Abstract
Irregular packing problems (also known as nesting problems) belong to the more general class of cutting and packing problems and consist of allocating a set of irregular and regular pieces to larger rectangular or irregular containers, while minimizing the waste of material or space. These problems combine the combinatorial hardness of cutting and packing problems with the computational difficulty of enforcing the geometric non-overlap and containment constraints. Unsurprisingly, nesting problems have been addressed, both in the scientific literature and in real-world applications, by means of heuristic and metaheuristic techniques. However, more recently a variety of mathematical models has been proposed for nesting problems. These models can be used either to provide optimal solutions for nesting problems or as the basis of heuristic approaches based on them (e.g. matheuristics). In both cases, better solutions are sought, with the natural economic and environmental positive impact. Different modeling options are proposed in the literature. We review these mathematical models under a common notation framework, allowing differences and similarities among them to be highlighted. Some insights on weaknesses and strengths are also provided. By building this structured review of mathematical models for nesting problems, research opportunities in the field are proposed.

Abstract
The European Union has been making efforts to increase energy efficiency within its member states, in line with most of the industrialized countries. In these efforts, the energy consumed by public lighting networks is a key target as it represents approximately 50% of the electricity consumption of European cities. In this paper we propose an approach for the autonomous management of public lighting networks in which each luminary is managed individually and that takes into account both their individual characteristics as well as ambient data. The approach is compared against a traditional management scheme, leading to a reduction in energy consumption of 28%. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.

Abstract
The two-dimensional level strip packing problem has received little attention from the scientific community. To the best of our knowledge, the most competitive model is the one proposed in 2004 by Lodi et al., where the items are packed by levels. In 2015, an arc flow model addressing the two-dimensional level strip cutting problem was proposed by Mrad. The literature presents some mathematical models, despite not addressing specifically the two-dimensional level strip packing problem, they are efficient and can be adapted to the problem. In this paper, we adapt two mixed integer linear programming models from the literature, rewrite the Mrad's model for the strip packing problem and add well-known valid inequalities to the model proposed by Lodi et al. Computational results were performed on instances from the literature and show that the model put forward by Lodi et al. with valid inequalities outperforms the remaining models with respect to the number of optimal solutions found.

Abstract
Industry 4.0 is a result of technological evolution and is intended to promote technological transformations in industry at different levels. The impact in human employment has been perceived as a major threat and is a matter of concern. Some authors argue that automation will bring unimaginable changes as soon as computers get more intelligence and as machines become able to perform complex tasks more efficiently than humans. However, technological progress is also pointed out as a stimulus for human-beings to develop the competencies that differentiate them from the machines. In this context, this study aims to explore the impacts of adopting Industry 4.0 technologies on work. The results of a comprehensive literature review provide an integrated perspective to identify and understand such impacts, analysing them in four categories: evolution of employment and creation of new jobs, human-machine interaction, new competencies creation/ development, and, organizational and professional changes. © 2020 IEEE.

Abstract