Abstract
Cutting phases occur in many production processes when a larger object must be cut into multiple smaller pieces. Some examples of relevant industries being clothing, footwear, metalware and furniture. The cutting phase is composed of two stages. The first stage consists of finding a good layout for the set of small pieces that must be cut from the larger object and minimizing some objective such as raw-material waste (The Cutting and Packing Problem). Once this good layout has been established, it is provided as input for the second stage which consists of determining the path to cut the pieces which minimizes another objective, such as the total cutting time or distance (The Cutting Path Determination Problem). This second stage is crucial for efficient production planning. Only one linear mathematical model has previously been proposed for the Cutting Path Determination Problem. In this paper, this problem is addressed using two exact approaches based on the Rural Postman Problem (RPP) and the Traveling Salesman Problem (TSP). The RPP approach, in particular, is able to produce optimal solutions for instances containing more than 2000 edges in under 1 h.

Abstract
In recent years, cognitive robots have started to find their way into manufacturing halls. However, the full potential of these robots can only be exploited through (a) an integration of the robots with the Manufacturing Execution System (MES), (b) a new and simpler way of programming based on robot skills, automated task planning, and knowledge modeling, and (c) enabling the robots to function in a shared human/robot workspace with the ability to handle unexpected situations. The STAMINA project has built a robotic system that meets these objectives for an automotive kitting application, which has also been tested, validated, and demonstrated in a relevant environment (TRL6). This paper describes the STAMINA robot system and the evaluation of this system on a series of realistic kitting tasks. The structure of the system, evaluation methodology, and experimental results, are presented along with the insights and experiences gained from this work.

Abstract
This study proposes a model for (re-)designing machine layouts in already existing facilities with a multi-period time planning horizon. The model can be applied in several situations and at different moments of a layout life cycle, for example to design the initial layout of an existing facility, or to make some specific and local reconfigurations. This dynamic multiobjective model minimizes costs (production, material handling and reconfiguration costs), maximizes adjacency between machines, minimizes unsuitability (to combine characteristics of the machines and of the existing locations), and can allow changes between periods on the product mix or on the machine layout requirements (e.g., required area). The performance of the model was tested with a case study based on a real first-tier supplier of the automotive industry, thus showing the practical potential of the proposed approach.

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.