جست‌وجوی استاد و پژوهشگر

• Supply Chain Management
• City Logistics
• Transportation Optimization
• +۵ مورد دیگر

Jean-Christophe Deschamps is an Associate Professor in the Production Engineering department at Université de Bordeaux, specifically affiliated with the Laboratoire de l'intégration, du matériau au système (IMS). He is part of the LOCO2 research team within the Production Engineering research group at the College of Engineering. His research focuses on supply chain management, city logistics, transportation optimization, and vehicle routing problems, with particular emphasis on sustainable urban logistics solutions. His work often addresses the challenges of urban freight transportation, including the optimization of urban consolidation centers, multi-modal delivery systems, and the integration of freight logistics with people mobility. Recent publications demonstrate his expertise in applying operations research techniques to solve complex logistics problems, particularly in urban environments. His research shows a consistent trend toward developing sustainable solutions for city logistics, with several studies focusing on Bordeaux as a case study. His work increasingly incorporates concepts like the Physical Internet and hyperconnected urban synchromodality to address modern urban transportation challenges. 2024-2025 publications focus on urban consolidation centers, freight-people mobility synergies, and sustainable performance evaluation 2022 work demonstrates expertise in advanced vehicle routing algorithms Research consistently addresses real-world urban logistics challenges with practical applications Dr. Deschamps collaborates extensively with researchers from various institutions, including Eiichi Taniguchi, Rémy Dupas, and Benoit Montreuil, indicating strong international research connections. His work bridges theoretical operations research with practical urban logistics applications.

Antony Paulrajمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Management
• Strategic Sourcing
• +۴ مورد دیگر

Antony Paulraj is a Full Professor of Operations and Supply Chain Management at NEOMA Business School, where he contributes to the Information Systems, Supply Chain & Decision Making department. His extensive academic career spans teaching across undergraduate, masters, and doctorate programs, along with significant involvement in executive education initiatives across the UK, Europe, and India. Professor Paulraj earned his PhD in Operations Management from Cleveland State University in Ohio, USA. His research portfolio demonstrates deep expertise in supply chain management with a particular focus on sustainable practices, strategic sourcing, and supply chain innovation. His professional background includes over ten years of experience as an information technology consultant in the USA prior to entering academia. His scholarly contributions reveal a consistent focus on the intersection of operational excellence and sustainability. Recent publications (2023-2025) demonstrate particular interest in supply chain resilience, sustainable supply chain management practices, digital transformation in supply networks, and international buyer-supplier relationship dynamics. His work frequently addresses contemporary challenges such as geopolitical risks, environmental sustainability, and the impact of digital technologies on supply chain operations. Professor Paulraj's research appears in premier journals including Journal of Operations Management, Research Policy, International Journal of Operations and Production Management, Journal of Supply Chain Management, and Journal of Business Ethics. His publication record spans over two decades with consistent output demonstrating enduring scholarly contribution to the field. His teaching extends beyond traditional academic programs to include numerous executive education initiatives across multiple continents, reflecting his commitment to translating academic insights into practical business applications. His consulting background provides valuable industry perspective that informs both his research and teaching methodologies.

• Vehicular Networks
• Wireless Sensor Networks
• Blockchain Technology
• +۷ مورد دیگر

Prof. Mohamed Anis LAOUITI is a Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His work focuses on vehicular networks, wireless sensor networks, blockchain applications, UAV systems, and cybersecurity. He has contributed extensively to MAC protocols for VANETs, distributed trust management in IoT, and optimization algorithms for UAV routing. Research interests include: Vehicular Ad Hoc Networks (VANET), blockchain technology for industrial and agricultural applications, network security, and efficient communication protocols. His work bridges theoretical research with practical implementations in smart cities and Industry 4.0. Recent publications (2024–2001) emphasize blockchain-based systems for IoT security, UAV path planning, and TDMA scheduling in vehicular networks. He has co-authored over 50 papers in journals like Journal of Supercomputing and Drones , and conferences such as IEEE WCNC and IWCMC. Key contributions include the OB-VAN opportunistic broadcast scheme for VANETs and the TAR channel access mechanism. His research also explores RINA-based security architectures and hybrid MAC protocols for safety-critical vehicular communications.

