{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:53:30Z","timestamp":1783385610208,"version":"3.54.6"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Northeastern University USA"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The integration of Artificial Intelligence (AI) into decision-making is transforming industry norms by enabling smarter, faster, and more reliable decisions. This paper presents a novel approach for supplier selection in manufacturing by combining the Analytic Hierarchy Process (AHP) with the Generative Pre-trained Transformer (GPT). Using GPT as virtual agents to mimic expert evaluations automates the supplier selection process, significantly improving efficiency. The study demonstrates the effectiveness of large language models as virtual experts in complex decision-making, highlighting AI\u2019s crucial role in strategic manufacturing operations. Comparing GPT\u2019s assessments with human expert judgments confirms the model\u2019s reliability and effectiveness in optimizing supplier selection. This approach enhances decision-making processes and emphasizes the broad utility of AI-centric methodologies in various industries.<\/jats:p>","DOI":"10.1007\/s10479-026-07136-7","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:13:01Z","timestamp":1773324781000},"page":"2593-2615","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The AI-driven Decision-Making (AIDM) Framework: Integrating AHP and ChatGPT for Supplier Selection"],"prefix":"10.1007","volume":"359","author":[{"given":"Negar","family":"Sadeghi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4471-6049","authenticated-orcid":false,"given":"Mohammad","family":"Dehghani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nihan","family":"Kabadayi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"7136_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sca.2024.100074","volume":"7","author":"A Abdulla","year":"2024","unstructured":"Abdulla, A., & Baryannis, G. (2024). A Hybrid Multi-Criteria Decision-Making and Machine Learning Approach for Explainable Supplier Selection. Supply Chain Analytics, 7, Article 100074.","journal-title":"Supply Chain Analytics"},{"key":"7136_CR2","doi-asserted-by":"crossref","unstructured":"Abdulla, A., Baryannis, G., & Badi, I. (2019). Weighting the Key Features Affecting Supplier Selection Using Machine Learning Techniques.","DOI":"10.20944\/preprints201912.0154.v1"},{"key":"7136_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2023.100342","volume":"9","author":"A Abdulla","year":"2023","unstructured":"Abdulla, A., Baryannis, G., & Badi, I. (2023). An Integrated Machine Learning and MARCOS Method for Supplier Evaluation and Selection. Decision Analytics Journal, 9, Article 100342.","journal-title":"Decision Analytics Journal"},{"key":"7136_CR4","doi-asserted-by":"crossref","unstructured":"Aggarwal, I., Gunreddy, N., John, A., & Rajan. (2021). A Hybrid Supplier Selection Approach Using Machine Learning and Data Envelopment Analysis. In 2021 Innovations in Power and Advanced Computing Technologies (I-PACT), pages 1\u20135. IEEE.","DOI":"10.1109\/i-PACT52855.2021.9696826"},{"key":"7136_CR5","unstructured":"Ahamed, Z. M., Dhahir, H. M., Mohammed, M. M., Ali, R. H., Hassan, S. H., Muhialdeen, A. S., Saeed, Y. A., Fatah, M. L., Qaradakhy, A. J., Ali, R. M., et al. (2023). Comparative Analysis of ChatGPT and Human Decision-Making in Thyroid and Neck Swellings: A Case-Based Study. Barw Medical Journal."