Session 1 : AI-based analytical and intelligent techniques for supply and production planning under uncertainty.

Dr. O. BEN-AMMAR (IMT Mines Alès, France), Dr. B. BETTAYEB (CESI LINEACT, France), Dr. I. SLAMA (CentraleSupélec, France), Prof. A. DOLGUI (IMT Atlantique, France), Prof. Z. JEMAI (CentraleSupélec, France), Prof E. SAHIN (CentraleSupélec, France)

Managing uncertainty and risks is becoming one of the most important challenges in Supply Chain (SC) optimization. In fact, uncertainty causes several difficulties in supply and production planning, and inventory control. The sources of uncertainty are various and can take place at several levels of the SC: lead times, demand, prices, yield and capacities, etc. Recently, Artificial Intelligence tools have become an important mean used to improve decision support systems at strategic, tactical and operational levels in uncertain environments.

This invited session aims to promote new classical and AI-based research concepts, approaches and achievements for managing uncertainty at different levels of the Supply Chain.

Keywords: Uncertainty, Artificial Intelligence, Optimization, Simulation, Supply, Production, Manufacturing Systems

Session 2 : Operations Research for Health Care

Dr. S. MAKBOUL (UTT, France), Prof. A. YALAOUI (UTT, France).

Recent events, notably the COVID-19 pandemic, have highlighted significant logistical challenges in healthcare. As a result, healthcare centers are being urged to streamline operations to improve overall performance, particularly in the management of material flows within services. The objective of this session is to bring together the latest contributions and innovative approaches for addressing a wide range of operations research challenges in healthcare, including hospital management, logistics, staffing, home healthcare, and health networks. Participants are encouraged to share their most recent and innovative research findings and practical experiences. The session will focus on modeling and solving both deterministic and stochastic healthcare problems, using metaheuristics and exact algorithms to enhance the complex management of material and human flows in healthcare.

Keywords: Operating rooms management, healthcare logistics, material flow optimization, metaheuristics, optimization under uncertainty, home healthcare, healthcare networks.

Session 3 : Deep learning for smart intelligent transportation systems.

Prof. A. NAKIB (UPEC, France)

The special session on Deep Learning based intelligent transportation systems highlights cutting-edge research leveraging Artificial intelligence, machine learning, and Deep Learning to tackle real-world transportation challenges. It invites contributions across diverse areas, ranging from theoretical insights to practical applications and from academic studies to industrial implementations. Topics of interest include, but are not limited to, the following:

  • Video analysis for Incident detection
  • Roads’ and highways traffic Security
  • Tolling systems
  • Models to predict traffic congestion
  • Uncertain transportation data
  • Machine learning
  • Deep learning
  • Optimizing machine learning algorithms to predict traffic congestion
  • Multimodal analytics on transportation
  • Case studies

Session 4 : Machine learning and operation research for smart industry and services

Dr. O. SOUISSI (INPT, Morocco), Dr A. BEKRAR (Université de Valenciennes, France), Prof. H. ALLAOUI (Université d’Artois, France)

Machine Learning and Operations Research for Smart Industry and Services aims to advance the integration of data-driven decision-making within industrial and service-based environments, enhancing operational efficiency and strategic insights. While traditional industry approaches often rely on rigid systems and rule-based methods, the emerging use of machine learning and operations research offers dynamic adaptability, predictive capabilities, and optimization across diverse applications. This session emphasizes the synergy between advanced analytical models and real-world industrial challenges, showcasing the pivotal role of machine learning and optimization in crafting intelligent, responsive systems. Researchers and practitioners are encouraged to delve into methodologies that balance computational rigor with practical feasibility, addressing the increasing complexity of decision processes within modern industries.

This session will explore novel frameworks, methodologies, and case studies that demonstrate the transformative potential of machine learning and operations research within smart industry and service domains. Topics may include, but are not limited to:

  • Optimization models for enhanced production planning and scheduling.
  • Data-driven predictive maintenance strategies.
  • Algorithms for real-time decision-making in dynamic environments.
  • Impact assessments of AI and ML applications on operational efficiency.
  • Methods for managing uncertainty and variability in industrial systems.
  • Exact methods and Hybrid approaches for resource allocation and logistics optimization.
  • Models for integrating customer demand forecasting with supply chain management.
  • Hybrid approaches combining machine learning with traditional optimization techniques.
  • Applications of reinforcement learning for adaptive industrial operations.

