Ferran Torrent-Fontbona

Solving large immobile location-allocation by affinity propagation and simulated annealing. Application to select which sporting event to watch

Immobile Location-Allocation (ILA) is a combinatorial problem which consists in, given a set of facilities and a set of demand points, determining the optimal service each facility has to offer and allocating the demand to such facilities. The applicability of optimization methods is tied up to the dimensionality of the problem, but since the distance between data points is a key factor, clustering techniques to partition the data space can be applied, converting the large initial problem into several simpler ILA problems that can be solved separately. This paper presents a novel method that combines clustering and heuristic methods to solve an ILA problem, which reduces the elapsed time keeping the quality of the solution found compared with other heuristics methods.

Self-organising energy demand allocation through canons of distributive justice in a microgrid

The energy sector is being driven into a new era where considerable portions of electrical demand will be met through distributed energy resources (DERs). Microgrids have been suggested as a tool for integrating and managing DERs. In this context, we formulate the energy demand allocation problem in order to provide service to a given load. We then propose a dynamic method for agreeing and setting the rules to perform the allocation. The methodology is based on self-organisation and the concept of distributive justice which integrates different principles of fairness represented as legitimate claims. Legitimate claims are implemented as voting functions and are used to determine how the DER requests are satisfied. The method is tested by considering different configurations of DERs, mainly of the renewable type, and comparing them with other allocation methods. Results show that this self-organising allocation method provides a better balance amongst all the representations of justice, but also it is more robust for the external authorities that manipulate the allocation process. HighlightsSelf-organised approach based on distributive justice to allocate energy generation.Distributive justice allows a fair allocation considering different objectives.Experimentation performed over different microgrids.Results show the benefits of the proposed method regarding profit, CO2, etc.

Adaptive basal insulin recommender system based on Kalman filter for type 1 diabetes

Abstract Type 1 diabetes mellitus is a chronic disease that requires those affected to self-administer insulin to control their blood glucose level. However, the estimation of the correct insulin dosage is not easy due to the complexity of glucose metabolism, which usually leads to blood glucose levels far from the optimal. This paper presents an adaptive and personalised basal insulin recommender system based on Kalman filter theory that can be used with or without continuous glucose monitoring systems. The proposed approach is tested with the UVa/PADOVA simulator with eleven virtual adult subjects. It has been tested in combination with two different bolus calculators, and the performance achieved has been compared with that obtained with the default basal doses of the simulator, which can be assumed as optimal. The achieved results demonstrate that the proposed system rapidly converges to the optimal basal dose, and it can be used with adaptive bolus calculators without the risk of instability.

Prediction of glucose level conditions from sequential data

This work has received funding from the EU Horizon 2020 research and innovation programme under grant agreement No 689810 (PEPPER), and from the University of Girona under the grant MPCUdG2016 (Ajut per a la millora de la productivitat cientifica dels grups de recerca), and the Spanish MINECO under the grant number DPI2013- 47450-C21-R. This work has been developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016)

Using Multi-attribute Combinatorial Auctions for Resource Allocation

Social concerns about the environment and global warming suggest that industries must focus on reducing energy consumption, due to its social impact and changing laws. Furthermore, the smart grid will bring time-dependent tariffs that pose new challenges to the optimisation of resource allocation. In this paper we address the problem of optimising energy consumption in manufacturing processes by means of multi-attribute combinatorial auctions, so that resource price, delivery time, and energy consumed (and therefore environmental impact) are minimised. The proposed mechanism is tested with simulated data based on real examples, showing the impact of incorporating energy into task allocation problems. It is then compared with a sequential auction method.