},{"key":"7136_CR6","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1016\/j.spc.2021.02.015","volume":"27","author":"B Alavi","year":"2021","unstructured":"Alavi, B., Tavana, M., & Mina, H. (2021). A Dynamic Decision Support System for Sustainable Supplier Selection in Circular Economy. Sustainable Production and Consumption, 27, 905\u2013920.","journal-title":"Sustainable Production and Consumption"},{"issue":"3","key":"7136_CR7","doi-asserted-by":"publisher","first-page":"291","DOI":"10.5267\/j.dsl.2020.5.005","volume":"9","author":"R Astanti","year":"2020","unstructured":"Astanti, R., Mbolla, S., & Ai, T. (2020). Raw Material Supplier Selection in a Glove Manufacturing: Application of AHP and Fuzzy AHP. Decision Science Letters, 9(3), 291\u2013312.","journal-title":"Decision Science Letters"},{"issue":"4","key":"7136_CR8","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1080\/20479700.2017.1404730","volume":"13","author":"M Bahadori","year":"2020","unstructured":"Bahadori, M., Hosseini, S. M., Teymourzadeh, E., Ravangard, R., Raadabadi, M., & Alimohammadzadeh, K. (2020). A Supplier Selection Model for Hospitals Using a Combination of Artificial Neural Network and Fuzzy VIKOR. International Journal of Healthcare Management, 13(4), 286\u2013294.","journal-title":"International Journal of Healthcare Management"},{"key":"7136_CR9","doi-asserted-by":"crossref","unstructured":"Bahrini, A., Khamoshifar, M., Abbasimehr, H., Riggs, R. J., Esmaeili, M., Majdabadkohne, R. M., & Pasehvar, M. (2023). ChatGPT: Applications, Opportunities, and Threats. In 2023 Systems and Information Engineering Design Symposium (SIEDS), pages 274\u2013279. IEEE.","DOI":"10.1109\/SIEDS58326.2023.10137850"},{"key":"7136_CR10","doi-asserted-by":"crossref","unstructured":"Jason\u00a0W Burton, Ezequiel Lopez-Lopez, Shahar Hechtlinger, Zoe Rahwan, Samuel Aeschbach, Michiel\u00a0A Bakker, Joshua\u00a0A Becker, Aleks Berditchevskaia, Julian Berger, Levin Brinkmann, et\u00a0al. How Large Language Models Can Reshape Collective Intelligence. Nature Human Behaviour, pages 1\u201313, 2024.","DOI":"10.1038\/s41562-024-01959-9"},{"issue":"4","key":"7136_CR11","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1016\/j.eswa.2007.08.107","volume":"35","author":"D Celebi","year":"2008","unstructured":"Celebi, D., & Bayraktar, D. (2008). An Integrated Neural Network and Data Envelopment Analysis for Supplier Evaluation under Incomplete Information. Expert Systems with Applications, 35(4), 1698\u20131710.","journal-title":"Expert Systems with Applications"},{"key":"7136_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.04.047","volume":"139","author":"Y Cheng","year":"2020","unstructured":"Cheng, Y., Peng, J., Xin, G., Zhang, X., Liu, W., Zhou, Z., Yang, Y., & Huang, Z. (2020). An Intelligent Supplier Evaluation Model Based on Data-Driven Support Vector Regression in Global Supply Chain. Computers & Industrial Engineering, 139, Article 105834.","journal-title":"Computers & Industrial Engineering"},{"issue":"1","key":"7136_CR13","doi-asserted-by":"publisher","first-page":"5","DOI":"10.52812\/msbd.63","volume":"3","author":"EL Chuma","year":"2023","unstructured":"Chuma, E. L., & De Oliveira, G. G. (2023). Generative AI for Business Decision-Making: A Case of ChatGPT. Management Science and Business Decisions, 3(1), 5\u201311.","journal-title":"Management Science and Business Decisions"},{"key":"7136_CR14","doi-asserted-by":"crossref","unstructured":"Dehghanimohammadabadi, M., & Kabadayi, N. (2020). A Two-Stage AHP Multi-Objective Simulation Optimization Approach in Healthcare. International Journal of the Analytic Hierarchy Process,12(1).","DOI":"10.13033\/ijahp.v12i1.701"},{"key":"7136_CR15","doi-asserted-by":"crossref","unstructured":"Alireza Fallahpour, Ezutah\u00a0Udoncy Olugu, Siti\u00a0Nurmaya Musa, Dariush Khezrimotlagh, and Kuan\u00a0Yew Wong. An Integrated Model for Green Supplier Selection under Fuzzy Environment: Application of Data Envelopment Analysis and Genetic Programming Approach. Neural Computing and Applications, 27:707\u2013725, 