This session invites researchers to contribute to the advancement of intelligent, sustainable systems that align technological capabilities with operational needs, supporting a new era of efficiency and innovation in smart industry and services.

Session 5 : : Machine learning Enhanced Metaheuristics for Solving Planning and Scheduling Problems

Dr. M. TOUAT (UTT, France), Prof. H. CHEN (UTT, France), Prof. K. BENATCHBA (ESI, Algeria)

This session presents recent advances in applying artificial intelligence techniques to the field of combinatorial optimization. We focus particularly on scheduling and planning problems across various contexts, including production workshops, healthcare institutions, and transportation networks. The speakers will present both practical and theoretical case studies, demonstrating how learning techniques, such as machine learning and deep learning, are used to design and improve metaheuristics at different stages of the process.

Keywords: Scheduling, planning, metaheuristics, machine learning, deep learning, reinforcement learning

Session 6 : Optimization of Health Management Systems

Prof. M. BOUDHAR (USTHB, Algeria), Dr. Y. OUAZENE (UTT, France), Prof. F. YALAOUI (UTT, France).

The management of a hospital is very complex; in addition to patients (patient appointments, scheduling of treatment rooms and operating rooms, patient transport, etc.) it manages very different product flows and distribution channels and constraints that meet strict safety and traceability standards. Health is one of the sectors that can offer many challenges of practical applications to the optimization and decision-making community.

This special session focuses on applications, algorithms and decision support related to health management systems. It aims to provide the audience with an overview of current research topics in operations research and statistics applied to the health domain. In addition, the session aims to stimulate new ideas and interesting discussions on the different applications of optimization techniques to health data.

Topics of interest in this invited session include, but are not limited to:

  • Hospital supply chain.
  • Supplier selection.
  • Reverse logistics.
  • Care planning.
  • Surgical planning.

Session 7 : AI-Driven Scheduling and Decision-Making Trends in Cobots-Integrated Industry 4.0 Systems

Prof. M. GAHAM (CDTA, Algeria)

Both artificial intelligence techniques and Cobots-integrated automation—such as Collaborative Robots (Cobots) and AMRs—have evolved significantly, particularly within the context of Industry 4.0. By leveraging both established and cutting-edge AI tools—such as machine learning and large language models for optimization—novel Industry 4.0 applications are expected to enhance efficiency and flexibility through real-time scheduling, resource allocation, and decision-making, thereby improving Cobots-integrated manufacturing systems. These advancements boost system performance, adaptability, and resilience, driving front-line applications such as efficient human-aware multi-robot collaboration and seamless interaction between Cobots and human operators in dynamic environments. This session will explore emerging trends, challenges, and innovations in AI-driven scheduling and decision-making applied to Cobots-integrated manufacturing systems, with a focus on ensuring sustainable and resilient operations in Industry 4.0.

Keywords: AI Optimization, Cobots, Scheduling and allocation, Decision-Making, Industry 4.0

Session 8 : Trends and advances in inventory control

Dr. M. GODICHAUD (UTT, France), Dr. H-N. NGUYEN (Université de Danang, Vietnam), L. AMODEO (UTT, France)

Inventory control has a rich historical background in scholarly literature, featuring well-established basic models that have undergone continuous expansion over the years. This session is designed to spotlight and deliberate on emerging trends and advancements in inventory control, encompassing areas such as reverse logistics, CO2 emissions, energy considerations, and the integration of financial components. Moreover, alongside stochastic models incorporating random variables related to demand, lead times, or quality, research focusing on inventory control to enhance supply chain resilience is anticipated. Given that inventory management serves as a pivotal strategy for mitigating disruptions, resilience has emerged as a prominent concern within supply chains. In a broader context, inventory control plays a central role in supply chains, and submissions exploring related topics are encouraged. Contributions may include literature reviews, novel models, or innovative methodologies.

