Workshop and Special session proposals :
March 15th, 2023 ==> March 30th, 2023
Invited sessions consist of 5-6 papers of the regular format focusing on targeted subjects and presenting a unifying theme. Invited session organizers should submit an abstract that summarizes the aim and the content of the invited session. Please refer to the proposed template below. A code will be assigned to such a session. The invited author should submit their papers by using this code, example as: « ss1: your title ». If at least 5 papers are accepted, the session is included in the conference program as an invited session. If an invited session is not accepted, accepted papers submitted to this session will be included in regular sessions. Invited sessions, grouped together in a track must be submitted independently. The track chairs are asked to contact the organizers prior to session proposal submissions.
CONFIRMED SPECIAL SESSIONS
Description: Unmanned Vehicles, including Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vehicles (USVs), and Automated Guided Vehicles (AGVs), are emerging as game-changing technologies that have the potential to revolutionize the industrial field. These vehicles are often equipped with sensors, cameras, and other advanced technologies to perform certain tasks with high precision, accuracy, and low risk. The innovative use of unmanned vehicles brings numerous benefits, such as increased efficiency, cost reduction, improved safety, greater accuracy and consistency, access to hard-to-reach areas, environmental benefits. They are thus increasingly being used in a variety of applications in the industrial field. The aim of this session is to provide a platform for researchers and practitioners to share their latest findings and developments in unmanned vehicle-aided routing and scheduling, and to discuss the challenges and opportunities in this field. We believe that this session will inspire new ideas, collaborations, and innovations that will help to shape the future of the industrial field.
- Dr. Yantong Li, School of Maritime Economics and Management, Dalian Maritime University, China
- Dr. Yipei Zhang, School of Economics and Management, Chang’an University, China
- Dr. Qianli Ma, School of Maritime Economics and Management Dalian Maritime University, China
Description: In this session we will focus on security analysis and supervisory control of discrete event systems (DESs). There are several DES modelling frameworks such as Petri nets, automata, process algebras, rewriting logic, markov chains, Queues, max-plus algebras. DES models can be used to describe many kinds of systems, such as manufacturing systems, transportation systems, logistic systems, database management systems, communication protocols, computer communication networks, distributed software systems, and cyberphysical systems. The rapid and expeditious development of information and network technology makes the security and reliability of a discrete event system be the key to its survivability. Recently, much work has been conducted in the security analysis in the context of control of discrete event systems. The aim of this session is to present recent advances in the modeling, security analysis, control, as well as performance evaluation of discrete event systems. Prospective authors are invited to share their academic results and practical experiences to deal with these challenging issues in this area.
- Dr. Gaiyun Liu, School of Electro-Mechanical Engineering, Xidian University, China
- Dr. Kamel Barkaoui, The Cedric Lab and Computer Science Department, Conservatoire National des Arts et Métiers, France
- Dr. Hervé Marchand, INRIA Rennes-Bretagne Atlantique Campus Universitaire de Beaulieu, France
- Dr. Zhiwu Li, School of Electro-Mechanical Engineering Xidian University, China
Description: In today’s highly competitive markets, sustainability is quickly becoming a priority for manufacturing companies to consider in addition to time, quality, and cost. A sustainable future requires a holistic view of systems: environmental, economic, and social factors must be considered to meet the needs of the present without compromising the ability of future generations to meet their own needs. Many environmental laws and regulations are already in place today, such as increasing electricity tariffs and fuel prices, carbon footprint reduction and energy consumption, which already show the importance of the issue. The manufacturing industry, as a major consumer of energy and resources, must adapt to the new circumstances and break new ground. Therefore, a comprehensive approach that considers both the product and, in particular, the processes required for its production is needed. Furthermore, new strategies and business models are needed to lead companies into a sustainable future. Moreover, Manufacturing companies are increasingly adopting information technologies and data driven methodologies to have a complete and accurate view of their processes. Even though the manufacturing environment produces an immense amount of data through various sensors, the lack of well-integrated solutions is hindering the potential of digitalization in advanced planning and process control systems. Consequently, decision-making becomes challenging, and decision makers as well as operators often rely on their experience. Integrating smart manufacturing through the latest advances in analytics and machine learning into manufacturing decision-support technologies can help overcome these challenges and fully leverage the benefits of AI and digitalization towards sustainable and smart manufacturing environment.
This special session will provide a forum to investigate, exchange novel ideas and disseminate knowledge covering the broad area of smart and sustainable manufacturing in nowadays industry. Experts and professionals from academia, industry, and the public sector are invited to submit papers on their recent research and professional experiences on the subject. High quality papers reporting on relevant reviews of existing literature, theoretical studies, case studies, inter-disciplinary research are all very welcome.
