{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:21:03Z","timestamp":1783635663282,"version":"3.55.0"},"reference-count":43,"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":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["23BGL280"],"award-info":[{"award-number":["23BGL280"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010906","name":"NSAF Joint Fund","doi-asserted-by":"publisher","award":["51679126"],"award-info":[{"award-number":["51679126"]}],"id":[{"id":"10.13039\/501100010906","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010906","name":"NSAF Joint Fund","doi-asserted-by":"publisher","award":["11975311"],"award-info":[{"award-number":["11975311"]}],"id":[{"id":"10.13039\/501100010906","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010906","name":"NSAF Joint Fund","doi-asserted-by":"publisher","award":["51922065"],"award-info":[{"award-number":["51922065"]}],"id":[{"id":"10.13039\/501100010906","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013071","name":"Major Program of National Fund of Philosophy and Social Science of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013071","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005153","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"publisher","award":["52209146"],"award-info":[{"award-number":["52209146"]}],"id":[{"id":"10.13039\/501100005153","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.knosys.2026.116401","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T23:10:18Z","timestamp":1780787418000},"page":"116401","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ALRcallerX: Creating data-driven simulations by bridging R-based machine learning and AI-powered modeling in AnyLogic"],"prefix":"10.1016","volume":"348","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9816-3921","authenticated-orcid":false,"given":"Hang","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1708-8451","authenticated-orcid":false,"given":"Shan-e-hyder","family":"Soomro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1783-3327","authenticated-orcid":false,"given":"Xiaotao","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8303-8843","authenticated-orcid":false,"given":"Yunfei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8025-132X","authenticated-orcid":false,"given":"Chunpeng","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8512-3868","authenticated-orcid":false,"given":"Junbin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2905-0575","authenticated-orcid":false,"given":"Xiaoyu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9386-5430","authenticated-orcid":false,"given":"Wei","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2479-5951","authenticated-orcid":false,"given":"Tianyi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3157-0366","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5065-0815","authenticated-orcid":false,"given":"Mengru","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116401_bib0001","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/nphys2258","article-title":"2012. Quantum simulation","volume":"8","author":"Trabesinger","year":"2012","journal-title":"Nat. Phys. Insight"},{"key":"10.1016\/j.knosys.2026.116401_bib0002","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.eng.2019.01.014","article-title":"2019. Digital twins and cyber\u2013physical systems toward smart manufacturing and industry 4.0: correlation and comparison","volume":"5","author":"Tao","year":"2019","journal-title":"Engineering"},{"key":"10.1016\/j.knosys.2026.116401_bib0003","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1080\/17477778.2025.2500393","article-title":"2025. A literature review of supply chain analyses integrating discrete simulation modelling and machine learning","volume":"20","author":"Kogler","year":"2026","journal-title":"J. Simul."},{"key":"10.1016\/j.knosys.2026.116401_bib0004","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.jmsy.2020.06.018","article-title":"2020. Reinforcement learning for facilitating human-robot-interaction in manufacturing","volume":"56","author":"Oliff","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.knosys.2026.116401_bib0005","doi-asserted-by":"crossref","first-page":"225","DOI":"10.3390\/met12020225","article-title":"2022. Discrete event simulation for machine-learning enabled mine production control with application to gold processing","volume":"12","author":"Pe\u00f1a-Graf","year":"2022","journal-title":"Met. (Basel)"},{"key":"10.1016\/j.knosys.2026.116401_bib0006","article-title":"2025. A conceptual digital twin framework for supply chain recovery and resilience","volume":"9","author":"Victor Ogunsoto","year":"2025","journal-title":"Supply Chain Anal."