Dr. Jalel Euchi is an Associate Professor at the University of Sfax’s Higher Institute of Industrial Management, and an active researcher in the OLID Laboratory in Sfax, Tunisia. He earned his Ph.D. in 2011, jointly from Sfax University and Le Havre University (France), specializing in optimization and transportation problems. Earlier, he completed an M.Sc. in Operational Research & Production Management (2007) and a B.Sc. in Operational Research (2005), both at the University of Sfax.

Dr. Euchi has authored extensively on complex vehicle routing problems, heuristics, metaheuristics, and computational operations research, with publications appearing in respected outlets such as 4OR, IJOR, Energy Reports, Renewable & Sustainable Energy Reviews, and Swarm and Evolutionary Computation. He has co-authored influential works on hybrid optimization techniques for home healthcare routing, electric vehicle logistics, drone-assisted delivery, and multi-attribute decision-making frameworks. A notable contribution is his 2020 Springer paper, “A Hybrid Approach to Solve the Vehicle Routing Problem with Time Windows and Synchronized Visits in In-Home Health Care,” where he applies artificial intelligence methods to optimize caregiver schedules.

In addition to his research, Dr. Euchi is an active peer reviewer for several Springer, IEEE, and Elsevier journals, and a life member of the Operational Research Society of Tunisia His current interests encompass stochastic and distributed optimization, decision-making, logistics, deep and machine learning (including CNNs, GANs, Bayesian approaches), with applications to sustainable transportation systems like EVs and drones.

A recognized scholar with over 1,400 citations on ResearchGate, Dr. Euchi’s work continues to influence both theoretical development and real-world applications within operations research and industrial management .

Dr. Jalel Euchi’s research interests center on operations research, optimization, and intelligent decision-making systems with real-world industrial and societal applications. He specializes in the development and application of exact, heuristic, and metaheuristic algorithms to solve complex transportation and logistics problems—particularly vehicle routing, home healthcare delivery, drone scheduling, and electric vehicle networks. His work integrates classical operations research techniques with modern machine learning tools, including convolutional neural networks (CNNs), Bayesian models, and generative adversarial networks (GANs), to improve the efficiency and sustainability of logistics systems. Dr. Euchi is also deeply engaged in multi-criteria decision analysis (MCDA), stochastic modeling, and distributed optimization, with a focus on applications in smart cities, supply chain resilience, and sustainable energy systems. His interdisciplinary approach bridges theoretical models and practical solutions, advancing research in computational intelligence, industrial management, and data-driven optimization. His current focus also includes digital twins and AI-powered optimization for autonomous systems.