2016.","DOI":"10.1007\/s00521-015-1890-3"},{"issue":"2","key":"7136_CR16","first-page":"209","volume":"11","author":"A Fallahpour","year":"2018","unstructured":"Fallahpour, A., Kazemi, N., Molani, M., Nayyeri, S., & Ehsani, M. (2018). An Intelligence-Based Model for Supplier Selection Integrating Data Envelopment Analysis and Support Vector Machine. Interdisciplinary Journal of Management Studies, 11(2), 209\u2013241.","journal-title":"Interdisciplinary Journal of Management Studies"},{"issue":"16","key":"7136_CR17","doi-asserted-by":"publisher","first-page":"5676","DOI":"10.1080\/00207543.2023.2294116","volume":"62","author":"SF Wamba","year":"2024","unstructured":"Wamba, S. F., Guthrie, C., Queiroz, M. M., & Minner, S. (2024). ChatGPT and Generative Artificial Intelligence: An Exploratory Study of Key Benefits and Challenges in Operations and Supply Chain Management. International Journal of Production Research, 62(16), 5676\u20135696.","journal-title":"International Journal of Production Research"},{"issue":"1","key":"7136_CR18","first-page":"8811834","volume":"2020","author":"T Gegovska","year":"2020","unstructured":"Gegovska, T., Koker, R., & Cakar, T. (2020). Green Supplier Selection Using Fuzzy Multiple-Criteria Decision-Making Methods and Artificial Neural Networks. Computational Intelligence and Neuroscience, 2020(1), 8811834.","journal-title":"Computational Intelligence and Neuroscience"},{"issue":"4","key":"7136_CR19","doi-asserted-by":"publisher","first-page":"152","DOI":"10.3390\/logistics9040152","volume":"9","author":"OJ Gidiagba","year":"2025","unstructured":"Gidiagba, O. J., Tartibu, L., & Okwu, M. (2025). Integrating machine learning with multi-criteria decision-making models for sustainable supplier selection in dynamic supply chains. Logistics, 9(4), 152.","journal-title":"Logistics"},{"issue":"9","key":"7136_CR20","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1109\/TNN.2009.2027321","volume":"20","author":"D Golmohammadi","year":"2009","unstructured":"Golmohammadi, D., Creese, R. C., Valian, H., & Kolassa, J. (2009). Supplier Selection Based on a Neural Network Model Using Genetic Algorithm. IEEE Transactions on Neural Networks, 20(9), 1504\u20131519.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"2","key":"7136_CR21","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1016\/j.eswa.2006.12.008","volume":"34","author":"SH Ha","year":"2008","unstructured":"Ha, S. H., & Krishnan, R. (2008). A Hybrid Approach to Supplier Selection for the Maintenance of a Competitive Supply Chain. Expert Systems with Applications, 34(2), 1303\u20131311.","journal-title":"Expert Systems with Applications"},{"key":"7136_CR22","doi-asserted-by":"crossref","unstructured":"Kabadayi, N., & Dehghanimohammadabadi, M. (2022). Multi-Objective Supplier Selection Process: A Simulation-Optimization Framework Integrated with MCDM. Annals of Operations Research, pages 1\u201323.","DOI":"10.1007\/s10479-021-04424-2"},{"issue":"5","key":"7136_CR23","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1080\/23789689.2023.2165782","volume":"8","author":"MM Khan","year":"2023","unstructured":"Khan, M. M., Bashar, I., Minhaj, G. M., Wasi, A. I., & Hossain, N. U. I. (2023). Resilient and Sustainable Supplier Selection: An Integration of SCOR and Machine Learning Approach. Sustainable and Resilient Infrastructure, 8(5), 453\u2013469.","journal-title":"Sustainable and Resilient Infrastructure"},{"issue":"12","key":"7136_CR24","doi-asserted-by":"publisher","first-page":"35","DOI":"10.5120\/1631-2193","volume":"11","author":"J Kumar","year":"2010","unstructured":"Kumar, J., & Roy, N. (2010). A Hybrid Method for Vendor Selection Using Neural Network. International Journal of Computer Applications, 11(12), 35\u201340.","journal-title":"International Journal of Computer