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Area Statements, Area Editors, and Associate Editors:

Data Enabled Analytics

Area Editor
Dariush Khezrimotlagh
Pennsylvania State University, Harrisburg, USA
dk@psu.edu

Data-Enabled Analytics includes data-driven methodologies to extract insights, assess performance, and support informed decision-making across domains. It emphasizes the development of hybrid analytics frameworks that integrate multiple methods (e.g., data envelopment analysis, machine learning, causal and predictive modeling, prescriptive analytics, explainable AI, and real-time analytics inference) to address multi-faceted decision problems. The focus is on methodological advances for understanding, explaining, and evaluating performance, including benchmarking, causal inference, game-theoretic analytics , and large-scale or real-time pattern discovery. The purpose is to enable organizations to gain deeper insights into their operations while ensuring interpretability and reliability in how data are analyzed, and results are used.

Associate Editors
Juan Aparicio, University Miguel Hernandez of Elche, Spain
Vincent Charles, Queen's University, United Kingdom
Hirofumi Fukuyama, Fukuoka University, Japan
Chiang Kao, National Cheng Kung University, Taiwan
Sungmook Lim, Dongguk University, South Korea
Victor Podinovski, Loughborough University, United Kingdom
Biresh K. Sahoo, Xavier University, India

Data Driven Optimization

Area Editor
Ming Zhao
University of Delaware, USA
mzhao@udel.edu

We aim to publish high-quality papers focusing on research driven by real-world operational challenges and developing novel methodologies in data-driven optimization. Our emphasis is on creating innovative optimization models and algorithms that effectively integrate data to address critical managerial and operational issues. Submissions should demonstrate strong practical motivation, deliver implementable optimized solutions, and showcase potential or impact in relevant fields. We encourage studies that explore cutting-edge data-driven optimization methods and operations research applications. We value contributions that push the boundaries of theoretical foundations, develop innovative methodological and algorithmic advances, and deliver insights for operational decision-making.

Associate Editors
Bo Chen, University of Warwick, United Kingdom
Tianhu Deng, Tsinghua University, China
Xuan Vinh Doan, University of Warwick, United Kingdom
Xiang Fang, University of Colorado Denver, USA
Tulay Flamand, University of Colorado Denver, USA
Yuhong He, California State University, USA
Wilco Van den Heuvel, Erasmus University Rotterdam, The Netherlands
Pan Kai, Hong Kong Polytechnic University, China
Youngsoo Kim, Loyola University, USA
Dominik Kress, Helmut Schmidt University Hamburg, Germany
Dmytro Matsypura, The University of Sydney, Australia
Somayeh Moazeni, Stevens Institute of Technology, USA
Alexandra Newman, Colorado School of Mines, USA
Vera Tilson, University of Rochester, USA
Alena Otto, University of Passau, Germany

Decision Analysis and Preference Modeling

Area Editor
Luis M. C. Dias
University of Coimbra, Portugal
lmcdias@fe.uc.pt

We seek to publish high quality papers developing and using decision analysis and analytics methods for improved managerial decision-making practices. This area includes, but is not limited to, multicriteria decision analysis, project portfolio decision analysis, multiobjective programming, and the analytics of preference learning. We particularly welcome papers dealing with multiple criteria encompassing economic, environmental, and social dimensions in decision making processes towards a more sustainable future. We are also interested in papers bridging decision analysis with computer science and other disciplines. Suitable papers should be relevant to a wide audience and make either (1) a theoretical/methodological contribution with a clear applicability to managerial decision-making, or (2) an empirical/practical contribution demonstrating significant impact for end-users or providing insights on the best ways of using decision analytics tools and interacting with decision makers. Research findings should provide new insights and implications to the practice of management science.

Associate Editors
Sarah Ben Amor, University of Ottawa, Canada
Olga Battaia, Kedge Business School, France
Chialin Chen, National Taiwan University, Taiwan
Salvatore (Salvo) Corrente, University of Catania, Italy
Milosz Kadzinski, Poznan University of Technology, Poland
Juuso Liesiö, Aalto University, Finland
Banu Lokman, University of Portsmouth, United Kingdom
Evangelos Triantaphyllou, Louisiana State University, USA

Innovative Applications

Area Editor
Jay Rosenberger
University of Texas at Arlington, USA
jrosenbe@uta.edu

For decades, highly quantitative models developed using decision and data analytics, operations research, and the management sciences have aided managers and leaders in analysis and decision making. The domain applications have included a large variety of areas, such as manufacturing, logistics, transportation, defense, and health care. However, government and industry decision making is ubiquitous, as managers make critical decisions well outside of these domains as well. We seek to publish articles with high-quality decision and data analytics in new and innovative applications both within the traditional domain applications areas as well as areas that have received less attention and advancement from the operations research and the management sciences research communities. The articles should either (i) include a theoretical or methodological contribution that advances the state-of-the-art or (ii) provide a significant impact on the state of practice, preferably within a new and innovative domain.

