","article-title":"Comparison of dispatching rules in job-shop scheduling problem using simulation: A case study","volume":"11","author":"Kaban","year":"2012","journal-title":"Int. J. Simul. Model."},{"issue":"5","key":"10.1016\/j.aei.2026.104689_b13","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1080\/07408170008963918","article-title":"Robust scheduling of a two-machine flow shop with uncertain processing times","volume":"32","author":"Kouvelis","year":"2000","journal-title":"Iie Trans."},{"issue":"16","key":"10.1016\/j.aei.2026.104689_b14","doi-asserted-by":"crossref","first-page":"5772","DOI":"10.1080\/00207543.2022.2104180","article-title":"Reinforcement learning applied to production planning and control","volume":"61","author":"Esteso","year":"2023","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2026.104689_b15","first-page":"1621","article-title":"Learning to dispatch for job shop scheduling via deep reinforcement learning","volume":"33","author":"Zhang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.aei.2026.104689_b16","article-title":"Embodied intelligence toward future smart manufacturing in the era of AI foundation model","author":"Ren","year":"2024","journal-title":"IEEE\/ASME Trans. Mechatronics"},{"key":"10.1016\/j.aei.2026.104689_b17","unstructured":"M. Ziaee, A new mixed integer linear programming model for flexible job shop scheduling problem, in: Proceedings of the International Conference on Industrial Engineering and Operations Management, SEP, 2018, pp. 2079\u20132087."},{"key":"10.1016\/j.aei.2026.104689_b18","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10845-007-0026-8","article-title":"Mathematical modeling and heuristic approaches to flexible job shop scheduling problems","volume":"18","author":"Fattahi","year":"2007","journal-title":"J. Intell. Manuf."},{"issue":"7","key":"10.1016\/j.aei.2026.104689_b19","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1007\/s10845-020-01537-6","article-title":"Transfer-robot task scheduling in flexible job shop","volume":"31","author":"Ham","year":"2020","journal-title":"J. Intell. Manuf."},{"issue":"6","key":"10.1016\/j.aei.2026.104689_b20","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1287\/opre.43.6.1058","article-title":"A time window approach to simultaneous scheduling of machines and material handling system in an FMS","volume":"43","author":"Bilge","year":"1995","journal-title":"Oper. Res."},{"issue":"3","key":"10.1016\/j.aei.2026.104689_b21","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1016\/j.ejor.2023.07.036","article-title":"Flexible job-shop scheduling with transportation resources","volume":"312","author":"Berterotti\u00e8re","year":"2024","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.aei.2026.104689_b22","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.compchemeng.2014.02.023","article-title":"Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method","volume":"72","author":"Chu","year":"2015","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.aei.2026.104689_b23","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s10951-019-00627-5","article-title":"Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant","volume":"23","author":"Stevenson","year":"2020","journal-title":"J. Sched."},{"issue":"1","key":"10.1016\/j.aei.2026.104689_b24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.jmsy.2013.03.004","article-title":"Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times","volume":"33","author":"Rahmani","year":"2014","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.aei.2026.104689_b25","article-title":"Exploring the benefits of scheduling with advanced and real-time information integration in industry 4.0: A computational study","volume":"27","author":"Fernandez-Viagas","year":"2022","journal-title":"J. Ind. Inf. Integr."},{"key":"10.1016\/j.aei.2026.104689_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2022.102435","article-title":"Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window","volume":"79","author":"Wang","year":"2023","journal-title":"Robot. Comput.-Integr. Manuf."},{"issue":"2","key":"10.1016\/j.aei.2026.104689_b27","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.ejor.2024.02.006","article-title":"Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0","volume":"316","author":"Didden","year":"2024","journal-title":"European J. Oper. Res."},{"issue":"2","key":"10.1016\/j.aei.2026.104689_b28","first-page":"186","article-title":"Real-time production scheduling using a deep reinforcement learning-based multi-agent approach","volume":"62","author":"Taghipour","year":"2024","journal-title":"INFOR Inf. Syst. Oper. Res."