Abstract
This paper describes the results of our collaboration with the leading Portuguese food retailer to address the shelf-space planning problem of allocating products to shop-floor shelves. Our challenge was to introduce analytical methods into the shelf-space planning process to improve the return on space and automate a process heavily dependent on the experience of the retailer's space managers. This led to the creation of GAP, a decision support system that the company's space-management team uses daily. We developed a modular operations research approach that systematically applies mathematical programming models and heuristics to determine the best layout of products on the shelves. GAP combines its analytical strength with an ability to incorporate different types of merchandising rules to balance the tradeoff between optimization and customization.

Abstract
The manufacturer's pallet loading problem (MPLP) has been widely studied during the past 50 years. It consists of placing a maximum number of identical rectangular boxes onto a single rectangular pallet. In this paper, we have reviewed the methods that have been proposed for the solution of this problem. Furthermore, the various problem instances and data sets are analyzed that have been used in computational experiments for the evaluation of these methods. The most challenging and yet unsolved methods are identified. By doing so, areas of future research concerning the MPLP can be highlighted.

Abstract

Abstract
While bottom-up approaches to object recognition are simple to design and implement, they do not yield the same performance as top-down approaches. On the other hand, it is not trivial to obtain a moderate number of plausible hypotheses to be efficiently verified by top-down approaches. To address these shortcomings, we propose a hybrid top-down bottom-up approach to object recognition where a bottom-up procedure that generates a set of hypothesis based on data is combined with a top-down process for evaluating those hypotheses. We use the recognition of rectangular cuboid shaped objects from 3D point cloud data as a benchmark problem for our research. Results obtained using this approach demonstrate promising recognition performances.

Abstract
Growing interest on business collaboration motivates today's industries, especially small and medium enterprises (SMEs) to establish and manage dynamic and non-hierarchical business networks to respond to market opportunities with added business benefits. This business environment requires concurrent work and real-time information sharing between key business partners in order to design and develop customised products. The use of traditional communication media such as e-mail, phone and fax is not satisfactory to get real-time information and is time-consuming and most often ineffective. In such environments, an Information and Communication Technology (ICT)/Web-based technology supports real-time information sharing among networked SMEs for the collaborative design and manufacturing of customised products. This study proposes an innovative ICT platform supporting SMEs collaboration, through Web and the Internet of Things technologies, which follows the Net-Challenge conceptual approach and methodological guidelines for SMEs to form and operate virtual organisations for the collaborative development and delivery of customised products. The ICT Platform was assessed in three different industry domains, namely the textile and apparel, the footwear and the machine tools, respectively. This ICT solution promotes collaboration, with substantial advantages for SMEs including the reduction of costs and delivery time and improvement of the innovation potential.

Abstract
Cognitive robots, able to adapt their actions based on sensory information and the management of uncertainty, have begun to find their way into manufacturing settings. However, the full potential of these robots has not been fully exploited, largely due to the lack of vertical integration with existing IT infrastructures, such as the manufacturing execution system (MES), as part of a large-scale cyber-physical entity. This paper reports on considerations and findings from the research project STAMINA that is developing such a cognitive cyber-physical system and applying it to a concrete and well-known use case from the automotive industry. Our approach allows manufacturing tasks to be performed without human intervention, even if the available description of the environment-the world model-suffers from large uncertainties. Thus, the robot becomes an integral part of the MES, resulting in a highly flexible overall system.