Collaborative networks and systems (CNS) have received much attention during the past decades, in order to reach competitive advantage in their application domain. Many contribution did arise, from the industrial context to service oriented companies, for instance in the scope of artificial intelligence. Therefore, many contribution have been put forward related to collaborative and intelligent networks and systems.
In spite of the wide range of existing work in this area, it continuous to be imperative for Companies to understand and anticipate the importance of CNS in manufacturing to enable them to reach competitive advantage in the current global market and Industry 4.0 oriented scenario.
Moreover, there is a need to further develop collaborative and intelligent strategies, models and tools to enable Companies to fit the characteristics of industrial networks and systems in dynamic, and real-time based decision making environments, and specialization of companies, along with increasing mass customization requirements.
These main topics strengthen the specific characteristics of CN through: collaboration, to deliver products and services; decentralization of decision-making, and inter and intra-organizational integration to meet imposed performance requirements in competitive global markets.
Moreover, in the context of CNS normalization is a crucial step in all decision models, to produce comparable and dimension less data from heterogeneous data. Therefore, it is of upmost importance to use appropriate data normalization techniques for each application scenario, for instance, according to the kind of multi-criteria or multi-objective optimization methods or algorithms used for networked supply and operations management. This is even more important in the upcoming increasingly digital era of the I4.0, along with the perceived need for big data processing, regarding the need for vertical and horizontal integration of data and manufacturing processes.
This Special Issue intends to provide a contribution to the domain of collaborative and intelligent networks and systems to fill the gap in theories and practical applications for supporting industrial companies through suitable methods and solutions applicable to a wide range of instances. Therefore, the special issue aims to bring together researchers from a wide range of disciplines to provide potential contributions to the main topics underlying this proposal, although not limited to the following:
– Collaboration strategies
– Intelligent models, methods and tools
– Dynamic and real-time based decision-support approaches
– Decentralized decision support networks
– Hybrid intelligent decision support systems
– Machine learning
– Bio-inspired models and algorithms
– Negotiation and group decision making approaches
– Multi-criteria and multi-objective models
– Uncertainty treatment
– Data normalization and data fusion methods and techniques
– Data analytics for manufacturing systems and processes
– Cloud computing and big data – Learning and data mining
– Data visualization for the digital factory
– Real time machine and process monitoring, diagnostics and prognostics
– Real-time management
– Manufacturing Execution Systems
– Open source software applications for the digital or cyber manufacturing
– Internet-of-things for cyber manufacturing
Dr. Rajeev Agrawal, Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, IN | Dr. Leonilde Varela, Department of Production and Systems, School of Engineering, University of Minho (DPS-UMinho), PT | Dr. Vijaya Kumar Manupati, NIT Warangal, IN
Organizing Secretary– ICEM-2020
Department of Mechanical Engineering
Malaviya National Institute of Technology, Jaipur, Rajasthan-302017 (India)