Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management II

Dear Colleagues,

Collaborative networks and systems (CNS) have received much attention in recent decades, in order to reach competitive advantage in their application domain. Many contributions have arisen from the industrial context to service-oriented companies, for instance, in the scope of artificial intelligence. Therefore, many contributions have been put forward related to collaborative and intelligent networks and systems.

In spite of the wide range of existing work in this area, however, it continues to be imperative for companies to understand and anticipate the importance of CNS in manufacturing to enable them to reach a competitive advantage in the current global market and Industry 4.0-oriented scenario.

These main topics strengthen the specific characteristics of CN through collaboration to deliver products and services; the 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 multicriteria or multiobjective 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 for supporting engineering and production management 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, this 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;
• Learning organizations;
• Chaos and complexity management;
• Game theory models and aproaches for supporting production management;
• Blockchain tecnology applied to manufacturing and management;
• Intelligent models, methods, and tools;
• Dynamic and real-time based decision-support approaches;
• Decentralized and distributed decision-support networks and models;
• Social network based models, methods and tools;
• Hybrid intelligent decision-support and recommendation systems;
• Multiagents models and platforms;
• Machine and deep learning based approaches and systems;
• Bio-inspired models and algorithms applications;
• Negotiation and group decision-making approaches;
• Multicriteria and multiobjective models;
• Uncertainty treatment;
• Data normalization and data fusion methods and techniques;
• Data analytics for manufacturing systems and processes;
• Cloud computing, manufacturing and big data processing;
• Learning and data mining, and other data science oriented approaches;
• 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 digital or cyber manufacturing;
• Internet of Things for cyber manufacturing and management.

Dr. Leonilde Varela
Dr. Goran D. Putnik
Guest Editors

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