https://academic.oup.com/imaman/pages/call-for-papers-fusion-of-large-language-models
Submission closing date: 31 December 2026
The rapid advancement of Large Language Models (LLMs) has significantly reshaped the landscape of data-driven decision-making in management. These models offer unprecedented capabilities to process natural language, generate insights, and support complex reasoning—complementing traditional mathematical and optimization frameworks used in managerial contexts. However, the true potential of LLMs in management science lies not merely in their standalone use, but in their thoughtful integration with established quantitative methodologies to enhance the precision, interpretability, and robustness of managerial decisions.
This special issue aims to bridge the gap between advanced natural language processing and the rigorous domain of management mathematics. We seek to explore how LLMs can be synergistically combined with mathematical modeling, optimization techniques, statistical analysis, and computational intelligence to address complex decision-making challenges. Such integration is particularly vital in areas characterized by uncertainty, large-scale data, and the need for real-time analytical responses.
Key areas of interest include, but are not limited to:
By fostering a dialogue between AI researchers and management scientists, this special issue will highlight how LLMs can transform traditional mathematical approaches into more adaptive, intuitive, and powerful decision-making frameworks. We welcome theoretical advancements, novel methodologies, and practical case studies that demonstrate tangible impacts in fields such as finance, healthcare, logistics, public policy, and strategic planning.
The primary function of decision support systems in networks employing large language models is to emulate the decision-making capabilities of the human brain, thereby mitigating the limitations of mathematical models that are challenging to accurately and effectively address large language model (LLM) technology problems.
For this special issue, we invite papers related to the following topics (but not limited to):
Authors should submit a cover letter and a manuscript by December 31, 2026, via the Journal’s online submission site. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.
Please see the Author instructions on the website. When submitting via our submission site, please select the special issue’s title “LLM and Management Mathematics” to ensure that it will be reviewed for this special issue.
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Papers will be subject to a strict review process managed by the Guest Editors, and accepted papers will be published online individually, before print publication.
Business School, Hunan Normal University, Changsha, 16302@hunnu.edu.cn
Prof. Dr. Liurui Deng serves as Head of the Department of Finance at the Business School of Hunan Normal University. Her primary research interests include financial mathematics, financial risk management, investor behaviour, and predictive analysis of securities markets and economic indicators. As principal investigator, she has secured and led multiple national and ministerial-level research projects, including grants from the National Natural Science Foundation of China, the National Social Science Foundation of China, the Ministry of Education Humanities and Social Sciences Foundation, the Hunan Provincial Natural Science Foundation, and the Hunan Provincial Social Science Foundation, and so on. The total funding exceeds one million yuan, demonstrating strong academic leadership and competitive research capabilities.
Lee Shau Kee School of Business and Administration, Hong Kong Metropolitan University, Hong Kong, wyeoh@hkmu.edu.hk
Prof. William Yeoh is an expert in business intelligence, big data analytics, and cybersecurity. His expertise includes doctoral supervision, academic publishing, and funding acquisition. He has received awards for research and teaching, including Researcher of the Year, ICT Educator of the Year, and recognition as one of Australia's Top 25 Analytics Leaders. His leadership experience covers industry engagement, research coordination, and international collaborations at Deakin University, University of Ottawa, University of Indonesia, and UTAR. He is a Fellow of the Australian Computer Society (ACS).
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada, kelvin.wong@ieee.org
Prof. Dr. Kelvin KL Wong is an expert in medical image processing, computational science, and artificial intelligence (AI). He introduced "Cybernetical Intelligence" and was ranked among Stanford's top 1.3% biomedical engineers in 2020. Dr. Wong has pioneered AI-driven healthcare solutions, including Deep Red, a tool for modular programming and deep learning model creation. He led the African Telehealth Network in Zambia, enhancing healthcare delivery. Dr. Wong is a Foreign Fellow of the Zambia Academy of Sciences and a Fellow of IEAust, with significant contributions to AI and telehealth.
Department of Computer and Information Science, University of Macau, Macau, China, ccfong@umac.mo
Dr. Simon Fong is an Associate Professor at the University of Macau, specializing in data mining, big data analytics, meta-heuristic optimization algorithms, and their applications. He has authored over 445 papers and serves on the editorial boards of several high-impact journals. Dr. Fong co-founded the Data Analytics and Collaborative Computing Research Group. He holds leadership roles as Vice-Chair of the IEEE Computational Intelligence Society's Task Force and Vice-Director of the International Consortium for Optimization and Modelling in Science and Industry. His research includes work in data stream mining and business intelligence.
Professor and Chair in Business Analytics, Surrey Business School, University of Surrey, Guildford, UK, a.emrouznejad@surery.ac.uk
Ali Emrouznejad is a Professor and Chair in Business Analytics at Surrey Business School, UK. He also serves as the Director of the Centre for Business Analytics in Practice (CBAP), leading research in performance measurement and management, efficiency and productivity analysis, as well as AI and big data. Prof. Emrouznejad is the Editor-in-Chief of the Journal of Business Analytics. In addition, he holds roles as an editor, associate editor, department editor, or guest editor for various journals, including the European Journal of Operational Research, Journal of the Operational Research Society, Annals of Operations Research. OR Spectrum, RAIRO – Operations Research, Socio-Economic Planning Sciences, IMA Journal of Management Mathematics, among others.
He has published 25 books and over 250 articles in leading journals. He has been recognized by Stanford University as one of the top 2% most influential scientists worldwide. He is also member of the Business and Management panel for the UK Research Excellence Framework 2029.