},{"key":"10.1016\/j.knosys.2026.116401_bib0007","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/10618600.1996.10474713","article-title":"1996. R: a language for data analysis and graphics","volume":"5","author":"Ihaka","year":"1996","journal-title":"J. Comput. Graph. Stat"},{"key":"10.1016\/j.knosys.2026.116401_bib0008","series-title":"Proceedings of the Proceedings of the 2021 Winter Simulation Conference (WSC) (JW Marriott Desert Ridge, Arizona, 2021-12-12, 2021), IEEE, JW Marriott Desert Ridge, Arizona","article-title":"A tutorial on how to connect python with different simulation software to develop rich simheuristics","author":"Peyman","year":"2021"},{"key":"10.1016\/j.knosys.2026.116401_bib0009","doi-asserted-by":"crossref","first-page":"4072","DOI":"10.1093\/bioinformatics\/btz199","article-title":"2019. Simulation-assisted machine learning","volume":"35","author":"Deist","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.knosys.2026.116401_bib0010","series-title":"Proceedings of the Advances in Intelligent Data Analysis XVIII: 18th International Symposium on Intelligent Data Analysis, IDA 2020, April 27\u201329","article-title":"Combining machine learning and simulation to a hybrid modelling approach: current and future directions","author":"Rueden","year":"2020"},{"key":"10.1016\/j.knosys.2026.116401_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2021.105274","article-title":"2022. Simulation-assisted machine learning for operational digital twins","volume":"148","author":"Pylianidis","year":"2022","journal-title":"Env. Modell. Softw"},{"key":"10.1016\/j.knosys.2026.116401_bib0012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/aisy.202400626","article-title":"2025. Machine learning-assisted simulations and predictions for battery interfaces","volume":"7","author":"Sun","year":"2025","journal-title":"Adv. Intell. Syst."},{"key":"10.1016\/j.knosys.2026.116401_bib0013","series-title":"Proceedings of the 2024 Winter Simulation Conference (WSC), 15-18 December 2024 (Orlando, FL, USA, 2025-02-20, 2024), IEEE","article-title":"Hybrid modeling integrating artificial intelligence and modeling & simulation paradigms","author":"Tolk","year":"2024"},{"key":"10.1016\/j.knosys.2026.116401_bib0014","first-page":"1","article-title":"2021. Patient-specific computational simulation of coronary artery bifurcation stenting","volume":"11","author":"Zhao","year":"2021","journal-title":"Sci. Rep.-UK"},{"key":"10.1016\/j.knosys.2026.116401_bib0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-023-43392-y","article-title":"2023. LipIDens: simulation assisted interpretation of lipid densities in cryo-EM structures of membrane proteins","volume":"14","author":"Ansell","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.knosys.2026.116401_bib0016","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41524-024-01200-1","article-title":"2024. Atomistic simulation assisted error-inclusive bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals","volume":"10","author":"Mahata","year":"2024","journal-title":"Npj Comput. Mater."},{"key":"10.1016\/j.knosys.2026.116401_bib0017","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1038\/s41578-020-00255-y","article-title":"2020. Emerging materials intelligence ecosystems propelled by machine learning","volume":"6","author":"Song","year":"2020","journal-title":"Nat. Rev. Mater."},{"key":"10.1016\/j.knosys.2026.116401_bib0018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-024-48024-7","article-title":"2024. Task-oriented machine learning surrogates for tipping points of agent-based models","volume":"15","author":"Fabiani","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.knosys.2026.116401_bib0019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-024-50698-y","article-title":"2024. Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering","volume":"15","author":"Ding","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.knosys.2026.116401_bib0020","first-page":"1","article-title":"2021. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt","volume":"11","author":"Shahhosseini","year":"2021","journal-title":"Sci. Rep.-UK"},{"key":"10.1016\/j.knosys.2026.116401_bib0021","first-page":"1","article-title":"2024. Compressive strength of nano concrete materials under elevated temperatures using machine learning","volume":"14","author":"Zeyad","year":"2024","journal-title":"Sci. Rep.-UK"},{"key":"10.1016\/j.knosys.2026.116401_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2019.136242","article-title":"2020. Extinction of one of the world's largest freshwater fishes: lessons for conserving the endangered Yangtze fauna","volume":"710","author":"Zhang","year":"2020","journal-title":"Sci. Total Env."},{"key":"10.1016\/j.knosys.2026.116401_bib0023","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.knosys.2019.04.013","article-title":"2019. WekaDeeplearning4j: a deep learning package for Weka based on deeplearning4j","volume":"178","author":"Lang","year":"2019","journal-title":"Knowl.-Based Syst"},{"key":"10.1016\/j.knosys.2026.116401_bib0024","series-title":"Proceedings of the Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003) (Vienna, Austria, 2003)","article-title":"Rserve: a fast way to provide R functionality to applications","author":"Urbanek","year":"2003"},{"key":"10.1016\/j.knosys.2026.116401_bib0025","first-page":"29","article-title":"2016. Integrating R and Java for enhancing interactivity of algorithmic data analysis software solutions","author":"Furtun\u0103","year":"2016","journal-title":"Rom. Stat. Rev."},{"key":"10.1016\/j.knosys.2026.116401_bib0026","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.21105\/joss.02722","article-title":"2020. RCaller: a Java package for interfacing R","volume":"5","author":"Satman","year":"2020","journal-title":"J. Open Source Softw."