{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T06:50:32Z","timestamp":1791355832287,"version":"4.3.1"},"reference-count":88,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>When autonomous systems take operational authority over urban commerce, accountability and human oversight matter as much as efficiency. We present AAIRM, a governance-aware autonomous retail coordination framework addressing three requirements for trustworthy procurement: tamper-evident decision provenance, data sovereignty, and adaptive demand coordination. AAIRM couples a Proximal Policy Optimization (PPO) ordering policy, a permissioned Blockchain Trust Ledger (BTL) for multi-party audit trails, and a Federated Demand Learning (FDL) layer. Evaluation is simulation-based, using a multi-category synthetic environment and the public M5 dataset; no live retail deployment is claimed. At its cost-optimal operating point (92.3% fill), AAIRM lowers normalized inventory cost by 13.2% (95% CI 12.2\u201314.2) against a conventionally parameterized reorder-point\/economic-order-quantity (ROP\u2013EOQ) baseline and by 10.2% (95% CI 8.0\u201312.4) on M5. Service parity is obtained by moving AAIRM along its own cost\u2013service frontier rather than by retuning baselines: At 96.1% and 97.4% fill, it retains 8.2-point and 4.9-point advantages. A layered ablation attributes 8.8 points of the advantage to the reinforcement learning (RL) policy and 3.8 points to the combined coordination-and-governance block, which we further decompose into feasibility projection, escalation logic, cross-category policy rules, supplier ranking, and negotiation. The language-model orchestrator contributes 0.6 points (95% CI \u22120.9 to 2.1), statistically indistinguishable from zero and practically equivalent within a \u00b12-point margin. Against a feasibility-matched multi-agent RL comparator, the residual cost gap does not survive multiple-comparison correction, so the defensible advantages are hard feasibility and auditability, not cost. The BTL adds 6.6% decision-cycle overhead; its 500-case mutation replay is an integration check, and we give an explicit adversary model showing that crash-fault-tolerant ordering does not resist colluding majorities. FedProx converges within 0.9 WAPE points of centralized training at a 1.3-point cost penalty, without secure aggregation or differential privacy; we analyze rather than evaluate when those mechanisms become necessary. A 500-SKU study exposes a reward-hacking failure in a high-spoilage category, corrected by category-specific governance. We close with an architectural, non-empirical reading of labor exposure and three governance instruments regulators can act on.<\/jats:p>","DOI":"10.3389\/frai.2026.1942376","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T05:58:53Z","timestamp":1791352733000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Governance-aware autonomous retail coordination in artificial intelligence cities using multi-agent reinforcement learning, blockchain accountability, and federated learning"],"prefix":"10.3389","volume":"9","author":[{"given":"Toqeer Ali","family":"Syed","sequence":"first","affiliation":[{"name":"AI Center, Faculty of Computer and Information Systems, Islamic University of Madinah","place":["Madinah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Akarma","sequence":"additional","affiliation":[{"name":"AI Center, Faculty of Computer and Information Systems, Islamic University of Madinah","place":["Madinah, Saudi Arabia"]},{"name":"AI V&V Lab, King Fahd University of Petroleum and Minerals","place":["Dhahran, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahid","family":"Kamal","sequence":"additional","affiliation":[{"name":"Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing & Informatics, Multimedia University","place":["Cyberjaya, Selangor, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salman","family":"Jan","sequence":"additional","affiliation":[{"name":"Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing & Informatics, Multimedia University","place":["Cyberjaya, Selangor, Malaysia"]},{"name":"Faculty of Computer Studies, Arab Open University-Bahrain","place":["A'Ali, Bahrain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad B.","family":"Alkhodre","sequence":"additional","affiliation":[{"name":"AI