The topics of interest include, but are not limited to: 

  • Inventory models with CO2 emissions or energy considerations,
  • Inventory models for closed-loop supply chain, reverse logistic
  • Inventory models with trade credit, delayed payment, pricing   
  • Stochastic models and methods for inventory management,
  • Supply chain resiliency and inventory,
  • Linear and non-linear programming, simulation, heuristics and metaheuristics for inventory control and supply chain

Session 9 : Recent Advancements in Intelligent Manufacturing and Logistics Systems.

Prof. F. BELKAID (University of Tlemcen, Algérie), Dr. R. BOUFELLOUH (University of Tlemcen, Algérie).

The constantly shifting market conditions and dynamic production environments introduce substantial uncertainties and challenges for managing operations effectively. To remain competitive, industries must address challenges such as resource scarcity, energy consumption reduction and effective logistics management. Artificial Intelligence (AI) and Reinforcement Learning (RL) have opened new possibilities for solving complex problems. The integration of AI with advanced optimization techniques is revolutionizing management practices in manufacturing and logistics. These technologies enable innovative and effective solutions for the design, development, optimization and management of manufacturing processes and systems, even under increasingly complex and unconventional constraints.

This special session invites researchers and practitioners to explore innovative approaches and applications leveraging AI, RL and other advanced methods to enhance efficiency, flexibility, and resilience in intelligent manufacturing systems and logistic. We also welcome original contributions that focus on the design and performance evaluation of production lines and Reconfigurable Manufacturing Systems. Topics of Interest Include but are not limited to:

  • Production scheduling under unconventional constraints
  • Integrated planning in production and logistics
  • Optimization of production line design, including line balancing and buffer sizing
  • Advanced modeling and optimization of manufacturing processes
  • AI techniques, multi-agent systems and collaborative approaches in manufacturing and logistics
  • Innovation in Modern Production and Reconfigurable Manufacturing Systems
  • Sustainable operations management in smart manufacturing systems

Session 10 : Metaheuristics for industrial problems solving

Dr. L. DEROUSSI (Clermont Auvergne University, France), Dr. N. GRANGEON (Clermont Auvergne University, France).

This session will focus on innovative applications of metaheuristics to real-world industrial challenges. Metaheuristics, such as genetic algorithms, particle swarm optimization, simulated annealing, ant colony optimization, and many others, offer robust frameworks for addressing complex optimization problems in sectors such as manufacturing, logistics, energy, transportation,...

We welcome original research papers and case studies that explore the design, implementation and evaluation of metaheuristics in industrial contexts. Topics of interest include, but are not limited to:

  • Production and supply chain optimization
  • Scheduling and resource allocation
  • Design and manufacturing process improvement
  • Energy-efficient solutions and sustainable practices
  • Robust and adaptive metaheuristics for dynamic environments

We encourage authors to submit contributions that demonstrate theoretical advances as well as practical applications, highlighting the impact of metaheuristics in industrial settings. We look forward to your submissions!

Keywords: Optimization, Metaheuristics, Industrial case studies.

Session 11: Green Networks for Sustainable Industry and Service Systems

Dr. A. J. TELMOUDI (ENSIT, University of Tunis, Tunisia), Dr. Y. OUAZENE (UTT, France)

This special session focuses on advancing green network solutions to enhance sustainability and efficiency across industrial and service sectors. With increasing environmental challenges and the growing demand for energy-efficient systems, these interconnected domains are essential for reducing environmental impact, optimizing resources, and fostering the transition to eco-friendly and resilient operations.

The session aims to bring together researchers, practitioners, and industry leaders to present innovative approaches, technologies, and strategies for designing, implementing, and managing green networks, sustainable supply chains, and efficient scheduling systems. It also provides a platform to discuss challenges, share insights, and explore future directions in building sustainable industrial and service ecosystems.

Topics of Interest:

  • Energy-efficient networks for industrial and service applications.
  • Green supply chain and logistics optimization.
  • Scheduling techniques for sustainable and resource-efficient operations.
  • AI-driven solutions for sustainable network management.
  • Integration of renewable energy in industrial and service networks.
  • Optimization techniques for reducing resource consumption.
  • Environmental impact assessment and lifecycle analysis of networks.
  • Policies and frameworks for supporting green networks, supply chains, and scheduling practices.