},{"issue":"3","key":"10.1016\/j.aei.2026.104689_b29","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/09537287.2022.2076627","article-title":"A conceptual framework of the applicability of production scheduling from a contingency theory approach: Addressing the theory-practice gap","volume":"35","author":"Romero-Silva","year":"2024","journal-title":"Prod. Plan. Control"},{"key":"10.1016\/j.aei.2026.104689_b30","article-title":"Collaborative multiobjective decisions for cyber\u2013physical production systems under time-varying demands","author":"Liu","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.aei.2026.104689_b31","doi-asserted-by":"crossref","DOI":"10.1109\/TII.2025.3549789","article-title":"Replanning-oriented framework for efficient real-time decision-making in multi-UAV systems","author":"Hai","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.aei.2026.104689_b32","doi-asserted-by":"crossref","DOI":"10.1109\/TIE.2025.3561859","article-title":"Resilient real-time decision-making for autonomous mobile robot path planning in complex dynamic environments","author":"Hai","year":"2025","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.aei.2026.104689_b33","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.cie.2019.01.036","article-title":"Using real-time information to reschedule jobs in a flowshop with variable processing times","volume":"129","author":"Framinan","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.aei.2026.104689_b34","series-title":"2024 10th International Conference on Control, Decision and Information Technologies","first-page":"2470","article-title":"Reactive real-time scheduling using simulation-optimization and evolutionary algorithms","author":"Pasieka","year":"2024"},{"issue":"4","key":"10.1016\/j.aei.2026.104689_b35","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/TNNLS.2022.3208942","article-title":"A reinforcement learning approach for flexible job shop scheduling problem with crane transportation and setup times","volume":"35","author":"Du","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"10.1016\/j.aei.2026.104689_b36","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1109\/TII.2022.3189725","article-title":"Flexible job-shop scheduling via graph neural network and deep reinforcement learning","volume":"19","author":"Song","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"10.1016\/j.aei.2026.104689_b37","doi-asserted-by":"crossref","first-page":"7684","DOI":"10.1109\/LRA.2022.3184795","article-title":"Multi-agent reinforcement learning for real-time dynamic production scheduling in a robot assembly cell","volume":"7","author":"Johnson","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.aei.2026.104689_b38","first-page":"1","article-title":"Design and calibration of a DRL algorithm for solving the job shop scheduling problem under unexpected job arrivals","author":"Hammami","year":"2024","journal-title":"Flex. Serv. Manuf. J."},{"issue":"13","key":"10.1016\/j.aei.2026.104689_b39","doi-asserted-by":"crossref","first-page":"4419","DOI":"10.1080\/00207543.2022.2142314","article-title":"Reinforcement learning-based dynamic production-logistics-integrated tasks allocation in smart factories","volume":"61","author":"Lei","year":"2023","journal-title":"Int. J. Prod. Res."},{"key":"10.1016\/j.aei.2026.104689_b40","first-page":"1","article-title":"Hierarchical multi-agent deep reinforcement learning for dynamic flexible job-shop scheduling with transportation","author":"Wang","year":"2025","journal-title":"Int. J. Prod. Res."},{"issue":"6","key":"10.1016\/j.aei.2026.104689_b41","doi-asserted-by":"crossref","first-page":"e1013180","DOI":"10.1371\/journal.pcbi.1013180","article-title":"Embodied decisions as active inference","volume":"21","author":"Priorelli","year":"2025","journal-title":"PLoS Computational Biology"},{"issue":"1","key":"10.1016\/j.aei.2026.104689_b42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSMCC.2002.1009117","article-title":"Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems","volume":"32","author":"Kacem","year":"2002","journal-title":"IEEE Trans. Syst. Man, Cybern. C (Appl. Rev.)"},{"issue":"2","key":"10.1016\/j.aei.2026.104689_b43","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1111\/j.1468-0394.2005.00297.x","article-title":"Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions","volume":"22","author":"Wang","year":"2005","journal-title":"Expert Syst."},{"issue":"4","key":"10.1016\/j.aei.2026.104689_b44","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s10898-018-0681-7","article-title":"Joint production and transportation scheduling in flexible manufacturing systems","volume":"74","author":"Fontes","year":"2019","journal-title":"J. Global Optim."},{"key":"10.1016\/j.aei.2026.104689_b45","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.jmsy.2023.03.011","article-title":"A multi-agent system simulation based approach for collision avoidance in integrated job-shop scheduling problem with transportation tasks","volume":"68","author":"Sanogo","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.aei.2026.104689_b46","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.jmsy.2024.11.010","article-title":"Flexible robotic cell scheduling with graph neural network based deep reinforcement learning","volume":"78","author":"Wang","year":"2025","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.aei.2026.104689_b47","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.jmsy.2021.09.018","article-title":"Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance","volume":"61","author":"Ghaleb","year":"2021","journal-title":"J. Manuf. Syst."