},{"key":"10.1016\/j.knosys.2026.116401_bib0027","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.9734\/BJMCS\/2014\/10902","article-title":"2014. RCaller: a software library for calling R from Java","volume":"4","author":"Satman","year":"2014","journal-title":"Br. J. Math. Comput. Sci."},{"issue":"3","key":"10.1016\/j.knosys.2026.116401_bib0028","doi-asserted-by":"crossref","first-page":"446","DOI":"10.2507\/IJSIMM19-3-526","article-title":"Game-based workshops for the wood supply chain to facilitate knowledge transfer","volume":"19","author":"Kogler","year":"2020","journal-title":"Int. J. Simul. Model."},{"issue":"5","key":"10.1016\/j.knosys.2026.116401_bib0029","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1057\/jors.2014.52","article-title":"Exploring the model development process in discrete-event simulation: insights from six expert modellers","volume":"66","author":"Tako","year":"2015","journal-title":"J. Oper. Res. Soc."},{"key":"10.1016\/j.knosys.2026.116401_bib0030","series-title":"2018 Winter Simulation Conference (WSC)","first-page":"192","article-title":"Participative simulation (PartiSim): a facilitated simulation approach for stakeholder engagement","author":"Tako","year":"2018"},{"key":"10.1016\/j.knosys.2026.116401_bib0031","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1214\/09-SS057","article-title":"2009. Causal inference in statistics: an overview","volume":"3","author":"Pearl","year":"2009","journal-title":"Stat Surv."},{"key":"10.1016\/j.knosys.2026.116401_bib0032","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1214\/088342306000000114","article-title":"2006. Causal inference through potential outcomes and principal stratification: application to studies with\" censoring\" due to death","volume":"21","author":"Rubin","year":"2006","journal-title":"Stat. Sci."},{"key":"10.1016\/j.knosys.2026.116401_bib0033","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1086\/522055","article-title":"2007. The Lotka-Volterra predator-prey model with foraging\u2013Predation risk trade-offs","volume":"170","author":"Kr\u02c7ivan","year":"2007","journal-title":"Am. Nat."},{"key":"10.1016\/j.knosys.2026.116401_bib0034","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.neucom.2022.08.012","article-title":"2022. Robust learning of Huber loss under weak conditional moment","volume":"507","author":"Huang","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116401_bib0035","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.procs.2019.02.036","article-title":"2019. All convolutional neural networks for radar-based precipitation nowcasting","volume":"150","author":"Ayzel","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.knosys.2026.116401_bib0036","first-page":"1","article-title":"2022. Counting generations in birth and death processes with competing Erlang and exponential waiting times","volume":"12","author":"Giulia","year":"2022","journal-title":"Sci. Rep.-UK"},{"key":"10.1016\/j.knosys.2026.116401_bib0037","series-title":"Lyapunov Stability Theory","author":"Sastry","year":"1999"},{"key":"10.1016\/j.knosys.2026.116401_bib0038","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-022-35149-w","article-title":"2022. Precise atom manipulation through deep reinforcement learning","volume":"13","author":"Chen","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.knosys.2026.116401_bib0039","series-title":"2020 winter simulation conference (WSC)","first-page":"16","article-title":"Verification and validation of simulation models: an advanced tutorial","author":"Sargent","year":"2020"},{"key":"10.1016\/j.knosys.2026.116401_bib0040","series-title":"2022 Winter Simulation Conference (WSC)","first-page":"1283","article-title":"How to build valid and credible simulation models","author":"Law","year":"2022"},{"issue":"4","key":"10.1016\/j.knosys.2026.116401_bib0041","doi-asserted-by":"crossref","DOI":"10.14214\/sf.9984","article-title":"Discrete event simulation of multimodal and unimodal transportation in the wood supply chain: a literature review","volume":"52","author":"Kogler","year":"2018","journal-title":"Silva Fenn."},{"key":"10.1016\/j.knosys.2026.116401_bib0042","series-title":"International Conference on Simulation Tools and Techniques","first-page":"128","article-title":"Validation and verification techniques in simulation modelling for freight transportation","author":"Woletz","year":"2024"},{"key":"10.1016\/j.knosys.2026.116401_bib0043","first-page":"34","article-title":"Combined use of AI techniques and simulation to support production scheduling: evidence from empirical research","volume":"38","author":"Bandinelli","year":"2024","journal-title":"Proc. Eur. Counc. Model. Simul."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126011275?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126011275?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:05:31Z","timestamp":1783634731000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126011275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":43,"alternative-id":["S0950705126011275"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116401","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ALRcallerX: Creating data-driven simulations by bridging R-based machine learning and AI-powered modeling in AnyLogic","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116401","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"116401"}}