Constraint-based Scheduling for Closed-loop Production Control in RMSs

Reconfigurable manufacturing systems (RMS) are conceived to operate in dynamic production contexts often characterized by fluctuations in demand, discovery or invention of new technologies, changes in part geometry, variances in raw material requirements. With specific focus on the RMS production aspects, the scheduling problem implies the capability of developing plans that can be easily and efficiently adjusted and regenerated once a production or system change occurs. The authors present a constraint-based online scheduling controller for RMS whose main advantage is its capability of dynamically interpreting and adapting to production anomalies or system misbehavior by regenerating on-line a new schedule. The performance of the controller has been tested by running a set of closed-loop experiments based on a real-world industrial case study. Results demonstrate that automatically synthesizing plans and recovery actions positively contribute to ensure a higher production rate

From creativity to value creation

In today’s world, globally interconnected, volatile, and characterized by a sky-rocketing complexity, significant and unprecedented interdisciplinary is required among various stakeholders to create resilient and innovative value chains. Within this compelling context, we focus on the new role that university-industry collaboration plays on a large scale in bridging the gap between idea generation and value creation to economy and society. A new way to promote attitude towards entrepreneurial leadership at an early stage among students and teachers is experienced by linking curricular and extracurricular teaching and contents, as well as by supporting voluntary learning “on demand” among students. Intertwined links are indeed possible within a nursery environment, so-called Entreprenursery, where students are encouraged to express their creativity, both by raising startup ideas and by solving companies’ technical and scientific issues. Entrepreneurial students are thus supported in their innovative ideas through collaboration with teachers, experts, entrepreneurs. They are also stimulated to engage other students to be part of an interdisciplinary team. Cooperation in supporting cross-fertilization of creative ideas will be fed by competencies, an openminded environment, and where diversity integration plays an important role. Only through different thinking is it possible to develop outstanding achievements. Coordination is guaranteed by a collaborative IT platform, which is also open to SMEs to facilitate them in involving entrepreneurial students. Within this new collaborative framework, all stakeholders will profit from reciprocal learning and creativity, increasing the entrepreneurial attitudes of students and teachers and thus accelerating the transfer of academic startup ideas into industrial applications and business opportunities

Editorial of the Special Issue “Advances in Artificial Intelligence Methods Applications in Industrial Control Systems”

Today, Artificial Intelligence (AI) applications are considered to be of increasing relevance for the future of industrial control systems [...

Transfactory: Towards a New Technology-Human Manufacturing Co-evolution Framework

The evolution of work organizational models and of technology poses an unprecedented set of challenges and opportunities for ensuring workers’ well-being. After an overview of the evolution of these two aspects and the role of humans with their respect, we will frame the current and near-future developments according to the job demands-resources model, where humans’ well-being depends on the good balance between job demands (workload, stress, risks, etc.) and job resources, both personal (resilience, optimism, etc.) and job-related (organizational and technological support). Adaptive automation and artificial intelligence have the potential to become job resources rather than job demands, but only if a proper design is set. Central to the design is the notion of plasticity, i.e., the capacity of an element to adapt to changes in its coupling with interacting elements. We will discuss two forms of plasticity in the relationship between humans and technology. Short-term plasticity is mainly based on the immediate adaptation of technology to human needs and performance. The long-term plasticity is based on the co-evolution of humans and technology, where changes concern not just the mere performance, but wider and long-lasting aspects like knowledge, culture, identities, approaches, job frameworks. Organization is crucial to foster this kind of co-evolution, towards a new framework, called transfactory, where human needs and values govern the overall systems evolution

Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems

Industrial control systems play a central role in today's manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further developments are required. In recent years, developments in Artificial Intelligence (AI)-based methods have gained significantly attention and relevance in research and the industry for future industrial control systems. AI-based approaches are increasingly explored at various industrial control systems levels ranging from single automation devices to the real-time control of complex machines, production processes and overall factories supervision and optimization. Thereby, AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems. The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems' self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. Finally, major open challenges and future perspectives are addressed.ISSN:2076-341