- Dr. Hichem Haddou-Benderbal, Aix-Marseille University, University of Toulon, CNRS, Marseille, France
- Prof. Lyes Benyoucef, Aix-Marseille University, University of Toulon, CNRS, Marseille, France,
- Prof. Alexandre Dolgui, IMT Atlantique, LS2N-CNRS, Nantes, France
Description: Most of the current research concerning individual autonomous vehicles aims to provide methods and new tools which enable the autonomous vehicle to operate safely on road. Nowadays it is getting increasingly recognized that multiples situations will require the coordination of vehicles on their activity. The coordination of CAV will become possible thanks to the introduction of the new wireless communication technologies such as 5G and its associated protocols. These new communications devices enable the development of controllers with high performance and reliability for safe and secure coordination of the CAVs. To design such controllers, real-time measurements or estimates of several variables belonging to each vehicle or to the whole CAVs structure are required. Examples of variables that are needed to be estimated include lateral velocities and accelerations, yaw rates, sideslip angles, tire-road friction, inter-vehicles distances. Furthermore, the new communication technologies may induce some additional problems such as data packet loss, time delay in the data transmission, sensors faults , external cyberattacks and Denial Of Service (DOS). To overcome such drawbacks, the development of new and highly sophisticated estimation algorithms to guaranty safe and secure coordination of CAV’s applications is an important issue. In this open invited track session, we expect recent and original proposition in the fields of design estimation algorithms to ensure safe and secure coordination of CAVs.
- Dr. Sofiane Ahmed Ali, IBISC-Université Paris-Saclay, Evry, France
- Dr. Naima Ait-Oufroukh, IBISC-Université Paris-Saclay, Evry, France
- Dr. Vincent Vigneron, IBISC-Université Paris-Saclay, Evry, France
- Prof. Dalil Ichalal, IBISC-Université Paris-Saclay, Evry, France
- Prof. Said Mammar, IBISC-Université Paris-Saclay, Evry, France
Description: Swarm and evolutionary algorithms have been employed and improved for solving complex optimization and scheduling problems in various areas successfully due to their applicability and interesting computational aspects. This session deals with modeling, optimizing and scheduling challenges of engineering problems by swarm and evolutionary algorithms. It aims specifically at the most recent developments of swarm and evolutionary algorithms, ensemble with machine learning algorithms, and applications for various complex scheduling and optimization problems.
- Dr. Kaizhou Gao, Macau Institute of Systems Engineering, Macau University of Science and Technology, China
- Dr. Yaping Fu, Department of Management Science and Engineering, Qingdao University, China
- Dr. Naiqi Wu, Macau Institute of Systems Engineering, Macau University of Science and Technology, China
SS6: Recent advances in integrated maintenance modeling and optimization for manufacturing-distribution systems
Manufacturing systems represent a significant portion of industrial capital. For the management and the design of such systems, integrated approaches have proven to be more effective and have hence attracted researchers from reliability, availability, and maintainability studies (RAMS) as well as the operational research (OR) communities. Several recent studies demonstrated the benefit of making joint decision on production and maintenance planning problems. In addition to maintenance and production activities, other studies also integrated quality control, outsourcing and carbon emissions issues into the decision processes. Many analytical and simulation models have been proposed to support joint decision making. There are however many important areas of manufacturing systems decision making that are not studied or are insufficiently covered such as warranty, logistics, remanufacturing and other sustainability engineering and management aspects.
This session aims to bring together a group of researchers who have investigated these topics and will have them share their research work with the community with the goal of fostering future research on these key issues. The session also aims to establish a bridge between scientific communities sharing research issues in reliability & maintenance, operational research, etc.
- Dr. Abelhakim Khatab, Lorraine University, France
- Dr. Claver Diallo, Dalhousie University, Canada
- Dr. Lyes Benyoucef, Aix-Marseilles Université, France
- Dr. El-Houssaine Aghezzaf, Ghent University, Belgium
- Dr. Uday Venkatadri, Dalhousie University, Canada
With the emergence of the new manufacturing revolution, often called Industry 4.0, new manufacturing configurations, enhanced with advanced robotics and data acquisition, are being used. Companies and factories, faced with increasing competitiveness, are working on the development of new algorithms and systems to cope with the increasing use of autonomous machines, the demand load and environmental constraints. Moreover, they consistently deal with a dynamic and agile environment, characterized by a considerable amount of data, a changing manufacturing process and uncertain inputs. This Special Session is devoted to the novel intelligent algorithms and systems tackling the manufacturing problems such as: production lines design, production planning and scheduling, energy efficient-manufacturing systems, etc. We aim that this special session gathers researchers and practitioners working on promising research directions and recent advances in these topics.
