Negotiation mechanism for self-organized scheduling system with collective intelligence

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

New findings on the dynamics of HIV and TB coinfection models

In this paper we study a model for HIV and TB coinfection. We consider the integer order and the fractional order versions of the model. Let α∈[0.78,1.0] be the order of the fractional derivative, then the integer order model is obtained for α=1.0. The model includes vertical transmission for HIV and treatment for both diseases. We compute the reproduction number of the integer order model and HIV and TB submodels, and the stability of the disease free equilibrium. We sketch the bifurcation diagrams of the integer order model, for variation of the average number of sexual partners per person and per unit time, and the tuberculosis transmission rate. We analyze numerical results of the fractional order model for different values of α, including α=1. The results show distinct types of transients, for variation of α. Moreover, we speculate, from observation of the numerical results, that the order of the fractional derivative may behave as a bifurcation parameter for the model. We conclude that the dynamics of the integer and the fractional order versions of the model are very rich and that together these versions may provide a better understanding of the dynamics of HIV and TB coinfection.

Estudo e integração de business models e a sua aplicação em smart-ups e projetos portugueses

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração do Porto para obtenção do Grau de Mestre em Gestão das Organizações, Ramo Gestão de Empresas Orientador: Professor Doutor Eduardo Manuel Lopes de Sá e Silva Co-orientador: Mestre Maria de Fátima Mendes Monteiro

Maturity models in the hospital information systems management initial research

Os modelos de maturidade são instrumentos facilitadores da gestão das organizações, incluindo a gestão da sua função sistemas de informação, não sendo exceção as organizações hospitalares. Neste artigo apresenta-se uma investigação inicial que visa o desenvolvimento de um abrangente modelo de maturidade para a gestão dos sistemas de informação hospitalares. O desenvolvimento deste modelo justifica-se porque os modelos de maturidade atuais no domínio da gestão dos sistemas informação hospitalares ainda se encontram numa fase embrionária de desenvolvimento, sobretudo porque são pouco detalhados, não disponibilizam ferramentas para determinação da maturidade e não apresentam as características dos estágios de maturidade estruturadas por diferentes fatores de influência.

Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

Crowdsourcing innovation: a strategy to leverage enterprise innovation

Innovation is recognized by academics and practitioners as an essential competitive enabler for any company to survive, to remain competitive and to grow. Investments in tasks of R&D have not always brought the expected results. But that doesn't mean that the outcomes would not be useful to other companies of the same business area or even from another area. Thus, there is much knowledge already available in the market that can be helpful to some and profitable to others. So, the ideas and expertise can be found outside a company's boundaries and also exported from within. Information, knowledge, experience, wisdom is already available in the millions of the human beings of this planet, the challenge is to use them through a network to produce new ideas and tips that can be useful to a company with less costs. This was the reason for the emergence of the area of crowdsourcing innovation. Crowdsourcing innovation is a way of using the Web 2.0 tools to generate new ideas through the heterogeneous knowledge available in the global network of individuals highly qualified and with easy access to information and technology. So, a crowdsourcing innovation broker is an organization that mediates the communication and relationship between the seekers - companies that aspire to solve some problem or to take advantage of any business opportunity - with a crowd that is prone to give ideas based on their knowledge, experience and wisdom. This paper makes a literature review on models of open innovation, crowdsourcing innovation, and technology and knowledge intermediaries, and discusses this new phenomenon as a way to leverage the innovation capacity of enterprises. Finally, the paper outlines a research design agendafor explaining crowdsourcing innovation brokering phenomenon, exploiting its players, main functions, value creation process, and knowledge creation in order to define a knowledge metamodel of such intermediaries.