Optimised search heuristics: combining metaheuristics and exact methods to solve scheduling problems

Tese dout., Matemática, Investigação Operacional, Universidade do Algarve, 2009

Molecular cloning, full-length sequence and preliminary characterization of a 56-kDa protein induced by human interferons

Among the various proteins which are induced when human cells are are treatened with interferon, a predominant protein of unknown function, with molecular mass 56 kDa, has been observed. With the aim of exploring the molecular basis of the regulation of this protein and of its mRNA, in order to understand its biological functionand its possible contribution to the various antiviral and non-antiviral actions exerted by interferons.

Evaluation of the human factor in the scheduling of smart appliances in smart grids

Recently there has been an increase of interest in implementing a new set of home appliances, known as Smart Appliances that integrate Information Technologies, the Internet of Things and the ability of communicating with other devices. While Smart Appliances are characterized as an important milestone on the path to the Smart Grid, by being able to automatically schedule their loads according to a tariff or reflecting the power that is generated using renewable sources, there is not a clear understanding on the impact that the behavior of such devices will have in the comfort levels of users, when they shift their working periods to earlier, or later than, a preset time. Given these considerations, in this work we analyse the results of an assessment survey carried out to a group of Home Appliance users regarding their habits when dealing with these machines and the subjective impact in quality caused by either finishing its programs before or after the time limit set by the user. The results of this work are expected to be used as input for the evaluation of load scheduling algorithms running in energy management systems. © 2014 Springer International Publishing.

Real-time scheduling in multicore : time- and space-partitioned architectures

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2014

Energy-aware Workflow Scheduling and Fuzzy Logic Based Capacity Provisioning in Cloud Environment

Thesis (Master's)--University of Washington, 2015

Identification of a Novel Recycling Sequence in the C-tail of FPR2/ALX Receptor: Association with Cell Protection from Apoptosis

Formyl-peptide receptor type 2 (FPR2; also called ALX because it is the receptor for lipoxin A4) sustains a variety of biological responses relevant to the development and control of inflammation, yet the cellular regulation of this G-protein-coupled receptor remains unexplored. Here we report that, in response to peptide agonist activation, FPR2/ALX undergoes β-arrestin-mediated endocytosis followed by rapid recycling to the plasma membrane. We identify a transplantable recycling sequence that is both necessary and sufficient for efficient receptor recycling. Furthermore, removal of this C-terminal recycling sequence alters the endocytic fate of FPR2/ALX and evokes pro-apoptotic effects in response to agonist activation. This study demonstrates the importance of endocytic recycling in the anti-apoptotic properties of FPR2/ALX and identifies the molecular determinant required for modulation of this process fundamental for the control of inflammation.

IMP: Imperial Metagenomics Pipeline for high-throughput sequence data

We have developed an in-house pipeline for the processing and analyses of sequence data generated during Illumina technology-based metagenomic studies of the human gut microbiota. Each component of the pipeline has been selected following comparative analysis of available tools; however, the modular nature of software facilitates replacement of any individual component with an alternative should a better tool become available in due course. The pipeline consists of quality analysis and trimming followed by taxonomic filtering of sequence data allowing reads associated with samples to be binned according to whether they represent human, prokaryotic (bacterial/archaeal), viral, parasite, fungal or plant DNA. Viral, parasite, fungal and plant DNA can be assigned to species level on a presence/absence basis, allowing – for example – identification of dietary intake of plant-based foodstuffs and their derivatives. Prokaryotic DNA is subject to taxonomic and functional analyses, with assignment to taxonomic hierarchies (kingdom, class, order, family, genus, species, strain/subspecies) and abundance determination. After de novo assembly of sequence reads, genes within samples are predicted and used to build a non-redundant catalogue of genes. From this catalogue, per-sample gene abundance can be determined after normalization of data based on gene length. Functional annotation of genes is achieved through mapping of gene clusters against KEGG proteins, and InterProScan. The pipeline is undergoing validation using the human faecal metagenomic data of Qin et al. (2014, Nature 513, 59–64). Outputs from the pipeline allow development of tools for the integration of metagenomic and metabolomic data, moving metagenomic studies beyond determination of gene richness and representation towards microbial-metabolite mapping. There is scope to improve the outputs from viral, parasite, fungal and plant DNA analyses, depending on the depth of sequencing associated with samples. The pipeline can easily be adapted for the analyses of environmental and non-human animal samples, and for use with data generated via non-Illumina sequencing platforms.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

A regulatory framework for short-term stochastic maintenance outage scheduling in smart grids

The current regulatory framework for maintenance outage scheduling in distribution systems needs revision to face the challenges of future smart grids. In the smart grid context, generation units and the system operator perform new roles with different objectives, and an efficient coordination between them becomes necessary. In this paper, the distribution system operator (DSO) of a microgrid receives the proposals for shortterm (ST) planned outages from the generation and transmission side, and has to decide the final outage plans, which is mandatory for the members to follow. The framework is based on a coordination procedure between the DSO and other market players. This paper undertakes the challenge of optimization problem in a smart grid where the operator faces with uncertainty. The results show the effectiveness and applicability of the proposed regulatory framework in the modified IEEE 34- bus test system.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.