Session 12 : Less-than-truck load problem: advantages and challenges

Dr. O. OZTURK (Ottawa University, Canada), Dr. M. H AFSAR (UTT, France)

In the less-than-truckload problem, the vehicles are not fully loaded to have more frequent deliveries and smaller inventory levels. This strategy is usually more advantageous when companies focus on just-in-time delivery as well as reduced inventory costs. Moreover, when coupled with other factors prior to transportation such as production of different orders requiring different production times, order due dates, transportation costs determined by the used truck capacity, a less-than-truckload strategy may be more interesting for practitioners.  This session aims to present the recent advances in less-than-truckload transportation from both theoretical and practical perspective.

Session 13 : Logistics and urban mobility problems.

Dr. C. DUHAMEL (Le Havre Normandie university, France), Prof. N. LABADIE (UTT, France)

People mobility and the transportation of goods are profoundly affected by the growing congestion of urban transportation networks. Among the recent constraints are the establishment of low-emission zones and restricted traffic areas that limit access for some types of vehicles. This session will focus on models and methods aimed at rethinking urban logistics to address these challenges. Innovative solutions will be discussed, such as the integration of autonomous vehicles, the use of cargo bikes and robots, multimodal approaches, and multi-echelon delivery strategies combined with urban warehouses management.

Session 14 : Transportation and vehicle routing Problems.  GT2L session

Prof. P. LACOMME (Clermont Auvergne University, France), Prof. C. PRODHON (UTT, France)

This session focuses on recent methodological developments in solving complex problems related to transportation and vehicle routing. The challenges addressed include fleet management, the integration of environmental and economic constraints, as well as vehicle routing integrated with other problems such as location, inventory management, and production. Special emphasis will be made on the use of hybrid methods combining various learning and optimization techniques

Session 15 : Real Time and online problems in Transportation and Vehicle routing.

Dr. W. J. GUERRERO RUEDA (Universidad de La Sabana, Colombia), Prof. N. LABADIE (UTT, France)

This session aims to analyze the issues and challenges raised in vehicle routing and transportation problems within dynamic and real-time environments. It focuses on advanced models and solutions that integrate adaptive algorithms, artificial intelligence, and real-time fleet management systems.

Keywords: dynamic routing problems, real-time handling of unforeseen events (traffic congestion, breakdowns for maintenance routes, urgent requests, etc.), real-time optimization.

Session 16 : "Quantum Approximate Solving Methods.

Dr. W. COELHO (PASQAL, France), Prof. P. LACOMME (Clermont Auvergne University, France)

Quantum optimization represents an emerging field that introduces a new computing paradigm, making it possible to solve problems that were previously inaccessible. This session will focus on quantum metaheuristic approaches applied to operational research problems. Discussions will specifically cover the use of quantum algorithms to explore innovative solutions, as well as the opportunities and challenges related to integrating these cutting-edge technologies into real-world contexts.

Session 17 : Application of Quantum Technologies to Optimization.

Dr. J. MIKAEL (EDF, France), Prof. C. PRODHON (UTT, France)

Quantum technologies open up unprecedented opportunities in the field of optimization, providing tools able to solve complex problems in innovative ways. This session explores the practical applications of quantum computing to address optimization challenges, whether in logistics, resource management, or strategic planning. Special attention will be given to recent developments in quantum algorithms and their potential to transform classical optimization approaches.

Session 18 : Quantum Algorithms and Circuits, Quantum Artificial Intelligence

Prof. F. HHAIEN(UTT, France), Dr. H-M  AFASR (UTT, France), Prof. H. SNOUSSI (UTT, France)  et Prof. C. COUTEAU  (UTT, France) 

This session is dedicated to recent advances and trends in the fields of quantum optimization, quantum circuit design, and quantum artificial intelligence. It covers a variety of topics, including:

  • The development of exact, approximate, and quantum-inspired optimization algorithms,
  • Innovative approaches to the generation and implementation of efficient quantum circuits,
  • Challenges raised by error correction and mitigation, circuit complexity, and resource optimization,
  • The integration of artificial intelligence and quantum computing, with a focus on quantum machine learning and its potential applications.

