Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method

This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. The methodology is based on a decision support system (DSS). The proposed methodology combines a genetic algorithm with a new local search using Monte Carlo Method. The methodology is applied to the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The methodology is tested on a set of standard instances taken from the literature and compared with others. The computation results validate the effectiveness of the proposed methodology. The DSS developed can be utilized in a common industrial or construction environment.

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

Implementation of fractional-order electromagnetic potential through a genetic algorithm

Several phenomena present in electrical systems motivated the development of comprehensive models based on the theory of fractional calculus (FC). Bearing these ideas in mind, in this work are applied the FC concepts to define, and to evaluate, the electrical potential of fractional order, based in a genetic algorithm optimization scheme. The feasibility and the convergence of the proposed method are evaluated.

Including context in a routing algorithm for the internet of things

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

Step count algorithm adapted to indoor localization

This paper presents a step count algorithm designed to work in real-time using low computational power. This proposal is our first step for the development of an indoor navigation system, based on Pedestrian Dead Reckoning (PDR). We present two approaches to solve this problem and compare them based in their error on step counting, as well as, the capability of their use in a real time system.

A lightweight distributed super peer election algorithm for unstructured dynamic P2P systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Geodupa - a geography-driven partial membership algorithm

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Development of a satellite based PM10 concentration product for urban areas

Dissertação para obtenção do Grau de Doutor em Engenharia do Ambiente

Diffusional kurtosis imaging using a fast heuristic constrained linear least squares algorithm: a plugin for OsiriX

Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis. This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE). The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed, after which, and with some few minor adjust-ments the plugin shall become a valid option for DKI computation

Development of principle culture medium formulations for Saccharomyces cerevisiae

Saccharomyces cerevisiae as well as other microorganisms are frequently used in industry with the purpose of obtain different kind of products that can be applied in several areas (research investigation, pharmaceutical compounds, etc.). In order to obtain high yields for the desired product, it is necessary to make an adequate medium supplementation during the growth of the microorganisms. The higher yields are typically reached by using complex media, however the exact formulation of these media is not known. Moreover, it is difficult to control the exact composition of complex media, leading to batch-to-batch variations. So, to overcome this problem, some industries choose to use defined media, with a defined and known chemical composition. However these kind of media, many times, do not reach the same high yields that are obtained by using complex media. In order to obtain similar yield with defined media the addition of many different compounds has to be tested experimentally. Therefore, the industries use a set of empirical methods with which it is tried to formulate defined media that can reach the same high yields as complex media. In this thesis, a defined medium for Saccharomyces cerevisiae was developed using a rational design approach. In this approach a given metabolic network of Saccharomyces cerevisiae is divided into a several unique and not further decomposable sub networks of metabolic reactions that work coherently in steady state, so called elementary flux modes. The EFMtool algorithm was used in order to calculate the EFM’s for two Saccharomyces cerevisiae metabolic networks (amino acids supplemented metabolic network; amino acids non-supplemented metabolic network). For the supplemented metabolic network 1352172 EFM’s were calculated and then divided into: 1306854 EFM’s producing biomass, and 18582 EFM’s exclusively producing CO2 (cellular respiration). For the non-supplemented network 635 EFM’s were calculated and then divided into: 215 EFM’s producing biomass; 420 EFM’s producing exclusively CO2. The EFM’s of each group were normalized by the respective glucose consumption value. After that, the EFMs’ of the supplemented network were grouped again into: 30 clusters for the 1306854 EFMs producing biomass and, 20 clusters for the 18582 EFM’s producing CO2. For the non-supplemented metabolic network the respective EFM’s of each metabolic function were grouped into 10 clusters. After the clustering step, the concentrations of the other medium compounds were calculated by considering a reasonable glucose amount and by accounting for the proportionality between the compounds concentrations and the glucose ratios. The approach adopted/developed in this thesis may allow a faster and more economical way for media development.

Acute pain in adults admitted to the emergency room: development and implementation of abbreviated guidelines.

AIM: Although acute pain is frequently reported by patients admitted to the emergency room, it is often insufficiently evaluated by physicians and is thus undertreated. With the aim of improving the care of adult patients with acute pain, we developed and implemented abbreviated clinical practice guidelines (CG) for the staff of nurses and physicians in our hospital's emergency room. METHODS: Our algorithm is based upon the practices described in the international literature and uses a simultaneous approach of treating acute pain in a rapid and efficacious manner along with diagnostic and therapeutic procedures. RESULTS: Pain was assessed using either a visual analogue scale (VAS) or a numerical rating scale (NRS) at ER admission and again during the hospital stay. Patients were treated with paracetamol and/or NSAID (VAS/NRS <4) or intravenous morphine (VAS/NRS > or =04). The algorithm also outlines a specific approach for patients with headaches to minimise the risks inherent to a non-specific treatment. In addition, our algorithm addresses the treatment of paroxysmal pain in patients with chronic pain as well as acute pain in drug addicts. It also outlines measures for pain prevention prior to minor diagnostic or therapeutic procedures. CONCLUSIONS: Based on published guidelines, an abbreviated clinical algorithm (AA) was developed and its simple format permitted a widespread implementation. In contrast to international guidelines, our algorithm favours giving nursing staff responsibility for decision making aspects of pain assessment and treatment in emergency room patients.