Rita Ribeiro

Uninova, CA3, Faculty Member

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Papers by Rita Ribeiro

Data normalisation techniques in decision making: case study with TOPSIS method

International Journal of Information and Decision Sciences, 2018

Data normalisation is essential for decision-making methods because data has to be numerical and ... more Data normalisation is essential for decision-making methods because data has to be numerical and comparable to be aggregated into a single score per alternative. In multi-criteria decision-making (MCDM), normalisation must convert criteria values into a common scale, thus, enabling rating and ranking of alternatives. Therefore, it is a challenge to select a suitable normalisation technique to represent an appropriate mapping from source data to a common scale. There are some attempts in the literature to address the subject of normalisation, but it is still an open question which technique is more appropriate for any MCDM method. Our research contribution is an assessment approach for evaluating normalisation techniques. Here, we focus on six well-known normalisation techniques and on TOPSIS method. The proposed assessment process provides a more robust evaluation and selection of the best normalisation technique for usage in TOPSIS.

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Continuous Reinforcement Operator applied to Resilience in Disaster Rescue Networks

Resilience measurement can be viewed as a multicriteria hierarchical decision making problem sinc... more Resilience measurement can be viewed as a multicriteria hierarchical decision making problem since calculating the final level of resilience involves measuring different criteria, at several hierarchical levels, and then merging the information together. In this paper, a resilience model for disaster rescue networks is discussed with a fullreinforcement operator, denoted continuous reinforcement operator. This approach is tested with different levels of reinforcement and the results are compared with those from a Fuzzy Inference System. The proposed approach offers interesting features to support balanced development of disaster rescue networks and facilitates managerial decisions by imposing standards for criteria to penalize or reward the information fusion process.

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Normalization techniques for collaborative networks

Kybernetes, Jun 19, 2019

PurposeNormalization is a crucial step in all decision models, to produce comparable and dimensio... more PurposeNormalization is a crucial step in all decision models, to produce comparable and dimensionless data from heterogeneous data. As such, various normalization techniques are available but their performance depends on a number of characteristics of the problem at hand. Thus, this study aims to introduce a recommendation framework for supporting users to select data normalization techniques that better fit the requirements in different application scenarios, based on multi-criteria decision methods.Design/methodology/approachFollowing the proposed approach, the authors compare six well-known normalization techniques applied to a case study of selecting suppliers in collaborative networks.FindingsWith this recommendation framework, the authors expect to contribute to improving the normalization of criteria in the evaluation and selection of suppliers and business partners in dynamic networked collaborative systems.Originality/valueThis is the first study about comparing normalization techniques for selecting the best normalization in dynamic multiple-criteria decision-making models in collaborative networks.

Assessing Normalization Techniques for TOPSIS Method

IFIP Advances in Information and Communication Technology

In recent years, data normalization is receiving considerable attention due to its essential role... more In recent years, data normalization is receiving considerable attention due to its essential role in decision problems. Especially, considering the new developments in Big data and Artificial Intelligent to handle heterogeneous data from sensors, normalization&#39;s role as a preprocessing step for complex decision problems is more distinguished. However, selecting the best normalization technique among several introduced techniques in the literature is still an open issue. In this study we focus on evaluating normalization techniques in Multi-Criteria Decision Making (MCDM) methods namely for Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to recommend the most proper technique. A small numerical example, borrowed from literature, is used to show the applicability of the proposed assessment framework using several metrics for recommending the most suitable technique. This study helps decision makers to improve the accuracy of the final ranking of results in decision problems by selecting the best normalization technique for the related case study.

Assessing Normalization Techniques for Simple Additive Weighting Method

Procedia Computer Science, 2022

One of the current topics of attention in data analysis is the selection of the best normalizatio... more One of the current topics of attention in data analysis is the selection of the best normalization technique in the aggregation process when using Multi-Criteria Decision Making (MCDM) methods for solving decision problems. This is particularly critical in complex collaborative decision-making systems dealing with a large variety of heterogeneous data sources. Using different normalization techniques may result in different rankings of alternatives. So, enhancing the accuracy of the final ranking of alternatives could be achieved by selecting the most proper normalization techniques for each MCDM decision problem. In this direction, several attempts have been carried out, however, the lack of coherence and lack of a robust assessment framework persist. This situation encouraged the authors to propose an assessment framework that is enriched with several metrics for the evaluation of different normalization techniques in MCDM problems with the focus on partner/supplier selection in collaborative networks. As an illustration of the approach, in this work we assess different normalization techniques with the Simple Additive Weighting (SAW) method using metrics from the proposed assessment framework and select the most adequate technique for a small case study that is borrowed from literature. The suggested approach contributes to increasing the accuracy of final results for MCDM methods.