Applications"},{"issue":"12","key":"7136_CR25","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1016\/j.jclepro.2010.03.020","volume":"18","author":"RJ Kuo","year":"2010","unstructured":"Kuo, R. J., Wang, Y. C., & Tien, F. C. (2010). Integration of Artificial Neural Network and MADA Methods for Green Supplier Selection. Journal of Cleaner Production, 18(12), 1161\u20131170.","journal-title":"Journal of Cleaner Production"},{"key":"7136_CR26","doi-asserted-by":"crossref","unstructured":"Kuo, R. J., Hong, S. Y., & YC1201 Huang. (2010). Integration of Particle Swarm Optimization-Based Fuzzy Neural Network and Artificial Neural Network for Supplier Selection. Applied Mathematical Modelling,34(12), 3976\u20133990.","DOI":"10.1016\/j.apm.2010.03.033"},{"issue":"1","key":"7136_CR27","first-page":"29","volume":"1","author":"C Lakshmanpriya","year":"2013","unstructured":"Lakshmanpriya, C., Sangeetha, N., & Lavanpriya, C. (2013). Vendor Selection in Manufacturing Industry Using AHP and ANN. The SIJ Transactions on Industrial, Financial & Business Management, 1(1), 29\u201334.","journal-title":"The SIJ Transactions on Industrial, Financial & Business Management"},{"issue":"1","key":"7136_CR28","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s10479-019-03453-2","volume":"287","author":"Y Li","year":"2020","unstructured":"Li, Y., Diabat, A., & Chung-Cheng, L. (2020). Leagile Supplier Selection in Chinese Textile Industries: A DEMATEL Approach. Annals of Operations Research, 287(1), 303\u2013322.","journal-title":"Annals of Operations Research"},{"key":"7136_CR29","unstructured":"Morales, A. A. S. (2024). Gen-AI and the Future of Supply Chain Management: The Impact of Large Language Models on Modern Supply Chain Management. Master\u2019s thesis, Universidade NOVA de Lisboa (Portugal)."},{"key":"7136_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119746","volume":"222","author":"S Nazari-Shirkouhi","year":"2023","unstructured":"Nazari-Shirkouhi, S., Tavakoli, M., Govindan, K., & Mousakhani, S. (2023). A Hybrid Approach Using Z-Number DEA Model and Artificial Neural Network for Resilient Supplier Selection. Expert Systems with Applications, 222, Article 119746.","journal-title":"Expert Systems with Applications"},{"key":"7136_CR31","doi-asserted-by":"crossref","unstructured":"Pandey, M., Litoriya, R., & Pandey, P. (2024). Indicators of AI in Automation: An Evaluation Using Intuitionistic Fuzzy DEMATEL Method with Special Reference to ChatGPT. Wireless Personal Communications, pages 1\u201321.","DOI":"10.1007\/s11277-024-10917-7"},{"issue":"2","key":"7136_CR32","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s10660-023-09768-4","volume":"25","author":"A Pinar","year":"2025","unstructured":"Pinar, A. (2025). An Integrated Sentiment Analysis and Q-Rung Orthopair Fuzzy MCDM Model for Supplier Selection in E-Commerce: A Comprehensive Approach. Electronic Commerce Research, 25(2), 1311\u20131342.","journal-title":"Electronic Commerce Research"},{"issue":"3\u20135","key":"7136_CR33","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/0270-0255(87)90473-8","volume":"9","author":"RW Saaty","year":"1987","unstructured":"Saaty, R. W. (1987). The Analytic Hierarchy Process-What It Is and How It Is Used. Mathematical Modelling, 9(3\u20135), 161\u2013176.","journal-title":"Mathematical Modelling"},{"key":"7136_CR34","doi-asserted-by":"crossref","unstructured":"Sakshi, T. M., Tyagi, P., & Jain, V. (2024). Emerging Trends in Hybrid Information Systems Modeling in Artificial Intelligence. Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence, page 115.","DOI":"10.1515\/9783111331133-007"},{"key":"7136_CR35","unstructured":"Soori, M., Jough, F. K. G., Dastres, R., & Arezoo, B. (2024). AI-Based Decision Support Systems in Industry A Review. Journal of Economy and Technology."