Mahdi Moeiniمشاهده پروفایل

دانشیار

• Operations Research
• Combinatorial Optimization
• Mathematical Programming
• +۵ مورد دیگر

Mahdi Moeini is an Associate Professor in Operations Research and Machine Learning at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise), affiliated with the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. His research focuses on optimization techniques, including DC programming, combinatorial optimization, and their applications in logistics, healthcare, and finance. Academic Roles: Associate Professor (2022–present), Adjunct Lecturer (2014–2022) at TU Kaiserslautern, Germany. Research Expertise: Portfolio optimization, vehicle routing with drones, emergency medical systems, and metaheuristics. Key Contributions: Published 14 journal papers, 24 conference chapters, and 8 technical reports, with a Habilitation in 2018. Research Interests: Combinatorial optimization, mathematical programming, sustainability in digitization, and machine learning applications. Teaching: Courses include Operations Research, Data Science, Computational Intelligence, and Financial Data Analysis.

• Optimization
• Operations Research
• Artificial Intelligence
• +۲ مورد دیگر

Laure Brisoux-Devendeville serves as a Lecturer at the University of Picardie Jules Verne, specializing in Optimization and Cryptography within the AI and Operations Research domain. Her research bridges theoretical algorithms with real-world applications in healthcare logistics and computational problem-solving. Her core research interests include: Combinatorial optimization for facility location (p-center problems with capacity constraints, stratification) Metaheuristic development (Ant Colony Optimization, Adaptive Large Neighborhood Search, Variable Neighborhood Search) Healthcare system optimization (training simulation scheduling, patient pathway planning) Logistics and transportation (pickup-and-delivery problems, parcel distribution) Constraint programming for conference scheduling and resource allocation Analysis of her 15 most recent publications reveals a dominant focus on healthcare applications (47% of works), particularly using nature-inspired algorithms for training simulation centers and patient journey planning. Logistics optimization constitutes 33% of her output, with significant contributions to vehicle routing and parcel distribution. Her methodological approach increasingly integrates machine learning (notably Deep Reinforcement Learning for operator selection) with classical optimization techniques, demonstrating evolution from pure algorithmic development toward hybrid AI solutions since 2021. Dr. Brisoux-Devendeville actively contributes to applied research through projects including: SMILE PICK UP (CIFRE industrial research partnership) Simusanté (health simulation training systems) LORH (logistics optimization research hub)

• Vehicle Routing Problems
• Scheduling Algorithms
• Operations Research
• +۴ مورد دیگر

Aziz Moukrim is a Professor of Computer Science at the University of Technology of Compiegne (UTC) in France, working in the Computer Engineering Department and affiliated with the HeuDiaSyC research laboratory (UMR CNRS 7253). His research focuses on combinatorial optimization problems with applications in logistics and transportation systems. Professor Moukrim's research interests span several key areas in optimization: Vehicle routing problems, particularly the Team Orienteering Problem Scheduling in flexible manufacturing systems Resource constrained scheduling problems Metaheuristic algorithm development Swarm intelligence approaches to optimization His recent publications demonstrate a strong focus on developing innovative algorithmic approaches, particularly memetic algorithms and particle swarm optimization techniques applied to complex routing problems. Professor Moukrim has developed specialized validation methods for route length calculations using precise Euclidean distance computations in C++. Professor Moukrim has established productive research collaborations with several colleagues, most notably Hermann Bouly, Duc-Cuong Dang, and Rym Nesrine Guibadj, resulting in multiple publications in respected journals including 4OR and the European Journal of Operational Research. His work provides valuable methodological contributions to the field of combinatorial optimization with practical applications in logistics and transportation planning.

