The Distribution of Software in the European Union After the Decision of the CJEU “UsedSoft GmbH V. Oracle International Corp.†(“UsedSoftâ€)

In “UsedSoft GmbH v. Oracle International Corp.â€, the Europeam Court of Justice opened the way for the sale of "second-hand software" across Europe. The decision UsedSoft gives rise to new data in terms of the content of the right of distribution of a work, including the copy of a computer program, and the issue of exhaustion of the right of distribution of a copy of a computer program. The decision is expected to affect radically the functioning of the EU market of computer programs

Data-driven scenario generation for two-stage stochastic programming

Optimisation under uncertainty has always been a focal point within the Process Systems Engineering (PSE) research agenda. In particular, the efficient manipulation of large amount of data for the uncertain parameters constitutes a crucial condition for effectively tackling stochastic programming problems. In this context, this work proposes a new data-driven Mixed-Integer Linear Programming (MILP) model for the Distribution & Moment Matching Problem (DMP). For cases with multiple uncertain parameters a copula-based simulation of initial scenarios is employed as preliminary step. Moreover, the integration of clustering methods and DMP in the proposed model is shown to enhance computational performance. Finally, we compare the proposed approach with state-of-the-art scenario generation methodologies. Through a number of case studies we highlight the benefits regarding the quality of the generated scenario trees by evaluating the corresponding obtained stochastic solutions

Module detection in complex networks using integer optimisation

<p>Abstract</p> <p>Background</p> <p>The detection of <it>modules or community structure </it>is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the discovery of community structure based on modularity maximisation have been developed. However, satisfactory partitions of large graphs with modest computational resources are particularly challenging due to the NP-hard nature of the related optimisation problem. Furthermore, it has been suggested that optimising the modularity metric can reach a resolution limit whereby the algorithm fails to detect smaller communities than a specific size in large networks.</p> <p>Results</p> <p>We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations).</p> <p>Conclusions</p> <p>A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability.</p

Stable optimisation-based scenario generation via game theoretic approach

Systematic scenario generation (SG) methods have emerged as an invaluable tool to handle uncertainty towards the efficient solution of stochastic programming (SP) problems. The quality of SG methods depends on their consistency to generate scenario sets which guarantee stability on solving SPs and lead to stochastic solutions of good quality. In this context, we delve into the optimisation-based Distribution and Moment Matching Problem (DMP) for scenario generation and propose a game-theoretic approach which is formulated as a Mixed-Integer Linear Programming (MILP) model. Nash bargaining approach is employed and the terms of the objective function regarding the statistical matching of the DMP are considered as players. Results from a capacity planning case study highlight the quality of the stochastic solutions obtained using MILP DMP models for scenario generation. Furthermore, the proposed game-theoretic extension of DMP enhances in-sample and out-of-sample stability with respect to the challenging problem of user-defined parameters variability

Optimisation as a Tool for Gaining Insight: An Application to the Built Environment

The design of heating systems for dwellings using new technologies, or new versions of old technologies, requires the ability to predict the temperatures in a dwelling. The temperature behaviour can be modelled, typically by differential equations which incorporate thermal driving forces and the thermal inertia of a dwelling. The development and characterisation of these models is usually based on fitting data accumulated over sufficient time to capture the behaviour of the dwelling under different conditions (summer, winter, etc.). Model fitting relies on assumptions about the behaviour of the system. Optimisation can be used to examine these assumptions and gain insight into this behaviour. This paper describes the application of a nature inspired algorithm, known as the Plant Propagation Algorithm, a variant of a Variable Neighbourhood Search algorithm, to the problem of modelling a dwelling heated by an air source heat pump. The algorithm is evaluated using different population evolution strategies and implemented using a simple parallel computing paradigm on a multi-core desktop system. The results are used to identify potential sources of missing data which could explain the observed behaviour of the dwelling. </jats:p

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information

Optimal Piecewise Linear Regression Algorithm for QSAR Modelling

Quantitative Structure‐Activity Relationship (QSAR) models have been successfully applied to lead optimisation, virtual screening and other areas of drug discovery over the years. Recent studies, however, have focused on the development of models that are predictive but often not interpretable. In this article, we propose the application of a piecewise linear regression algorithm, OPLRAreg, to develop both predictive and interpretable QSAR models. The algorithm determines a feature to best separate the data into regions and identifies linear equations to predict the outcome variable in each region. A regularisation term is introduced to prevent overfitting problems and implicitly selects the most informative features. As OPLRAreg is based on mathematical programming, a flexible and transparent representation for optimisation problems, the algorithm also permits customised constraints to be easily added to the model. The proposed algorithm is presented as a more interpretable alternative to other commonly used machine learning algorithms and has shown comparable predictive accuracy to Random Forest, Support Vector Machine and Random Generalised Linear Model on tests with five QSAR data sets compiled from the ChEMBL database