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
In this paper the authors present a meta-model aiming to support decision-makers that wish to know more about how to use systems models to cope with the integration of environmental concerns into the company strategy. This is made by using a General Morphological Analysis (GMA) to bridge the gap between Operations Research (OR) analysts, decision-makers and stake-holders, making all of them part of the problem structuring and formulation process, particularly in societal issues like the environmental ones. The novelty of this approach is two-fold: (i) there are no examples in literature of a GMA research that address a linkage between environmental practices, strategic objectives, and the integration of stakeholders in the decision-making process at the level of a company; (ii) there is no GMA that had covered all the phases of a decision-making problem (problem definition, problem analysis and problem solving) in such a context.

Abstract
This paper presents a novel approach on flexible and knowledge intensive process management, driven by a large automotive industry case study. The automotive company in analysis requires a very dynamic behaviour, based on high flexibility of both people and equipment. Market has been imposing a decreasing of automotive products life cycles, increasing the number of line adaptations during the entire value chain, resulting in an increased complexity from product design to production. To handle this complexity, new knowledge-based methods and technologies to model, simulate, optimize and monitor planned and existing manufacturing systems are required. Existing large Enterprise Information Systems impose totally structured and predictable workflow, while knowledge intensive processes are flexible and unpredictable, involving high amount of human-decision and interaction. This lead to the need of development of highly specialized applications. This paper presents a novel hybrid approach, including work, information and communication management, to support knowledge intensive processes. The application of the new solution in the automotive engineering process management proved to be very effective and efficient, leading to significant savings.

Abstract
The purpose of this paper is to develop a robust methodology to assess municipalities' performance concerning the consumption of resources in residential buildings. The assessment is carried out at a municipal level to inform decision makers about the relative position of their municipalities compared to others. In addition, the factors associated to better levels of municipal performance are identified, and the extent of their effects is quantified. The study uses an enhanced stochastic frontier panel model based on data of energy, water and materials consumption in Lisbon municipalities during the period 2003-2009. The study reveals that the municipalities' performance has remained stable over the years, although there are considerable differences in performance among municipalities. In addition, it is concluded that municipal performance tends to improve with the environmental policy expenditure and scale size, and decline with buildings' age, population density and the proportion of buildings with private ownership.

Abstract
Recent advances in telecommunications created new opportunities for monitoring public transport operations in real-time. This paper presents an automatic control framework to mitigate the Bus Bunching phenomenon in real-time. The framework depicts a powerful combination of distinct Machine Learning principles and methods to extract valuable information from raw location-based data. State-of-the-art tools and methodologies such as Regression Analysis, Probabilistic Reasoning and Perceptron's learning with Stochastic Gradient Descent constitute building blocks of this predictive methodology. The prediction's output is then used to select and deploy a corrective action to automatically prevent Bus Bunching. The performance of the proposed method is evaluated using data collected from 18 bus routes in Porto, Portugal over a period of one year. Simulation results demonstrate that the proposed method can potentially reduce bunching by 68% and decrease average passenger waiting times by 4.5%, without prolonging in-vehicle times. The proposed system could be embedded in a decision support system to improve control room operations. (C) 2016 Published by Elsevier B.V.

Abstract
In this paper we address the problem of maximizing the expected number of transplants in a kidney exchange program. We propose an integer programming model with an exponential number of decision variables which are associated with cycles. By introducing the concept of type of cycle, we avoid the complete cycle enumeration and develop a branch-and-price approach. © 2016 Elsevier B.V.