Associate Editors
Olga Battaia, Kedge Business School, France
Juan S. Borrero, University of South Florida, USA
Stanko Dimitrov, University of Waterloo, Canada
Zhaolin (Erick) Li, University of Sydney, Australia
Brian J. Lunday, Air Force Institute of Technology, USA

Supply Chain Management

Area Editor
Leon Yang Zhu
Cheung Kong Graduate School of Business, China
leon.zhu@ckgsb.edu.cn

We invite submissions that develop methodological and analytical models tailored to address pressing business challenges within the field of Supply Chain Management. Of particular interest are papers that accurately capture the interactions among inter-firm or intra-firm entities in real-world supply chain contexts, spanning both public and private sectors, concerning the design, procurement, production, delivery, and return of goods or services. The methodologies may include, but are not limited to, mechanism design, contract theory, game theory, econometrics, experiments, and surveys. We seek papers that present cutting-edge modeling techniques and solutions, providing insights that are either empirically grounded or directly driven by industry problems. Submissions should blend the rigorous standards of traditional academic research with the practical significance of industrial applications.

Associate Editors
M. Serkan Akturk, Clemson University, USA
Tolga Aydinliyim, City University of New York, USA
Sreekumar Bhaskaran, Southern Methodist University, USA
Jing (Jenny) Chen, Dalhousie University, Canada
Yael Perlman, Bar-Ilan University, Israel
Wenjing Shen, Drexel University, USA
Biying Shou, The Chinese University of Hong Kong, China
Konstantina Skouri, University of Ioannina, Greece
Ju Myung (JM) Song, University of Massachusetts at Lowell, USA
Xun (Bruce) Tong, University of Groningen, The Netherlands
Yulan Wang, The Hong Kong Polytechnic University, China
Alireza Yazdani, California State Polytechnic University, USA
Huan Zheng, Shanghai Jiao Tong University, China

Technology-driven Operations and Management Science

Area Editor
Junmin (Jim) Shi
New Jersey Institute of Technology, USA
jim.shi@njit.edu

In the era of prevailing technology and data (e.g., Web 3.0; Industry 4.0, and 5G), management science has been filled with new solution approaches and computational power. For example, Artificial intelligence (AI) can be used to automate certain decision-making processes and can provide insights that would have been impossible for humans to uncover. The new opportunity is ushering in the wave of applying scientific methods and analytical tools, often leveraging technology, to solve complex management problems and optimize decision-making.

This area focuses primarily on the application of emerging technologies like AI, Machine Learning (ML), Big Data Analytics, Blockchain, and the Internet of Things (IoT) to solve complex management problems, optimize decision-making, improve operational efficiency, etc. The TOMS area welcomes submissions that examine challenging managerial problems that arise outside the traditional boundaries of management science. These include a variety of areas such as digitization, IoT, AI, Blockchain, Cloud Computing, Data Science, Machine Learning, 3D printing, RFID chips, drones, with potential applications of Smart Cities, FinTech (e.g., cryptocurrency, P2P Lending, DeFin, etc.), AgriTech, InsurTech, PropTech, Information Systems (e.g., cloud computing, new models of software development, etc.), Electrical Vehicles (EV) and Autonomous Vehicles (AV), among many others.

Associate Editors
Long He, George Washington University, USA
Junghee Lee, University of Notre Dame, USA
Mahdi Mahmoudzadeh, University of Auckland, New Zealand
Sandun Perera, University of Nevada, USA

Transportation and Logistics

Area Editor
Maria Battarra
University of Bath, UK
M.Battarra@bath.ac.uk

The area of transportation and logistics encompasses all applied problems related to the routing of people and goods, aiming to enhance delivery and transportation services, reduce costs, and lower emissions. New technologies, evolving policies and regulations, and online platforms offer remarkable opportunities for supporting decision-makers using advanced Operations Research techniques to address complex business and societal challenges.

We welcome the submission of high quality and original papers addressing these critical transportation and logistics challenges and propose methodologies that contribute to foundational knowledge in the field. Full consideration will be given to original contributions that: • Accurately and precisely model impactful new transportation and logistics applications (avoiding artificial environments) or address classical transportation and logistics problems from the existing literature
• Present innovative, rigorous, and adequate quantitative Operations Research methodologies
• Provide reproducible analytical and computational results, benchmarked against similar algorithms according to the discipline's standards
• Exhibit exceptional conciseness and clarity in their presentation
• When relevant, offer managerial insights rooted in industrial practice and supported by real-world data.

Associate Editors
Merve Bodur, The University of Edinburgh, UK
Teobaldo Leite Bulhões Júnior, Federal University of Paraíba, Brazil
Fabio Furini, Sapienza University of Rome, Italy
Ahmed Ghoniem, University of Massachusetts-Amherst, USA
Lars Magnus Hvattum, Molde University College, Norway
Tamas Kis, Hungarian Academy of Sciences, Hungary
Markus Leitner, Vrije Universiteit Amsterdam, The Netherlands
Janny M. Y. Leung, University of Macau, China
Jannik Matuschke, Katholieke Universiteit Leuven, Belgium
Alena Otto, University of Passau, 94032 Passau, Germany
Dimitris Paraskevopoulos, University of London, UK
Yu Yang, University of Florida, USA