Selecting Normalization Techniques for the Analytical Hierarchy Process

IFIP Advances in Information and Communication Technology, 2020

published version of the paper. Such differences, if any, are usually due to reformatting during ... more

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The Role of Decision Making in the Big Data Era

Currently data heavily, constantly, and globally flows into all areas of our economy. Individuals... more Currently data heavily, constantly, and globally flows into all areas of our economy. Individuals and mainly organizations have to tackle the problem of processing large data in support of their respective needs and operations, aiming at improving their manageability and efficiency. Big Data urges for advances in technology and cannot count anymore with classical database tools to manage and analyze information data-sets. This work positions the importance of Decision Making and DSS to exploit Big Data analysis so that organizations can get ready to compete with high productivity.

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Interoperable Decision Support System Based on Multivariate Time Series for Setup Data Processing and Visualization

Advances in Intelligent Systems and Computing, 2020

Nowadays, interoperable decision support systems play a crucial role to improve production activi... more Nowadays, interoperable decision support systems play a crucial role to improve production activity control in industrial Companies, and enable them to face the growing exigencies imposed by the arising Industry 4.0 era. In this paper an interoperable decision support system based on multivariate time series for setup data processing and visualization is put forward. The proposed system is described, in the context of a general architecture presented, and its application through an illustrative example from a stamping factory is analysed. Through the case study it is possible to realize about the importance of the proposed system, and its suitability of application to other companies, for instance in other industrial sectors and manufacturing environments.

Selection of Normalization Technique for Weighted Average Multi-criteria Decision Making

IFIP Advances in Information and Communication Technology, 2018

One of the main challenges when evaluating resilience frameworks is the aggregation of different ... more One of the main challenges when evaluating resilience frameworks is the aggregation of different criteria. To calculate the final level of resilience we have to measure different criteria and then fuse the information to obtain a score. Normalization is a crucial step in any decision making process of evaluation of alternatives or frameworks. Normalization transforms heterogeneous criteria data (qualitative, quantitative, different units, etc.) into numerical and comparable data to enable aggregation (fusion) of criteria to determine the rating of decision alternatives. In this study, we evaluate the effects of different normalization techniques on the most well-known multi-criteria (MCDM) method, called Weighted Average (WA) or SAW (Simple Additive Weighting). A small case study for selecting resilience frameworks illustrates our assessment process for selecting the suitable normalization technique.

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends

The great abundance of multi-sensor traffic data (traditional traffic data sources loops, cameras... more The great abundance of multi-sensor traffic data (traditional traffic data sources loops, cameras and radars accompanied or even replaced by the most recent Bluetooth detectors, GPS enabled floating car data) although offering the chance to exploit Big Data advantages in traffic planning, management and monitoring, has also opened the debate on data cleaning, fusion and interpretation techniques. The current paper concentrates on floating taxi data in the case of a Greek city, Thessaloniki city, and proposes the use of advanced spatiotemporal dynamics identification techniques among urban road paths for gaining a deep understanding of complex relations among them. The visualizations deriving from the advanced time series analysis proposed (hereinafter referred also as knowledge graphs) facilitate the understanding of the relations and the potential future reactions/outcomes of urban traffic management and calming interventions, enhances communication potentials (useful and consumabl...

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On the Impact of Big Data Analytics in Decision-Making Processes

Integrated Series in Information Systems, 2021

Comparison of Normalization Techniques on Data Sets With Outliers

International Journal of Decision Support System Technology, 2022

With the fast growing of data-rich systems, dealing with complex decision problems with skewed in... more With the fast growing of data-rich systems, dealing with complex decision problems with skewed input data sets and respective outliers is unavoidable. Generally, data skewness refers to a non-uniform distribution in a dataset, i.e. a dataset which contains asymmetries and/or outliers. Normalization is the first step of most multi-criteria decision making (MCDM) problems to obtain dimensionless data, from heterogeneous input data sets, that enable aggregation of criteria and thereby ranking of alternatives. Therefore, when in presence of outliers in criteria datasets, finding a suitable normalization technique is of utmost importance. As such, in this work, we compare seven normalization techniques (Max, Max-Min, Vector, Sum, Logarithmic, Target-based, and Fuzzification) on criteria datasets, which contain outliers to analyse their results for MCDM problems. A numerical example illustrates the behaviour of the chosen normalization techniques and an (ongoing) evaluation assessment fra...

Data fusion approach for eucalyptus trees identification

International Journal of Remote Sensing, 2021

Remote sensing is based on the extraction of data, acquired by satellites or aircrafts, through m... more Remote sensing is based on the extraction of data, acquired by satellites or aircrafts, through multispectral images, that allow their remote analysis and classification. Analysing those images with data fusion techniques is a promising approach for identification and classification of forest types. Fusion techniques can aggregate various sources of heterogeneous information to generate valueadded maps, facilitating forest-type classification. This work applies a data fusion algorithm, denoted FIF (Fuzzy Information Fusion), which combines computational intelligence techniques with multicriteria concepts and techniques, to automatically distinguish Eucalyptus trees from satellite images. The algorithm customization was performed with a Portuguese area planted with Eucalyptus. After customizing and validating the approach with several representative scenarios to assess its suitability for automatic classification of Eucalyptus, we tested on a large tile obtaining a sensitivity of 69.61%, with a specificity of 99.43%, and an overall accuracy of 98.19%. This work demonstrates the potential of our approach to automatically classify specific forest types from satellite images, since this is a novel approach dedicated to the identification of eucalyptus trees.

