Abstract
Among the most complex problems in the field of 2-dimensional cutting & packing are irregular packing problems, in which items may have a more complex geometry. These problems are prominent in several areas, including, but not limited to, the textile, shipbuilding and leather industries. They consist in placing a set of items, whose geometry is often represented by simple polygons, into one or more containers such that there is no overlap between items and the utility rate of the container is maximized. In this work, the irregular strip packing problem, an irregular packing variant with a variable length container, is investigated. The placement space is reduced by adopting a rectangular grid and a full search is performed using preprocessed raster penetration maps to efficiently determine the new position of an item. Tests were performed using simple dotted board model cases and irregular strip packing benchmark instances. The comparison of our results with the state of the art solutions showed that the proposed algorithm is very competitive, achieving better or equal compaction in 9 out of 15 instances and improving the average density in 13 instances. Besides the contribution of the new best results, the proposed approach showed the advantage of adopting discrete placement, which can be potentially applied to other irregular packing problems.

Abstract
In two-dimensional nesting problems (irregular packing problems) small pieces with irregular shapes must be packed in large objects. A small number of exact methods have been proposed to solve nesting problems, typically focusing on a single problem variant, the strip packing problem. There are however several other variants of the nesting problem which were identified in the literature and are very relevant in the industry. In this paper, constraint programming (CP) is used to model and solve all the variants of irregular cutting and packing problems proposed in the literature. Three approaches, which differ in the representation of the variable domains, in the way they deal with the core constraints and in the objective functions, are the basis for the three models proposed for each variant of the problem. The non-overlap among pieces, which must be enforced for all the problem variants, is guaranteed through the new global constraint NoOverlap in one of the proposed approaches. Taking the benchmark instances for the strip-packing problem, new instances were generated for each problem variant. Extensive computational experiments were run with these problem instances from the literature to evaluate the performance of each approach applied to each problem variant. The models based on the global constraint NoOverlap performed consistently better for all variants due to the increased propagation and to the low memory usage. The performance of the CP model for the strip packing problem with the global constraint NoOverlap was then compared with the Dotted Board with Rotations using larger instances from the literature. The experiments show that the CP model with global constraint NoOverlap can quickly find good quality solutions in shorter computational times even for large instances.

Abstract
Chatbots have been used in business contexts as a new way of communicating with customers. They use natural language to interact with the customers, whether while offering products and services, or in the support of a specific task. In this context, an important and challenging task is to assess the effectiveness of the machine-to-human interaction, according to business’ goals. Although several analytic tools have been proposed to analyze the user interactions with chatbot systems, to the best of our knowledge they do not consider user-defined criteria, focusing on metrics of engagement and retention of the system as a whole. For this reason, we propose the KnowBots tool, which can be used to discover relevant patterns in the dialogues of chatbots, by considering specific business goals. Given the non-trivial structure of dialogues and the possibly large number of conversational records, we combined sequential pattern mining and subgroup discovery techniques to identify patterns of usage. Moreover, a friendly user-interface was developed to present the results and to allow their detailed analysis. Thus, it may serve as an alternative decision support tool for business or any entity that makes use of this type of interactions with their clients.

Abstract
Today's manufacturing environment is increasingly pressured to higher flexibility induced by uncertain production volumes as well as uncertain product lifetime. A way to improve productivity in a flexible production system is by using a safe and flexible cooperation between robot and operator. Therefore, manufacturing companies are experiencing an increase in human-robot interactions and in the use of collaborative robots (cobots). To make full use of cobots, it is essential to understand the drivers for their adoption as well as how these drivers are aligned with the companies' strategic objectives. By means of in-depth interviews in six companies in Portugal and France, this study provides a comprehensive understanding of the drivers that influence the intent to adopt, or the effective adoption, of cobots and the alignment of these drivers with the strategic objectives of the company. Empirical results reveal "operational efficiency" and "ergonomics and human factors" concerns as important drivers in the adoption intent. In terms of strategic objectives, it was found that drivers are aligned with productivity and flexibility improvement as well as quality improvement strategic objectives. Understanding these drivers can help in motivating manufacturing companies to adopt cobots, in facilitating their adoption, and in reaping the benefits of this technology.

Abstract
The concept of industry 4.0 (i4.0) encompasses the integration of different technologies into an autonomous, knowledge- and sensor-based, self-regulating production system. Our objective is to synthesize which are the challenges and opportunities of adopting i4.0 from the perspective of technology provider companies. A single-case research was conducted with ten companies at the Portuguese Production Technologies Cluster. Based on i4.0 technologies – Augmented reality; Additive Manufacturing; Big Data; Cloud Computing; Cyber-Physical Systems; Cybersecurity; Smart Robotics; Simulation; and System Integration – interviewees mentioned that the main adoption challenges are the analysis of data generated, integration of new technologies with available equipment and workforce, and computational limitations. The main opportunities are improvements in: efficiency; flexibility; productivity; cybersecurity; quality of products and services; and decision process due to data analysis. Interviewees have also foreseen changes in company's business model through the integration of internal resources with complementary activities of their partners and other cluster companies. © 2019 Elsevier Inc.

Abstract
The highly disruptive transformation that digital platforms are imposing on entire sectors of the economy, along with the broad digitalization of industrial business processes, is having an impact on supply chains around the world. To take advantage of this new aggregated market paradigm new business models with a heavy focus on servitization are changing the value proposition of businesses. In this paper, we describe a reference architectural framework designed to support a digital platform fostering the optimization of supply chains by the pairing of unused industrial capacity with production demand. This framework aims at harmonizing stakeholder requirements with specifications of different levels in order to set up a coherent reference blueprint that serves as a starting point for development activities. A four-layer approach is used to articulate between technical components, with the data and tools layers, and the ecosystem, with the business and interfaces layers. The overall architecture and component description is presented as extensions of the initial set of affordances.