Center, Faculty of Computer and Information Systems, Islamic University of Madinah","place":["Madinah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arshad","family":"Jamal","sequence":"additional","affiliation":[{"name":"Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing & Informatics, Multimedia University","place":["Cyberjaya, Selangor, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1145\/2976749.2978318","article-title":"\u201cDeep learning with differential privacy,\u201d","volume-title":"Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (CCS)","author":"Abadi","year":"2016"},{"key":"B2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1257\/jep.33.2.3","article-title":"Automation and new tasks: how technology displaces and reinstates labor","volume":"33","author":"Acemoglu","year":"2019","journal-title":"J. Econ. Perspect"},{"key":"B3","article-title":"\u201cConstrained policy optimization,\u201d","volume-title":"Proceedings of the 34th international conference on machine learning (ICML), volume 70 of proceedings of machine learning research","author":"Achiam","year":"2017"},{"key":"B4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jbusres.2020.11.050","article-title":"Intelligent purchasing: how artificial intelligence can redefine the purchasing function","volume":"124","author":"Allal-Ch\u00e9rif","year":"2021","journal-title":"J. Bus. Res"},{"key":"B5","volume-title":"Constrained Markov Decision Processes. Stochastic Modeling","author":"Altman","year":"1999"},{"key":"B6","doi-asserted-by":"publisher","first-page":"106003","DOI":"10.1016\/j.cie.2019.106003","article-title":"Multi-agent supply chain scheduling problem by considering resource allocation and transportation","volume":"137","author":"Aminzadegan","year":"2019","journal-title":"Comput. Ind. Eng"},{"key":"B7","article-title":"Concrete problems in AI safety","author":"Amodei","year":"2016","journal-title":"arXiv"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3190508.3190538","article-title":"\u201cHyperledger fabric: a distributed operating system for permissioned blockchains,\u201d","author":"Androulaki","year":"2018","journal-title":"Proceedings of the thirteenth EuroSys conference"},{"key":"B9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1257\/jep.29.3.3","article-title":"Why are there still so many jobs? The history and future of workplace automation","volume":"29","author":"Autor","year":"2015","journal-title":"J. Econ. Perspect"},{"key":"B10","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-15729-0","volume-title":"Inventory Control, 3rd Edn","author":"Axs\u00e4ter","year":"2015"},{"key":"B11","article-title":"\u201cHow to backdoor federated learning,\u201d","author":"Bagdasaryan","year":"2020","journal-title":"Proceedings of the 23rd international conference on artificial intelligence and statistics (AISTATS), volume 108 of"},{"key":"B12","doi-asserted-by":"crossref","DOI":"10.1109\/ICBC51069.2021.9461099","article-title":"\u201cA Byzantine fault-tolerant consensus library for Hyperledger Fabric,\u201d","volume-title":"Proceedings of the 2021 IEEE international conference on blockchain and cryptocurrency (ICBC)","author":"Barger","year":"2021"},{"key":"B13","article-title":"\u201cMachine learning with adversaries: Byzantine tolerant gradient descent,\u201d","volume-title":"Advances in neural information processing systems (NeurIPS), Vol. 30","author":"Blanchard","year":"2017"},{"key":"B14","year":"2020","journal-title":"Retail Supply Chain Digital Readiness Report"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1145\/3133956.3133982","article-title":"\u201cPractical secure aggregation for privacy-preserving machine learning,\u201d","author":"Bonawitz","year":"2017","journal-title":"Proceedings of the 2017 ACM SIGSAC conference on computer and communications security (CCS)"},{"key":"B16","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ejor.2021.07.016","article-title":"Deep reinforcement learning for inventory control: a roadmap","volume":"298","author":"Boute","year":"2022","journal-title":"Eur. J. Oper. Res"},{"key":"B17","author":"Brynjolfsson","year":"2014","journal-title":"The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies"},{"key":"B18","article-title":"\u201cPractical Byzantine fault