},{"issue":"4","key":"10.1016\/j.aei.2026.104689_b48","doi-asserted-by":"crossref","first-page":"4016","DOI":"10.1109\/TITS.2023.3234010","article-title":"Real-time charging scheduling of automated guided vehicles in cyber-physical smart factories using feature-based reinforcement learning","volume":"24","author":"Lin","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"24","key":"10.1016\/j.aei.2026.104689_b49","doi-asserted-by":"crossref","first-page":"7377","DOI":"10.1080\/00207543.2014.931609","article-title":"Integrated maintenance planning and production scheduling with Markovian deteriorating machine conditions","volume":"52","author":"Aramon Bajestani","year":"2014","journal-title":"Int. J. Prod. Res."},{"issue":"3","key":"10.1016\/j.aei.2026.104689_b50","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1057\/jors.2014.18","article-title":"Single machine scheduling with time-dependent linear deterioration and rate-modifying maintenance","volume":"66","author":"Rustogi","year":"2015","journal-title":"J. Oper. Res. Soc."},{"issue":"11","key":"10.1016\/j.aei.2026.104689_b51","doi-asserted-by":"crossref","first-page":"439","DOI":"10.3390\/a15110439","article-title":"Taxonomy of scheduling problems with learning and deterioration effects","volume":"15","author":"Paredes-Astudillo","year":"2022","journal-title":"Algorithms"},{"key":"10.1016\/j.aei.2026.104689_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.122773","article-title":"New rolling horizon optimization approaches to balance short-term and long-term decisions: An application to energy planning","volume":"245","author":"Cuisinier","year":"2022","journal-title":"Energy"},{"issue":"1","key":"10.1016\/j.aei.2026.104689_b53","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13676-012-0008-7","article-title":"Conflict-free vehicle routing: Load balancing and deadlock prevention","volume":"1","author":"Gawrilow","year":"2012","journal-title":"EURO J. Transp. Logist."},{"key":"10.1016\/j.aei.2026.104689_b54","series-title":"In-context reinforcement learning for variable action spaces","author":"Sinii","year":"2023"},{"key":"10.1016\/j.aei.2026.104689_b55","series-title":"International Conference on Machine Learning","first-page":"3053","article-title":"Rllib: Abstractions for distributed reinforcement learning","author":"Liang","year":"2018"},{"key":"10.1016\/j.aei.2026.104689_b56","first-page":"24611","article-title":"The surprising effectiveness of ppo in cooperative multi-agent games","volume":"35","author":"Yu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.aei.2026.104689_b57","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s10845-022-02037-5","article-title":"Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling","volume":"35","author":"Jing","year":"2024","journal-title":"J. Intell. Manuf."},{"issue":"1","key":"10.1016\/j.aei.2026.104689_b58","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s12065-024-00989-6","article-title":"Deep reinforcement learning-based spatio-temporal graph neural network for solving job shop scheduling problem","volume":"18","author":"Gebreyesus","year":"2025","journal-title":"Evol. Intell."},{"issue":"7","key":"10.1016\/j.aei.2026.104689_b59","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.3390\/math13071167","article-title":"Modeling and exploratory analysis of discrete event simulations for optimizing overhead hoist transport systems and logistics in semiconductor manufacturing","volume":"13","author":"Sung","year":"2025","journal-title":"Mathematics"},{"key":"10.1016\/j.aei.2026.104689_b60","series-title":"Proceedings of the 2004 Winter Simulation Conference, 2004.","first-page":"1962","article-title":"Capacity analysis of automated material handling systems in semiconductor fabs","volume":"vol. 2","author":"Kuhl","year":"2004"},{"key":"10.1016\/j.aei.2026.104689_b61","series-title":"2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation","first-page":"1","article-title":"Measurements of the safety function response time on a private 5G and IO-link wireless testbed","author":"Beuster","year":"2024"},{"key":"10.1016\/j.aei.2026.104689_b62","series-title":"Advances in Artificial Intelligence and Applied Cognitive Computing: Proceedings from ICAI\u201920 and ACC\u201920","first-page":"157","article-title":"Effects of domain randomization on simulation-to-reality transfer of reinforcement learning policies for industrial robots","author":"Scheiderer","year":"2021"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003812?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003812?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T03:30:44Z","timestamp":1776569444000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626003812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":62,"alternative-id":["S1474034626003812"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104689","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Embodied multi-agent scheduling for workshop operations with partial global plans","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104689","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104689"}}