
Advances in Operations Research Theory, Algorithms, and Applications
Description
This book gathers a selection of peer-reviewed papers presented at the Seventh International Conference of the Tunisian Operations Research Society (IC_TORS'25) which was held in Sousse, Tunisia from 11th to 13th April 2025. It was sponsored by the Association of European Operational Research Societies (EURO), the International Federation of Operational Research Societies (IFORS), and INFORMS Bahrain International Group.
The book explores research issues in operational research and decision aid, highlighting recent theoretical advancements in areas such as linear, nonlinear, integer, stochastic, multilevel, and multi-objective optimization, as well as business analytics, simulation, decision theory, and multi-criteria decision aid. It also presents real-world applications across various sectors, including Industry 4.0, renewable energy, agriculture, green logistics and transportation, healthcare, water resource management, and sustainable, resilient supply chains.
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Persons
Dr. Adnen El Amraoui is Associate Professor at the University of Artois (France). He received his Ph.D. degree in process control from the Technical University of Belfort-Montbéliard (UTBM, France) in 2011. His research interests include combinatorial optimization, artificial intelligence, approximation, interval analysis, scheduling and production planning and supply chain management. He is the first and the corresponding author of several publications in refereed journals. He has been an administrative and scientific manager in several research projects, such as ANR, FUI, RISE, UTIQUE "France-Tunisia".
Prof. Safa Bhar Layeb is a Professor of industrial engineering and an active member of the OASIS Laboratory at the National Engineering School of Tunis (Tunisia). She holds a Polytechnic Engineering degree, a Master's degree in Mathematical Engineering, a Ph.D. in Applied Mathematics, and a university habilitation (HDR) in Industrial Engineering. She is the founding chair of the African Working Group in Health Systems and a member of the Executive Committee of the African Federation of Operations Research Societies (AFROS). She has served as a guest editor and reviewer for over twenty international journals. She has co-authored over thirty papers in reputable indexed journals, sixty papers in international conferences, and twenty book chapters. Her teaching and research interests include operations research, optimization, and data science approaches and their applications in healthcare organizations, industrial engineering, logistics, and supply chain management.
Prof. Ahmed Frikha is a Full Professor of operational research at the Higher Institute of Industrial Management, University of Sfax (Tunisia), in which he occupied various administrative positions as its director. His main research interests are multi-criteria decision aid, logistics and optimization, mathematical programming, and artificial intelligence. He participated in many research projects, published various papers in international journals, and supervised several PhD theses. His teaching activities are focused on operational research, mathematical programming, decision theory, information theory, manufacturer systems, production management, and project management.
Prof. Hela Moalla is a Full Professor in Quantitative Methods at the Higher Business School, University of Sfax (Tunisia). Her main research interests are multi-criteria decision aid, disaggregation approaches, mathematical programming, imperfection modelling, and logistic problems. She is the author of some papers in international journals, working papers and communications. Her teaching activities and interests are focused on linear programming, linear integer programming, decision theory, game theory, and data analysis.
Content
Paper 01: Integrating Machine Learning and Multi-Criteria Decision-Making for Strategic Organizational Planning.- Paper 02: Random Forest-Guided Spherical Fuzzy AHP for Group-Decision Making Under Uncertainty.- Paper 03: Two-Stage Deep Reinforcement Learning Approach to Solving the Physician Scheduling Problem.- Paper 04: Machine Learning-Based Evapotranspiration Prediction and Irrigation Optimization in Tunisia.