Abstract
The rise of a new digital industrial paradigm known as industry 4.0, powered by several enabling technologies such as flexible and collaborative robots, autonomous vehicles, internet of things, cloud and manufacturing servitization, is seen as the key enabler for the fourth industrial revolution: the digital manufacturing. However, there are still several challenges related to the effective adoption of these technologies and to enterprise level interoperability so that an entire cyber physical production system can work seamlessly. Besides the set of challenges faced by industrialists, this research work presents innovative results towards a plug and play seamlessly integration of technologies, from the shop floor to the enterprise management layers, ensuring a total integrated architecture supporting the full product lifecycle management.

Abstract
Today, manufacturing is moving towards customer-driven and knowledge-based proactive production. Shorter product life cycles lead to increased complexity in areas such as product and process design, factory deployment and production operations. To handle this complexity, new knowledge-based methods and technologies are needed to model, simulate, optimize and monitor manufacturing systems. Existing large Enterprise Information Systems (EIS) impose structured and predictable workflow, while processes "on the ground" are often unpredictable and involve a large number of human based decisions and collaboration. This is leading to a major shift on EIS paradigm and leading to development of a set of specialized small applications, each one with fewer features, but highly specialized, flexible, cross linked and easy to use. This paper presents a hybrid management solution intended to support collaboration and decision in the scope of automotive engineering and planning. The solution, labelled as HPM - Hybrid Process Manager, encompasses a set of tools for work, information and communication management fully integrated with knowledge based engineering processes. Its overall aim is to ease the flow of information between all the partners, making it more reliable and actual, allowing a closer control and faster reaction to upcoming events. The adoption of HPM approach proves to be quite effective and efficient, leading to significant results in terms of cost and time saving. When using the solution, managers no longer need to constantly ask for reporting, leading to a significant reduction on email and paperwork. It is relevant to underline that the proposed approach allowed planners to concentrate in important issues improving the product and avoid non-value added efforts and time on collateral activities. Another main advantage stays on the experience retrieval module built in top of the solution, allowing easy access to expertise, knowledge and best practices generated by previous projects, so that they can be readily incorporated in the design of new processes as a factor of knowledge sustainability. (C) 2016 Published by Elsevier B.V.

Abstract
In this paper we present a hybrid approach to integrative clustering based on the p-median problem with clients' preferences. We formulate the problem of simultaneous clustering of a set of objects, characterized by two sets of features, as a bi-level p-median model. An exact approach involving a branch-and-cut method combined with the simulated annealing algorithm is used, that allows one to find a two-source clustering. The proposed approach is compared with some well-known mathematical optimisation based clustering techniques applied to the NCI-60 tumour cell line anticancer drug screen dataset. The results obtained demonstrate the applicability of our approach to find competitive integrative clusterings. Copyright © by the paper's authors.

Abstract
This study proposes an optimization model to improve the robustness of an existing bus schedule. Robustness represents the ability of schedules to absorb deviations from the timetable and to prevent their propagation through the daily operations. The model developed proposes an optimal assignment of arrival times and distribution of slacks among Time Control Points of a bus line, in order to minimize delays and anticipations from schedule. This required the use of data collected through GPS devices installed in buses, informing the location of buses during their daily operation. The robustness of bus schedules was evaluated through the quantification of delays and anticipations of real observations of bus shifts by comparison with the timetable. The performance measures used to evaluate robustness are the average delay (or anticipation) of buses by comparison with the timetable, and the probability that a passenger that arrives on time according to the timetable will miss the bus or have to wait more than a specified threshold at a Time Control Point. We also compared the improvement of the schedule proposed by the optimization model with the original schedule. The results obtained in a real-world case study, corresponding to a bus line operating in Porto, showed that the model could return an improved schedule for all performance measures considered when compared with the original schedule.

Abstract

Abstract
The United States, Brazil, and Argentina are responsible for 83% of world’s soybean production. Together, they respond to more than 80% of soybean grains and soybean meal exported and for more than 60% of soybean oil exportation. This paper studies the soybean trade of these three major exporters with the top ten commercial partners of each one in order to examine the main factors that influence this relationship. We follow a network analysis approach to evaluate the level of interdependence between exporters and importers. Our research studies the three main soybean products: grain, meal, and oil. The findings seem to indicate that countries prefer importing soybean grains to process inside their borders due to commodity prices and logistics costs.