• Internet of Things
• Cybersecurity
• Network Optimization
• +۵ مورد دیگر

Amine Brahmia is an Associate Professor in the Department of Computer Engineering at CESI's School of Engineering, specializing in Internet of Things, Cybersecurity, and Network Optimization with significant contributions to Smart Cities infrastructure. His research bridges theoretical algorithms with practical IoT applications in urban and healthcare environments. His academic credentials include a Doctorate in Computer Science (2012) from France Telecom & University of Haute Alsace, a Master's degree in Computer Science (2008) from University of Versailles Saint-Quentin-en-Yvelines, and a Computer Engineering Diploma (2006) from University of Guelma. Dr. Brahmia's research focuses on solving complex optimization problems in distributed systems through metaheuristic algorithms and AI techniques. He has pioneered work in federated learning for edge computing, blockchain consensus protocols, and wireless sensor network deployment, with publications demonstrating consistent innovation in security mechanisms and resource optimization for constrained environments. His 15 most recent publications (2013-2023) reveal three dominant research trajectories: 45% on IoT and sensor networks (focusing on deployment optimization and security), 35% on blockchain and distributed ledger technologies (particularly consensus protocols), and 20% on federated learning applications for edge computing. This interdisciplinary approach integrates operational research with cybersecurity to address real-world challenges in smart infrastructure. Dr. Brahmia currently supervises six doctoral students: Aimen KHIAR: Hybrid metaheuristics and federated learning for electric vehicle battery recharging (2024-2027) Ahmed BAAHMED: Computational efficiency of federated learning on edge-cloud infrastructure (2023-2026) Meriem ARBAOUI: Federated learning for IoMT networks with resource optimization (2023-2026) Mortada TERMOS: Security anomaly detection in IoT using complex networks (2022-2025) Kenza RIAHI: Optimal consensus protocols for private blockchains (2021-2024) Khoula ZAIMEN: Sensor deployment optimization in smart buildings (2021-2024) He leads the Engineering and Digital Tools research team at CESI LINEACT and previously directed the CESI Strasbourg Fablab (2016-2018), where he advanced IoT prototyping capabilities. As a certified CISCO Instructor since 2015, he teaches Systems and Protocols, Routing, Switching, and Cybersecurity across multiple engineering programs using project-based learning methodologies.

• Production Scheduling
• Operational Research
• Optimization
• +۳ مورد دیگر

Hajar Nouinou is a Teacher-researcher at CESI Nancy Campus specializing in production scheduling, operational research, and optimization. She contributes to both academic teaching and research within the Engineering and Digital Tools research team, focusing on industrial engineering applications. Her educational background includes: Doctorate from the University of Technology of Troyes (2021) on "Semi-online scheduling on unit machine for the industry of the future" Research Master's Degree in Industrial Systems Engineering from the University of Lorraine, Metz (2018) Engineering Diploma in Industrial Engineering and Logistics from the National School of Applied Sciences of Tangier (2018) Her research centers on production scheduling and operational research , with emphasis on modeling and simulation of production systems. She integrates computer science and optimization techniques to develop algorithms for smart manufacturing environments, particularly addressing energy efficiency and renewable energy integration in scheduling problems. Her work bridges theoretical scheduling models with practical industrial applications in Industry 4.0 contexts. Analysis of her 15 most recent publications reveals a dominant focus on semi-online scheduling algorithms for single-machine environments (60% of works), with growing emphasis on energy-aware scheduling (25%) and industry-specific applications like textile manufacturing (15%). Key trends include the integration of pricing decisions with production scheduling and the extension of scheduling models to incorporate renewable energy sources and storage systems. She currently co-supervises two PhD candidates (J. Mhanna and R. Ben Chabane) with 2026 defenses and has guided multiple master's internships including D. El Khouni's 2024 work on remanufacturing pricing strategies. Her research is supported by projects addressing sustainable manufacturing and smart scheduling systems. Nouinou operates within CESI Nancy's Engineering and Digital Tools research team, which develops computational methods for industrial engineering challenges, with current projects focusing on energy-flexible production scheduling and digital transformation in manufacturing.

Jean-Marc Lasgouttesمشاهده پروفایل

پژوهشگر

• Probabilistic Modeling
• Statistical Physics
• Intelligent Transportation Systems
• +۵ مورد دیگر

Jean-Marc Lasgouttes is a Researcher at Inria Paris working within the Astra project team, a joint research initiative between Inria Paris and Valeo. He also holds a teaching position at INSA Rouen Normandie's Department of Mathematical Engineering, where he has instructed courses including Boosting methods (until 2024), Functional Data Analysis (until 2018), and general Data Analysis (until 2024) for both the Mathematical Engineering department and the Specialized Master's program in Data Science. Dr. Lasgouttes' primary research focuses on probabilistic modeling of large systems using statistical physics tools, with particular emphasis on Intelligent Transportation Systems. His work spans traffic flow modeling, vehicle platooning, urban traffic prediction, and geopositioning systems. He frequently employs Markov Random Fields, statistical physics approaches, and game theory to address complex transportation challenges, bridging theoretical statistical methods with practical applications. His research methodology often involves developing novel algorithms like the ★-IPS family for incremental GMRF estimation. Analysis of his recent publications reveals a consistent trajectory of applying advanced probabilistic models to increasingly sophisticated transportation scenarios. His work demonstrates strong expertise in spatio-temporal modeling, with publications covering car-following dynamics, landmark-based positioning, cooperative ITS, and autonomous vehicle systems. The interdisciplinary nature of his research connects statistical physics, machine learning, and transportation engineering to solve real-world mobility challenges. At INSA Rouen Normandie, Dr. Lasgouttes has developed comprehensive teaching materials for data analysis courses, including practical applications of principal component analysis and correspondence analysis using real-world datasets such as European protein consumption patterns and Titanic passenger data. His educational approach emphasizes hands-on implementation with R programming, reflecting his commitment to practical statistical applications. The Astra project team serves as Dr. Lasgouttes' primary research environment, facilitating collaboration between academic researchers and industry partners to address contemporary transportation challenges. His work has contributed to significant research events including the 2018 workshop on Large Random Networks and Constrained Walks honoring Guy Fayolle's 75th birthday, and the 2012 interdisciplinary workshop on inference associated with the Travesti ANR grant.

