851 resultados para driver information systems, genetic algorithms, prediction theory, transportation
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This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.
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The growing pressure to increase the quality of health services, as well as reducing costs, has caused healthcare organizations to increase the use of Information and Communication Technologies (ICT) through the development and adoption of Healthcare Information Systems (HIS). However, the need for exchange of information between HIS and between organizations has also increased, resulting in the problem of interoperability. This problem is considered complex, but the use of Service Oriented Architecture (SOA) appears as a good way to address this issue. This paper presents a systematic review, performed in order to find out how and in which contexts SOA is being used to ensure the interoperability of HIS.
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In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of genetic algorithms for the solution of a nurse rostering problem at a major UK hospital. The hospital is made up of wards of up to 30 nurses. Each ward has its own group of nurses whose shifts have to be scheduled on a weekly basis. In addition to fulfilling the minimum demand for staff over three daily shifts, nurses’ wishes and qualifications have to be taken into account. The schedules must also be seen to be fair, in that unpopular shifts have to be spread evenly amongst all nurses, and other restrictions, such as team nursing and special conditions for senior staff, have to be satisfied. The basis of the family of genetic algorithms is a classical genetic algorithm consisting of n-point crossover, single-bit mutation and a rank-based selection. The solution space consists of all schedules in which each nurse works the required number of shifts, but the remaining constraints, both hard and soft, are relaxed and penalised in the fitness function. The talk will start with a detailed description of the problem and the initial implementation and will go on to highlight the shortcomings of such an approach, in terms of the key element of balancing feasibility, i.e. covering the demand and work regulations, and quality, as measured by the nurses’ preferences. A series of experiments involving parameter adaptation, niching, intelligent weights, delta coding, local hill climbing, migration and special selection rules will then be outlined and it will be shown how a series of these enhancements were able to eradicate these difficulties. Results based on several months’ real data will be used to measure the impact of each modification, and to show that the final algorithm is able to compete with a tabu search approach currently employed at the hospital. The talk will conclude with some observations as to the overall quality of this approach to this and similar problems.
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Assessment processes are essential to guarantee quality and continuous improvement of software in healthcare, as they measure software attributes in their lifecycle, verify the degree of alignment between the software and its objectives and identify unpredicted events. This article analyses the use of an assessment model based on software metrics for three healthcare information systems from a public hospital that provides secondary and tertiary care in the region of Ribeirão Preto. Compliance with the metrics was investigated using questionnaires in guided interviews of the system analysts responsible for the applications. The outcomes indicate that most of the procedures specified in the model can be adopted to assess the systems that serves the organization, particularly in the attributes of compatibility, reliability, safety, portability and usability.
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Declarative techniques such as Constraint Programming can be very effective in modeling and assisting management decisions. We present a method for managing university classrooms which extends the previous design of a Constraint-Informed Information System to generate the timetables while dealing with spatial resource optimization issues. We seek to maximize space utilization along two dimensions: classroom use and occupancy rates. While we want to maximize the room use rate, we still need to satisfy the soft constraints which model students’ and lecturers’ preferences. We present a constraint logic programming-based local search method which relies on an evaluation function that combines room utilization and timetable soft preferences. Based on this, we developed a tool which we applied to the improvement of classroom allocation in a University. Comparing the results to the current timetables obtained without optimizing space utilization, the initial versions of our tool manages to reach a 30% improvement in space utilization, while preserving the quality of the timetable, both for students and lecturers.
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The 10th European Conference on Information Systems Management is being held at The University of Evora, Portugal on the 8 /9 September 2016. The Conference Chair is Paulo Silva and the Programme Chairs are Prof. Rui Quaresma and Prof. António Guerreiro. ECISM provides an opportunity for individuals researching and working in the broad field of information systems management, including IT evaluation to come together to exchange ideas and discuss current research in the field. This has developed into a particularly important forum for the present era, where the modern challenges of managing information and evaluating the effectiveness of related technologies are constantly evolving in the world of Big Data and Cloud Computing. We hope that this year’s conference will provide you with plenty of opportunities to share your expertise with colleagues from around the world. The keynote speakers for the Conference are Carlos Zorrinho from the Portuguese Delegation and Isabel Ramos from University of Minho, Portugal. ECISM 2016 received an initial submission of 84 abstracts. After the double blind peer review process 25 aca demic papers, 7 PhD research papers, 3 Masters research paper and 5 work in progress papers have been ac cepted for publication in these Conference Proceedings. These papers represent research from around the world, including Belgium, Brazil, China, Czech Republic, Kazakhstan, Malaysia, New Zealand, Norway, Oman, Poland, Portugal, South Africa, Sweden, The Netherlands, UK and Vietnam.