tolerance,\u201d","volume-title":"Proceedings of the third symposium on operating systems design and implementation (OSDI)","author":"Castro","year":"1999"},{"key":"B19","volume-title":"Statistical Power Analysis for the Behavioral Sciences, 2nd Edn","author":"Cohen","year":"1988"},{"key":"B20","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1108\/SCM-09-2018-0309","article-title":"Blockchain technology: implications for operations and supply chain management","volume":"24","author":"Cole","year":"2019","journal-title":"Supply Chain Manage. Int. J"},{"key":"B21","volume-title":"Corporaci\u00f3n Favorita grocery Sales Forecasting","year":"2017"},{"key":"B22","volume-title":"Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence","author":"Crawford","year":"2021"},{"key":"B23","doi-asserted-by":"publisher","first-page":"104132","DOI":"10.1016\/j.compind.2024.104132","article-title":"Artificial intelligence in supply chain management: a systematic literature review of empirical studies and research directions","volume":"162","author":"Culot","year":"2024","journal-title":"Comput. Ind"},{"key":"B24","author":"Dafoe","year":"2018","journal-title":"AI Governance: A Research Agenda"},{"key":"B25","article-title":"Safe exploration in continuous action spaces","author":"Dalal","year":"2018","journal-title":"arXiv"},{"key":"B26","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.ejor.2021.10.045","article-title":"Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management","volume":"301","author":"De Moor","year":"2022","journal-title":"Eur. J. Oper. Res"},{"key":"B27","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1177\/10242589221143044","article-title":"Negotiating limits on algorithmic management in digitalised services: cases from Germany and Norway","volume":"29","author":"Doellgast","year":"2023","journal-title":"Transf. Eur. Rev. Labour Res"},{"key":"B28","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3390\/asi7050093","article-title":"Machine learning and deep learning models for demand forecasting in supply chain management: a critical review","volume":"7","author":"Douaioui","year":"2024","journal-title":"Appl. Syst. Innov"},{"key":"B29","author":"Eubanks","year":"2018","journal-title":"Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor"},{"key":"B30","year":"2024","journal-title":"Regulation (EU) 2024\/1689 Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts"},{"key":"B31","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.techfore.2016.08.019","article-title":"The future of employment: how susceptible are jobs to computerisation?","volume":"114","author":"Frey","year":"2017","journal-title":"Technol. Forecast. Soc. Change"},{"key":"B32","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s10100-023-00872-2","article-title":"Multi-echelon inventory optimization using deep reinforcement learning","volume":"32","author":"Geevers","year":"2024","journal-title":"Cent. Eur. J. Oper. Res"},{"key":"B33","article-title":"\u201cInverting gradients: how easy is it to break privacy in federated learning?\u201d","volume-title":"Advances in neural information processing systems (NeurIPS), Vol. 33","author":"Geiping","year":"2020"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1287\/msom.2021.1064","article-title":"Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems","volume":"24","author":"Gijsbrechts","year":"2022","journal-title":"Manuf. Serv. Oper. Manage"},{"key":"B35","doi-asserted-by":"crossref","DOI":"10.1145\/3605764.3623985","article-title":"\u201cNot what you've signed up for: compromising real-world LLM-integrated applications with indirect prompt injection,\u201d","volume-title":"Proceedings of the 16th ACM workshop on artificial intelligence and security (AISec)","author":"Greshake","year":"2023"},{"key":"B36","doi-asserted-by":"publisher","first-page":"8048","DOI":"10.24963\/ijcai.2024\/890","article-title":"\u201cLarge language model based multi-agents: a survey of progress and challenges,\u201d","author":"Guo","year":"2024","journal-title":"Proceedings of the thirty-third international joint conference on artificial intelligence (IJCAI-24)"},{"key":"B37","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.mfglet.2024.03.010","article-title":"Large language model based agent for process planning of fiber composite