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A data fusion approach for business partners selection

International Journal of Information and Decision Sciences, 2018

Strategies of market diversification push companies to provide novel products and services to cus... more Strategies of market diversification push companies to provide novel products and services to customers, belonging to new geographic and demographic segments. Additionally, market development strategies targeting non-buying customers in selected segments or new buyers in new segments may be paired with increased product diversification and improved business agility. To fulfil the requirements associated with manufacturing, a wider range of products and increased customised demands imply having a wider set of competences available. Most companies find it increasingly difficult to have all required competences in their internal structures; therefore, they need to rely on strategic business partnerships and suppliers to be successful. In this paper, we discuss a data fusion decision approach for supplier and business partner evaluation, which includes past, current and forecast information about business partners. This approach may prove vital for companies to establish strong collaborative business networks.

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Collaborative framework for virtual organisation synthesis based on a dynamic multi-criteria decision model

International Journal of Computer Integrated Manufacturing, 2018

Globalisation and increased competition pushes companies to grow outside political and geographic... more Globalisation and increased competition pushes companies to grow outside political and geographical boundaries, frequently forcing them to increase product diversification and optimise their relation with potential and effective partners and customers. In this scenario, implementing agile collaboration networks with business partners is a requirement. In this article the authors propose a collaborative framework based on a model for Historical, Present and Future evaluations, which in turn is based on a Dynamic Multi-criteria Decision Method using software agents and the Cloud for Collaborative Networked and Virtual Organization Synthesis. The model uses a dynamic decision approach for supplier and business partner evaluation, enabling software agents to autonomously capture business opportunities, select business partners/suppliers, award and process associated orders.

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GaiaData Release 1

Astronomy & Astrophysics, 2016

Context. The first data release from the Gaia mission contains accurate positions and magnitudes ... more Context. The first data release from the Gaia mission contains accurate positions and magnitudes for more than a billion sources, and proper motions and parallaxes for the majority of the 2.5 million Hipparcos and Tycho-2 stars. Aims. We describe three essential elements of the initial data treatment leading to this catalogue: the image analysis, the construction of a source list, and the near real-time monitoring of the payload health. We also discuss some weak points that set limitations for the attainable precision at the present stage of the mission. Methods. Image parameters for point sources are derived from one-dimensional scans, using a maximum likelihood method, under the assumption of a line spread function constant in time, and a complete modelling of bias and background. These conditions are, however, not completely fulfilled. The Gaia source list is built starting from a large ground-based catalogue, but even so a significant number of new entries have been added, and a large number have been removed. The autonomous onboard star image detection will pick up many spurious images, especially around bright sources, and such unwanted detections must be identified. Another key step of the source list creation consists in arranging the more than 10 10 individual detections in spatially isolated groups that can be analysed individually. Results. Complete software systems have been built for the Gaia initial data treatment, that manage approximately 50 million focal plane transits daily, giving transit times and fluxes for 500 million individual CCD images to the astrometric and photometric processing chains. The software also carries out a successful and detailed daily monitoring of Gaia health.

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A Knowledge Based System for Supporting Sustainable Industrial Management in a Clothes Manufacturing Company Based on a Data Fusion Model

Lecture Notes in Business Information Processing, 2016

In this paper we propose a knowledge based system (KBS), based on smart objects and a data fusion... more In this paper we propose a knowledge based system (KBS), based on smart objects and a data fusion model to support industrial management decision making applied to a clothes manufacturing enterprise. The management processes cover factory-production levels to higher decision-making levels. Therefore, the proposed KBS contributes to solving different kind of decision problems, including factory supervision, production planning and control, productivity management, real-time monitoring, and data acquisition and processing. The web access via different middleware devices and tools at different process levels, along with the use of integrated algorithms, decision methods, and smart objects, promote an optimized use of knowledge and resources. In this paper the proposed KBS is introduced and an example of its use is illustrated with an example of a clothes manufacturing resources selection, using the embedded dynamic multi-criteria fusion model.

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Swarm intelligence for Decision Support in Operations Management

In Dargam F Delibasic B Hern Ndez J E Liu S Ribeiro R Zarat P Decision Support Systems Operations Management Trends and Solutions in Industries Euro Working Group For Decision Support System 12 Apr 2012 13 Apr 2012 Liverpool Uk Ewgss Wordpress 2012, Apr 12, 2012

Contributions to adaptable agents societies

Introduction of Empirical Topology in Construction of Relationship Networks of Informative Objects

IFIP Advances in Information and Communication Technology, 2010

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