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Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.
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This paper compares the performance of the complex nonlinear least squares algorithm implemented in the LEVM/LEVMW software with the performance of a genetic algorithm in the characterization of an electrical impedance of known topology. The effect of the number of measured frequency points and of measurement uncertainty on the estimation of circuit parameters is presented. The analysis is performed on the equivalent circuit impedance of a humidity sensor.
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This paper proposes an algorithm to estimate two parameter values vs, transcription of frq gene, and vd, maximum rate of FRQ protein degradation for an existing 3rd order Neurospora model in literature. Details of the algorithm with simulation results are shown in this paper.
USE AND CONSEQUENCES OF PARTICIPATORY GIS IN A MEXICAN MUNICIPALITY: APPLYING A MULTILEVEL FRAMEWORK
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This paper seeks to understand the use and the consequences of Participatory Geographic Information System (PGIS) in a Mexican local community. A multilevel framework was applied, mainly influenced by two theoretical lenses – structurationist view and social shaping of technology – structured in three dimensions – context, process and content – according to contextualist logic. The results of our study have brought two main contributions. The first is the refinement of the theoretical framework in order to better investigate the implementation and use of Information and Communication Technology (ICT) artifacts by local communities for social and environmental purposes. The second contribution is the extension of existing IS (Information Systems) literature on participatory practices through identification of important conditions for helping the mobilization of ICT as a tool for empowering local communities.
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Topology optimization consists in finding the spatial distribution of a given total volume of material for the resulting structure to have some optimal property, for instance, maximization of structural stiffness or maximization of the fundamental eigenfrequency. In this paper a Genetic Algorithm (GA) employing a representation method based on trees is developed to generate initial feasible individuals that remain feasible upon crossover and mutation and as such do not require any repairing operator to ensure feasibility. Several application examples are studied involving the topology optimization of structures where the objective functions is the maximization of the stiffness and the maximization of the first and the second eigenfrequencies of a plate, all cases having a prescribed material volume constraint.
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This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical evaluation.
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A negociação é a ferramenta mais eficiente de conseguir algo que se deseja, ela ocorre quando existem conflitos e alternativas a serem selecionadas que podem envolver toda a empresa. As diversas alternativas apresentam interesses comuns e conflitantes, expressando a complexidade das relações. Com a crescente demanda por agilidade na resposta aos novos perfis de procura, as organizações precisam ser mais versáteis nos processos e mais rápidas para reagir às mudanças do mercado, e as Negociações de Ganhos Mútuos (NGM) são uma forma atual de condução de criação de valor. Esta dissertação tem como objetivo propor a utilização das ferramentas da teoria de NGM como instrumento apoiador aos gestores de compras públicas adquirentes de produtos e serviços de TI a atingir os resultados esperados. Para isso, foi realizada uma pesquisa de natureza descritiva-exploratória por meio de abordagem qualitativa. Para atingir este objetivo, foram feitos estudos teóricos das temáticas: Flexibilidade Organizacional, Gestão de Compras Públicas, Sistemas de Informação, Alinhamento Estratégico e Teoria da Negociação, de forma a se obter um melhor entendimento da pesquisa. Foi elaborado um questionário não estruturado, sendo este o instrumento de pesquisa que foi utilizado neste estudo. Aplicado o questionário diretamente aos participantes, obteve-se uma participação de 10 respondentes, sendo todos participantes dos processos licitatórios na empresa pública Delta. Uma vez efetuada a coleta de dados, foram analisadas as respostas utilizando uma modalidade da técnica de análise de conteúdo, chamada pattern-matching, com o propósito de comparar os resultados com o referencial teórico utilizado no estudo. Como resultado do estudo, identificou-se a utilização da abordagem distributiva nos processos licitatórios.
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