structures","volume":"40","author":"Holland","year":"2024","journal-title":"Manuf. Lett"},{"key":"B38","year":"2023","journal-title":"Retail Inventory Distortion: The Good, the Bad, and the Ugly"},{"key":"B39","doi-asserted-by":"crossref","DOI":"10.1109\/ICCA66035.2025.11430865","article-title":"\u201cBlockchain-monitored agentic AI architecture for trusted perception-reasoning-action pipelines,\u201d","volume-title":"Proceedings of the IEEE international conference on control and automation","author":"Jan","year":"2025"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-026-63383-5","article-title":"EAGF: a four-pillar ethical AI governance framework for trustworthy cybersecurity in 5G renewable energy IoT systems","author":"Jan","year":"2026","journal-title":"Sci. Rep"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2025.2604311","article-title":"Agentic LLMs in the supply chain: towards autonomous multi-agent consensus-seeking","author":"Jannelli","year":"2025","journal-title":"Int. J. Prod. Res"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/9781680837896","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Lear"},{"key":"B43","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/978-1-4419-6485-4_15","article-title":"\u201cManaging perishable and aging inventories: review and future research directions,\u201d","author":"Karaesmen","year":"2011","journal-title":"Planning Production and Inventories in the Extended Enterprise"},{"key":"B44","doi-asserted-by":"publisher","first-page":"240","DOI":"10.3390\/a14080240","article-title":"Adaptive supply chain: demand-Supply synchronization using deep reinforcement learning","volume":"14","author":"Kegenbekov","year":"2021","journal-title":"Algorithms"},{"key":"B45","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.ijinfomgt.2017.12.005","article-title":"1 blockchain's roles in meeting key supply chain management objectives","volume":"39","author":"Kshetri","year":"2018","journal-title":"Int. J. Inf. Manage"},{"key":"B46","doi-asserted-by":"publisher","first-page":"108815","DOI":"10.1016\/j.cie.2022.108815","article-title":"Managing healthcare supply chain through artificial intelligence (AI): a study of critical success factors","volume":"175","author":"Kumar","year":"2023","journal-title":"Comput. Ind. Eng"},{"key":"B47","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/EMR.2015.7123235","article-title":"The bullwhip effect in supply chains","volume":"43","author":"Lee","year":"2015","journal-title":"IEEE Eng. Manage. Rev"},{"key":"B48","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: challenges, methods, and future directions","volume":"37","author":"Li","year":"","journal-title":"IEEE Signal Process. Mag"},{"key":"B49","first-page":"429","article-title":"\u201cFederated optimization in heterogeneous networks,\u201d","volume-title":"Proceedings of machine learning and systems (MLSys), Vol. 2","author":"Li","year":""},{"key":"B50","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1016\/j.ijforecast.2021.11.013","article-title":"M5 accuracy competition: results, findings, and conclusions","volume":"38","author":"Makridakis","year":"","journal-title":"Int. J. Forecast"},{"key":"B51","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1016\/j.ijforecast.2021.07.007","article-title":"The M5 competition: background, organization, and implementation","volume":"38","author":"Makridakis","year":"","journal-title":"Int. J. Forecast"},{"key":"B52","doi-asserted-by":"publisher","first-page":"369","DOI":"10.3390\/systems13050369","article-title":"The moderating effects of operations and supply chain issues on digital readiness, value creation, and firm satisfaction","volume":"13","author":"Marjerison","year":"2025","journal-title":"Systems"},{"key":"B53","first-page":"1273","article-title":"\u201cCommunication-efficient learning of deep networks from decentralized data,\u201d","volume-title":"Proceedings of the 20th international conference on artificial intelligence and statistics (AISTATS), volume 54 of proceedings of machine learning research","author":"McMahan","year":"2017"},{"key":"B54","article-title":"\u201cLearning differentially private recurrent language models,\u201d","volume-title":"International conference on learning representations (ICLR)","author":"McMahan","year":"2018"},{"key":"B55","doi-asserted-by":"publisher","first-page":"108783","DOI":"10.1016\/j.compchemeng.2024.108783","article-title":"An analysis of multi-agent reinforcement learning for decentralized inventory control systems","volume":"188","author":"Mousa","year":"2024","journal-title":"Comput. Chem. Eng"},{"key":"B56","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s00146-023-01824-9","article-title":"The poverty of ethical AI: impact sourcing and AI supply chains","volume":"40","author":"Muldoon","year":"2023","journal-title":"AI Soc"},{"key":"B57","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1287\/opre.30.4.680","article-title":"Perishable inventory theory: a review","volume":"30","author":"Nahmias","year":"1982","journal-title":"Oper. Res"},{"key":"B58","volume-title":"Production and Operations Analytics, 8th Edn","author":"Nahmias","year":"2020"},{"key":"B59","article-title":"\u201cIn search of an understandable consensus algorithm,\u201d","volume-title":"Proceedings of the 2014 USENIX annual technical conference (USENIX ATC)","author":"Ongaro","year":"2014"},{"key":"B60","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1287\/msom.2020.0939","article-title":"A deep Q-network for the beer game: deep reinforcement learning for inventory optimization","volume":"24","author":"Oroojlooyjadid","year":"2022","journal-title":"Manuf. Serv. Oper. Manage"},{"key":"B61","article-title":"\u201cThe effects of reward misspecification: mapping and mitigating misaligned models,\u201d","volume-title":"International conference on learning representations (ICLR)","author":"Pan","year":"2022"},{"key":"B62","doi-asserted-by":"publisher","first-page":"100838","DOI":"10.1016\/j.hrmr.2021.100838","article-title":"Algorithms as work designers: how algorithmic management influences the design of jobs","volume":"32","author":"Parent-Rocheleau","year":"2022","journal-title":"Hum. Resour. Manag. Rev"},{"key":"B63","article-title":"InvAgent: a large language model based multi-agent system for inventory management in supply chains","author":"Quan","year":"2024","journal-title":"arXiv"},{"key":"B64","author":"Ray","year":"2019","journal-title":"Benchmarking Safe Exploration in Deep Reinforcement Learning"},{"key":"B65","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1080\/00207543.2018.1533261","article-title":"Blockchain technology and its relationships to sustainable supply chain management","volume":"57","author":"Saberi","year":"2019","journal-title":"Int. J. Prod. Res"},{"key":"B66","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.ijpe.2007.10.021","article-title":"On the benefits of CPFR and VMI: a comparative simulation study","volume":"113","author":"Sari","year":"2008","journal-title":"Int. J. Prod. Econ"},{"key":"B67","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017","journal-title":"arXiv"},{"key":"B68","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/BF03325096","article-title":"Agent-based systems for intelligent manufacturing: a state-of-the-art survey","volume":"1","author":"Shen","year":"1999","journal-title":"Knowl. Inf. Syst"},{"key":"B69","doi-asserted-by":"crossref","DOI":"10.1109\/SP.2017.41","article-title":"\u201cMembership inference attacks against machine learning models,\u201d","volume-title":"Proceedings of the 2017 IEEE symposium on security and privacy (S&P)","author":"Shokri","year":"2017"},{"key":"B70","doi-asserted-by":"crossref","DOI":"10.52202\/068431-0687","article-title":"\u201cDefining and characterizing reward hacking,\u201d","volume-title":"Advances in neural information processing systems (NeurIPS), Vol. 35","author":"Skalse","year":"2022"},{"key":"B71","article-title":"\u201cResponsive safety in reinforcement learning by PID Lagrangian methods,\u201d","volume-title":"Proceedings of the 37th international conference on machine learning (ICML), volume 119 of proceedings of machine learning research","author":"Stooke","year":"2020"},{"key":"B72","doi-asserted-by":"publisher","first-page":"6211","DOI":"10.1080\/00207543.2024.2311180","article-title":"Performance of deep reinforcement learning algorithms in two-echelon inventory control systems","volume":"62","author":"Stranieri","year":"2024","journal-title":"Int. J. Prod. Res"},{"key":"B73","volume-title":"Reinforcement Learning: An Introduction, 2nd