Session 19 : Rich Vehicle Routing Problems

Dr A. YAHIAOUI (UTT, France) , Prof M. RÖNNQVIST (CIRRELT, Canada)

Vehicle routing problems have been the focus of many researchers in the field of operations research and combinatorial optimization. This session focuses on rich vehicle routing problems with diverse constraints such as time windows, synchronization, queuing, etc., with applications in different areas of logistics, such as urban transportation, deliveries, last mile distribution, healthcare, natural resources, etc. The session is open to contributions on modeling techniques as well as exact and approximate resolution approaches for VRPs. Innovative resolution methods based on AI and learning techniques are particularly encouraged.

Keywords : Vehicle routing problems, synchronization, time windows, queuing, metaheuristics, AI techniques, learning.

Session 20 : Industrial Internet of Things (IIoT) in Supply Chain Management: Transforming Operations for the Future

Prof. A. CHAABANE (ÉTS Montréal, Canada), Prof. F. HNAIEN (UTT, France), Prof. R. LARBI (ÉTS Montréal, Canada), Prof. N. BAHRIA (École nationale supérieure des ingénieurs de Tunis - ENSIT, Tunisie)

The Industrial Internet of Things (IIoT) revolutionizes supply chain management by integrating smart devices, real-time data, and advanced analytics into operational processes. IIoT enhances supply chain efficiency, resilience, and sustainability, offering innovative solutions to complex industry challenges. The special session will focus on exploring the transformative impact of IIoT on supply chain systems.

This Special Issue aims to bring together researchers, practitioners, and industry leaders to share insights, methodologies, and applications that advance the state of the art in IIoT-enabled supply chains. We invite original contributions that address theoretical, practical, and industrial aspects of IIoT in supply chain management.

Topics include, but are not limited to:

  • IIoT-Driven Supply Chain Innovation: Real-time asset tracking and condition monitoring; Predictive maintenance leveraging IIoT data; Autonomous warehousing and inventory systems.
  • Optimization and Analytics in IIoT-Enabled Supply Chains: Advanced analytics for IIoT-based supply chain decision-making, Optimization algorithms for IIoT-integrated logistics systems, Machine learning and AI for IIoT applications in supply chains.
  • Resilience and Risk Management with IIoT: IIoT solutions for risk detection and mitigation, Enhancing supply chain resilience through IoT technologies, IIoT for real-time disruption management and recovery planning.
  • IIoT and Sustainability: IIoT-enabled circular economy practices in supply chains, Energy-efficient and environmentally conscious logistics using IIoT, Carbon footprint reduction through IIoT applications.
  • Emerging Technologies in IIoT Supply Chains: Digital twins for IIoT-enabled supply chain modeling, Integration of blockchain with IIoT for transparency and security, Role of 5G, edge computing, and cloud platforms in IIoT ecosystems.
  • Case Studies and Practical Applications: Industry case studies on IIoT implementation in supply chains, Challenges and lessons learned from IIoT adoption, Sector-specific applications in manufacturing, retail, healthcare, and more.

Keywords:  Industrial Internet of Things (IIoT), Supply chain management, Smart devices, Real-time data, IIoT-enabled supply chains, Digital twins, Blockchain integration, 5G technology, Edge computing, Cloud platforms.
 

Session 21 Sustainable and Intelligent Manufacturing Systems: Bridging Industry 4.0 and Sustainability

Dr. T. BENAZZOUZ (National School of Applied Sciences (ENSA) of Marrakech, Cadi Ayyad University, Morocco). Dr. S. DAHBI (National School of Applied Sciences (ENSA) of Marrakech, Cadi Ayyad University, Morocco)

Brief Description:

This special session addresses the intersection of Industry 4.0 technologies and sustainable manufacturing practices. It aims to showcase advancements in smart production systems, additive manufacturing, energy-efficient processes, and green manufacturing. Participants will explore how digitalization, AI, IoT, and robotics can drive sustainability in manufacturing, reducing environmental impact while improving productivity and operational efficiency.

Keywords: Smart Manufacturing Systems; Sustainability; Green Manufacturing; Circular Economy Practices; Sustainable Systems/ Applications/ Manufacturing; Lean Production Methods; Additive Manufacturing; Artificial Intelligence; Digital Twin; Hybrid Products.

Date of update 07 février 2025