Using weight aggregation in tabu search for multiobjective exams timetabling problem

EnExams timetabling is a difficult task in many educational institutions. We can distinct two major sets of constraints when defining exams timetabling problems, categorized in soft and hard constraints. Guaranteing that any student as a non overlapping exams schedule and that necessary requirements like rooms and teacher are available are consider hard constraints. An evenly distributed schedule, a short duration of the overall exams period can be regarded as soft constraints. To handle soft constraints under the hard constraints verification we adopted a multiobjective optimization approach. This problem is NP-hard for which we have developed an heuristic tabu search method to find a solution. Tabu search comprises an iterative local search defined as a neighborhood inspection of a certain point in the search space. To find an improved solution we have to evaluate points in this neighborhood which can be considered a multiple attribute decision problem. In this context we have used multicriteria methods in order to rank the solutions

Contribution on some construction methods for aggregation functions

Abstract. In this paper, based on [14], we present some well established construction methods for aggregation functions as well as some new ones

An integrated framework for supporting fuzzy decision-making in networked manufacturing environments

In this paper we propose an integrated framework, based on smart objects to support fuzzy decision-making processes applied to manufacturing environments. The processes involved range from factory-production level up to higher decision-making levels, either in the context of traditional single enterprises, up to the one of supply chains and distributed and ubiquitous manufacturing environments. Therefore, the proposed framework promotes contributions for solving different kind of problems, including, among others: networked supply chain management; production planning and control; factory supervision and productivity management; real-time monitoring; data acquisition and processing. The web access via different middleware devices and tools at different process levels, along with the use of integrated algorithms and smart objects, which is possible and will promote an optimized use of knowledge and resources for supporting better decision-making. Moreover, the proposed framework also aims at promoting a wider collaboration process among various groups of stakeholders.This work was supported by FCT “Fundação para a Ciência e a Tecnologia” under the program: PEst20152020.info:eu-repo/semantics/publishedVersio

Spatial-temporal business partnership selection in uncertain environments

Small and Medium (SME) companies are facing growing challenges while trying to implement globalized business strategies. Contemporary business models need to account for spatial-temporal changeable environments, where lack of confidence and uncertainty in data are a reality. Further, SMEs are finding it increasingly difficult to include all required competences in their internal structures; therefore, they need to rely on reliable business and supplier partnerships to be successful. In this paper we discuss a spatial-temporal decision approach capable of handling lack of confidence and imprecision on current and/or forecast data. An illustrative case study of business' partner selection demonstrates the approach suitability, which is complemented by a statistical analysis with different levels of uncertainty to assess its robustness in uncertain environments.The authors wish to acknowledge the support of the Fundacao para a Ciencia e Tecnologia (FCT), Portugal, through the grant: "Projeto Estrategico - PEst2015-2020, reference: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Aggregation functions based on penalties

This article studies a large class of averaging aggregation functions based on minimizing a distance from the vector of inputs, or equivalently, minimizing a penalty imposed for deviations of individual inputs from the aggregated value. We provide a systematization of various types of penalty based aggregation functions, and show how many special cases arise as the result. We show how new aggregation functions can be constructed either analytically or numerically and provide many examples. We establish connection with the maximum likelihood principle, and present tools for averaging experimental noisy data with distinct noise distributions.<br /

Data fusion approach for eucalyptus trees identification

UIDB/00066/2020 DSAIPA/AI/0100/2018Remote 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 value-added 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.publishersversionpublishe

Search methodologies for efficient planetary site selection

Abstract—Landing on distant planets is always a challenging task due to the distance and hostile environments found. In the design of autonomous hazard avoidance systems we find the particularly relevant task of landing site selection, that has to operate in real-time as the lander approaches the planet’s surface. Seeking to improve the computational complexity of previous approaches to this problem, we propose the use of non-exhaustive search methodologies. A comparative study of several algorithms, such as Tabu Search and Particle Swarm Optimization, was performed. The results are very promising, with Particle Swarm Optimization showing the capacity to consistently produce solutions of very high quality, on distinct landing scenarios. I

Failure Mode and Effects Analysis Using Generalized Mixture Operators

Failure mode and effects analysis (FMEA) is a method based on teamwork to identify potential failures and problems in a system, design, process and service in order to remove them. The important part of this method is determining the risk priorities of failure modes using the risk priority number (RPN). However, this traditional RPN method has several shortcomings. Therefore, in this paper we propose a FMEAwhich uses generalized mixture operators to determine and aggregate the risk priorities of failure modes. In a numerical example, a FMEA of the LGS gas type circuit breaker product in Zanjan Switch Industries in Iran is presented to further illustrate the proposed method. The results show that the suggested approach is simple and provides more accurate risk assessments than the traditional RPN