Edn","author":"Sutton","year":"2018"},{"key":"B74","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1111\/j.1540-5915.1998.tb01356.x","article-title":"Modeling supply chain dynamics: a multiagent approach","volume":"29","author":"Swaminathan","year":"1998","journal-title":"Decis. Sci"},{"key":"B75","doi-asserted-by":"publisher","first-page":"e0353610","DOI":"10.1371\/journal.pone.0353610","article-title":"Agentic AI-enhanced digital twins for smart city civil infrastructure: a secure, autonomous and auditable management framework","volume":"21","author":"Syed","year":"","journal-title":"PLoS ONE"},{"key":"B76","article-title":"Agentic AI for smart inventory replenishment","author":"Syed","year":"2025","journal-title":"arXiv"},{"key":"B77","doi-asserted-by":"publisher","first-page":"106","DOI":"10.3390\/smartcities9070106","article-title":"Fedagent-chain: a secure federated and agentic ai framework for multilingual disability-inclusive employment in ai cities","volume":"9","author":"Syed","year":"","journal-title":"Smart Cities"},{"key":"B78","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.jbusres.2020.09.009","article-title":"Artificial intelligence in supply chain management: a systematic literature review","volume":"122","author":"Toorajipour","year":"2021","journal-title":"J. Bus. Res"},{"key":"B79","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1080\/00207543.2022.2056540","article-title":"Using the proximal policy optimisation algorithm for solving the stochastic capacitated lot sizing problem","volume":"61","author":"van Hezewijk","year":"2023","journal-title":"Int. J. Prod. Res"},{"key":"B80","doi-asserted-by":"publisher","first-page":"186345","DOI":"10.1007\/s11704-024-40231-1","article-title":"A survey on large language model based autonomous agents","volume":"18","author":"Wang","year":"2024","journal-title":"Front. Comput. Sci"},{"key":"B81","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1016\/j.ejor.2021.03.042","article-title":"Designing smart replenishment systems: internet-of-things technology for vendor-managed inventory at end consumers","volume":"295","author":"Wei\u00dfhuhn","year":"2021","journal-title":"Eur. J. Oper. Res"},{"key":"B82","year":"2023","journal-title":"The Future of Jobs Report 2023"},{"key":"B83","doi-asserted-by":"publisher","first-page":"100073","DOI":"10.1016\/j.dche.2022.100073","article-title":"Distributional reinforcement learning for inventory management in multi-echelon supply chains","volume":"6","author":"Wu","year":"2023","journal-title":"Digit. Chem. Eng"},{"key":"B84","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.ifacol.2024.09.200","article-title":"Multi-agent systems and foundation models enable autonomous supply chains: opportunities and challenges","volume":"58","author":"Xu","year":"2024","journal-title":"IFAC-PapersOnLine"},{"key":"B85","article-title":"\u201cReAct: synergizing reasoning and acting in language models,\u201d","volume-title":"The eleventh international conference on learning representations (ICLR)","author":"Yao","year":"2023"},{"key":"B86","doi-asserted-by":"publisher","first-page":"46","DOI":"10.56038\/ejrnd.v4i3.605","article-title":"AI-driven optimization of order procurement and inventory management in supply chains","volume":"4","author":"Yeldan","year":"2024","journal-title":"Eur. J. Res. Dev"},{"key":"B87","doi-asserted-by":"publisher","first-page":"24611","DOI":"10.52202\/068431-1787","article-title":"\u201cThe surprising effectiveness of PPO in cooperative multi-agent games,\u201d","author":"Yu","year":"2022","journal-title":"Advances in neural information processing systems (NeurIPS), Vol. 35"},{"key":"B88","article-title":"\u201cDeep leakage from gradients,\u201d","volume-title":"Advances in neural information processing systems (NeurIPS), Vol. 32","author":"Zhu","year":"2019"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1942376\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T05:59:02Z","timestamp":1791352742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1942376\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,7]]},"references-count":88,"alternative-id":["10.3389\/frai.2026.1942376"],"URL":"https:\/\/doi.org\/10.3389\/frai.2026.1942376","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,7]]},"article-number":"1942376"}}