},{"issue":"1","key":"7136_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/29966892.2025.2474824","volume":"1","author":"SP Subramanian","year":"2025","unstructured":"Subramanian, S. P., Pandian, P., Sivaprakasam, R., & Kadarkarai, J. (2025). A Novel Machine Learning Framework for Optimized Supplier Selection Using the Weights by ENvelope and SLOpe (WENSLO) Technique. Applied Operations and Analytics, 1(1), 1\u201310.","journal-title":"Applied Operations and Analytics"},{"key":"7136_CR37","doi-asserted-by":"crossref","unstructured":"Svoboda, I., & Lande, D. (2024). Enhancing Multi-Criteria Decision Analysis, with AI: Integrating Analytic Hierarchy Process and GPT-4 for Automated Decision Support. arXiv preprint arXiv:2402.07404.","DOI":"10.2139\/ssrn.5069656"},{"issue":"1","key":"7136_CR38","first-page":"109","volume":"4","author":"SH Tang","year":"2013","unstructured":"Tang, S. H., Hakim, N., Khaksar, W., Ariffin, M. K. A., Sulaiman, S., & Pah, P. S. (2013). A Hybrid Method Using Analytic Hierarchical Process and Artificial Neural Network for Supplier Selection. International Journal of Innovation, Management and Technology, 4(1), 109\u2013111.","journal-title":"International Journal of Innovation, Management and Technology"},{"key":"7136_CR39","doi-asserted-by":"crossref","unstructured":"Vahidnia, M. H. (2025). Multi-agent systems of large language models as weight assigners: An approach to collaborative weighting in spatial multi-criteria decision-making. Geomatica, page Article 100071.","DOI":"10.1016\/j.geomat.2025.100071"},{"key":"7136_CR40","unstructured":"Wang, H., Zhang, F., & Chaoxu, M. (2025). One for All: A General Framework of LLMs-Based Multi-Criteria Decision Making on Human Expert Level.,arXiv preprint arXiv:2502.15778."},{"key":"7136_CR41","doi-asserted-by":"crossref","unstructured":"Wang, X., & Xiaojun, W. (2024). Can ChatGPT Serve as a Multi-Criteria Decision Maker?. A Novel Approach to Supplier Evaluation.ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 10281\u201310285. IEEE.","DOI":"10.1109\/ICASSP48485.2024.10447204"},{"issue":"1","key":"7136_CR42","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1007\/s10479-023-05698-4","volume":"342","author":"Z-J Wang","year":"2024","unstructured":"Wang, Z.-J., Chen, Z.-S., Qin, S., Chin, K.-S., Pedrycz, W., & Skibniewski, M. J. (2024). Enhancing the Sustainability and Robustness of Critical Material Supply in Electrical Vehicle Market: An AI-Powered Supplier Selection Approach. Annals of Operations Research, 342(1), 921\u2013958.","journal-title":"Annals of Operations Research"},{"issue":"5","key":"7136_CR43","doi-asserted-by":"publisher","first-page":"9105","DOI":"10.1016\/j.eswa.2008.12.039","volume":"36","author":"W Desheng","year":"2009","unstructured":"Desheng, W. (2009). Supplier Selection: A Hybrid Model Using DEA, Decision Tree and Neural Network. Expert Systems with Applications, 36(5), 9105\u20139112.","journal-title":"Expert Systems with Applications"},{"key":"7136_CR44","unstructured":"Zuheros, C., Herrera-Poyatos, D., Montes, R., & Herrera, F. (2024). Large Language Models for Crowd Decision Making Based on Prompt Design Strategies Using ChatGPT: Models. Analysis and Challenges.,arXiv preprint arXiv:2403.15587."}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-026-07136-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-026-07136-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-026-07136-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:20:37Z","timestamp":1775737237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-026-07136-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["7136"],"URL":"https:\/\/doi.org\/10.1007\/s10479-026-07136-7","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,12]]},"assertion":[{"value":"24 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}