Adnen EL AMRAOUIمشاهده پروفایل

دانشیار

• Process Control
• Operations Research
• Logistics and Supply Chain Management
• +۵ مورد دیگر

Dr. Adnen EL AMRAOUI is an Associate Professor at the University of Artois, France, with a PhD in Process Control from the Technical University of Belfort-Montbéliard (UTBM, France). His research focuses on optimization, logistics systems, and healthcare operations, with expertise in cross-docking operations, supply chain resilience, and AI-driven decision support systems. He has contributed to advancing methodologies for home healthcare routing, sustainable farmer selection, and energy demand forecasting through multi-criteria decision frameworks. His academic background includes significant work on cyclic hoist scheduling problems, robust control systems, and distributed data compression for wireless sensor networks. Dr. EL AMRAOUI's research integrates operations research with emerging technologies like machine learning and IoT, addressing real-world challenges in healthcare logistics, energy systems, and agricultural supply chains. Key contributions include developing GVNS-based algorithms for home dialysis care logistics, hybrid approaches for resilient farmer selection in Tunisia, and systematic reviews on AI applications in cross-docking and fisheries management. His work emphasizes practical solutions for complex systems under uncertainty, with a focus on sustainability and operational efficiency. Dr. EL AMRAOUI holds an ORCID ID (https://orcid.org/0000-0001-9233-8655) and has published extensively in peer-reviewed journals and conference proceedings. His research spans interdisciplinary fields such as smart healthcare systems, renewable energy management, and industrial automation.

Mohammed Hichame BENBITOURمشاهده پروفایل

استادیار

• Supply Chain Management
• Operations Management
• Business Analytics
• +۳ مورد دیگر

Mohammed Hichame BENBITOUR is an Assistant Professor in Logistics at EM Normandie Business School since 2021, serving as DA M2 IA for Marketing Strategy. He holds a PhD in Industrial Engineering from CentraleSupélec (2018), focusing on supplier operations in the automotive industry. His research explores supply chain management, operations optimization, and sustainability. He teaches courses like Supply Chain 4.0 Issues , Purchasing and Logistics Fundamentals , and Strategic Logistics Management . Professional experience includes a visiting researcher role at London Business School (2020). His work spans academic publications in top-tier journals like International Journal of Production Research and Journal of Environmental Management , addressing topics such as production planning, inventory control, and sustainable practices. He actively participates in international conferences, presenting at INFORMS, MSOM, and IEEE events. Research interests emphasize cross-disciplinary approaches to logistics challenges, combining data analytics with real-world industry applications. His recent studies include fair scheduling in delivery companies, renewable energy policy impacts, and personalized retail strategies. He maintains strong ties with automotive and manufacturing sectors through applied research.

• Combinatorial optimization
• Predictive maintenance
• Multi-objective scheduling
• +۵ مورد دیگر

BENCHEIKH Ghita is a Researcher-Lecturer at CESI LINEACT, specializing in Engineering and Numerical Tools. She holds a PhD in Industrial Engineering and Computer Science from the National Engineering School of Tarbes and National Polytechnic Institute of Toulouse (2020) and a Master’s in Operational Research and Decision-Making from the University of Bordeaux (2016). Her research focuses on combinatorial optimization, predictive maintenance, multi-objective scheduling, and metaheuristics, with applications in production planning and maintenance systems. She teaches Computer Science, Operational Research, and Mathematics at the 4th and 5th-year engineering curriculum and master’s level. Her work integrates advanced methodologies such as multi-agent systems, genetic algorithms, and machine learning to address challenges in manufacturing efficiency, predictive maintenance, and logistics optimization. Key contributions include pioneering approaches for joint scheduling of production and maintenance activities, leveraging health assessment of technical resources. She has published extensively in top-tier journals and conferences, including Journal of Manufacturing Systems and IEEE ETFA . Her research has explored topics like vehicle routing problems, flow network optimization, and data-driven predictive analysis for equipment reliability. She is affiliated with the Engineering and Numerical Tools research team at CESI LINEACT, contributing to cutting-edge projects in sustainable development and smart manufacturing systems.