- Dr. Yassine Ouazne, University of Technolgy of Troyes, France
- Dr. Fabio Fruggiero, School of Engineering -SdI- University of Basilicata, Italy
This Invited Session aims to give a up to date survey of linear and nonlinear Unknown Input Observers (UIO) design. This type of observers, including Decoupling UIO, Proportional-Integral-Observer, functional observer, and descriptor observer, can take into account disturbances, faults or model uncertainties. Such observers have the ability to estimate the state of a system even in the presence of unknown inputs, and also, for some types of observers, to estimate simultaneously the states and unknown inputs. Different techniques and methods have been considered for designing such observers for linear and nonlinear systems subject to unknown input signals and applied in fault detection and isolation methods. This Invited Session aims to track the history and the evolution of observer design ideas in the last decades, benefiting practitioners by enabling them to find an appropriate solution quickly. This call also focusses on results proposing solutions to some issues, such as relative degree 1, slow invariant zeros or internal dynamics, the use of neural networks in modeling of unknown inputs (ARTISMO project), online learning, and some other limitations and constraints of the existing results. The session is open to experimental applications (including transportation, vehicles, robotics, energy systems, etc.), and simulation results using benchmarks to evaluate and compare different design of various UIOs.
- Prof. Dalil Ichalal, University of Evry, Université Paris Saclay, France
- Dr. Naima Ait-Oufroukh, University of Evry, Université Paris Saclay, France
- Dr. Sofiane Ahmed Ali, University of Evry, Université Paris Saclay, France
- Dr. Vincent Vigneron, University of Evry, Université Paris Saclay, France
- Prof. Said Mammar, University of Evry, Université Paris Saclay, France
The intelligent manufacturing systems, computers and communication networks, intelligent transportation, and various service systems that human society relies on today are discrete event systems at a certain level of abstraction. Many discrete event system tools, such as Petri nets, Automata, and Markov chains, have been applied for studying modeling, scheduling, model checking, fault diagnosis and state estimation of these systems. On the other hand, machine learning has developed rapidly, and a lot of classical methods such as decision trees, random forests, neural networks, and Markov networks have been arisen. Such developments provide the possibility for the integration of discrete event systems and machine learning. Some interesting results are obtained and a lot of problems remain open..
- Dr. Jiliang Luo, College of Information Science and Engineering, Huaqiao University, China
- Dr. Weimin Wu, Institute of Cyber-Systems and Control, Zhejiang University, China
- Dr. Jiazhong Zhou, College of Information Science and Engineering, Huaqiao University, China
With the maturation of Artificial Intelligence of Things, many countries have promoted the smart city concept to improve citizens’ living quality, encouraging many technology developments on the Internet of Behavior (IoB) that utilizes Internet of Things (IoT) to analyze behavioral patterns. For example, during the epidemic of COVID-19, a face-mask detection system and thermal imaging camera can identify if employees fulfill the standards; the equipment can also check if people keep social distances in public gatherings. Smart Care Systems can utilize IoT to analyze older adults’ behaviors, which understand elders’ living and health conditions or track their diets, heartbeats, and sleep through wearable watches. After collecting and analyzing the data, the system will provide feedback regarding personal health suggestions. IoB is at its initial stage that requires combinations from diverse techniques, such as IoT, big data, and artificial intelligence. These technologies analyze behavioral patterns and benefit enterprises to conduct marketing activities or transfer harmful user behaviors. IoB also requires sensor networks to exchange and share data, which makes it essential to consider the energy consumption issue of the sensors. With the development of large-scale sensors and data collection, it is predictable that there will be more and more IoB applications and framework proposed. IoB needs scholars to involve in-depth researches and present more frameworks that are effective, enabling IoB to achieve real-time behavioral analysis. Given IoB’s importance and rich applications, it is a very worthwhile topic of research. For this special issue, our proposed goal is to address more than just IoB algorithms; we hope to explore IoB applications and researches in more areas of study and see how IoB models can take a vast amount of available data and help us uncover undiscovered phenomena, retrieve useful knowledge, and draw conclusions and reasoning.
- Prof. Mu-Yen Chen Department of Engineering Science National Cheng Kung University, Taiwan
- Prof. Hsin-Te Wu Computer Science and Information Engineering National Taitung University, Taiwan
- Prof. Alireza Souri Haliç University, Turkey