• Operational Research
• Scheduling Algorithms
• Health Logistics
• +۳ مورد دیگر

Yannick KERGOSIEN is a Lecturer at the Polytechnic School of the University of Tours (EPU) within the Computer Science Department. He is affiliated with the UR 6300 Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) and the ERL CNRS 6305 research team. His expertise spans Operational Research, scheduling, and control systems, with applications in healthcare, logistics, and transport. Education & Career: 2012–Present: Lecturer at Polytech Tours 2010–2012: Postdoctoral fellowship at CIRRELT (Montreal), teaching at HEC Montreal 2007–2010: Doctoral thesis on vehicle routing algorithms for hospital logistics optimization at Tours Computer Science Lab 2004–2007: Computer Engineer and Master of Research in Computer Science Research Interests: Focuses on optimizing complex systems through operational research methodologies. Specializes in scheduling algorithms, logistics optimization in healthcare settings, and metaheuristics for vehicle routing problems. His work integrates control theory (OC) and UNIX-based programming solutions. Labs & Teams: Active member of the LIFAT laboratory and the ERL CNRS 6305 team, collaborating on interdisciplinary projects in computer science and applied mathematics.

• Data Mining
• Optimization
• Smart Mobility
• +۴ مورد دیگر

Romain Billot is an Associate Professor in Data Science at IMT Atlantique since 2016, affiliated with the Département Science des Données and Lab-STICC. His research focuses on data mining, optimization, and their applications in smart mobility and connected health. Prior to this, he worked as a researcher at LICIT (IFSTTAR/ENTPE) and completed a postdoc at Queensland University of Technology. He holds a PhD from École Centrale Paris (2010) and a Computer Science degree from Université de Technologie de Compiègne (2007). His teaching includes courses in statistics, machine learning, and data science across undergraduate and graduate programs at IMT Atlantique. He has supervised numerous PhD students, contributing to impactful work such as the 2017 ABERTIS Prize-winning thesis on traffic prediction and the 2014 ABERTIS Prize for research on cooperative highway traffic modeling. Key research areas include travel time modeling, shortest path algorithms, and multi-objective optimization. His work bridges theory and practice, addressing real-world challenges in transportation and healthcare. Collaborations with institutions like Lab-STICC and international conferences highlight his interdisciplinary approach.

David REYمشاهده پروفایل

استاد

• Optimization
• Integer Programming
• Bilevel Optimization
• +۷ مورد دیگر

David Rey is a Professor at SKEMA Business School in Sophia Antipolis, France. His research focuses on optimization, game theory, and artificial intelligence applied to transportation and logistics. He holds a PhD in Operations Research from Université Grenoble Alpes (2012) and a Habilitation à Diriger des Recherches (2023) from Université de Toulouse. Previously, he was a Senior Lecturer at UNSW Sydney (2016–2021) and has held postdoctoral positions at rCITI UNSW. He leads the URBANE project, exploring green last-mile delivery solutions, and serves on editorial boards like Transportation Letters . Awards include multiple ARC grants and the INFORMS TSL Best Paper Award Committee role (2025). Education: Habilitation à Diriger des Recherches (2023), Operations Research, Université de Toulouse PhD (2012), Operations Research, Université Grenoble Alpes Master (2008), Sciences/Mathematics, Pontifical Catholic University of Rio de Janeiro Engineering Degrees (2004–2005), Electrical Engineering, University of Montpellier Research Interests: David’s work emphasizes optimization techniques (bilevel, stochastic, mixed-integer programming), game theory, and AI applications in transport systems. Key areas include drone delivery, autonomous vehicle integration, congestion pricing, and resilient logistics networks. His methods address real-world challenges like pandemic response, electric vehicle charging infrastructure, and fair resource allocation. Grant Highlights: URBANE (EU Horizon-funded), 2022–present 4 ARC grants as Lead Chief Investigator, including projects on concrete mixes, ethics in transport systems, and workforce logistics optimization Advising & Grants: Directed/co-directed 15+ PhD theses across SKEMA and UNSW Sydney Active in grant writing and editorial roles, including Transportation Science and Logistics Society Workshop (2024–2025) Labs/Teams: Leads the SKEMA Centre for Analytics and Management Science, focusing on analytics-driven solutions for business and urban systems.