Determinação de DTR de pedaços de morango num tanque de mistura em presença de fluidos newtonianos e não-newtonianos

Em indústrias de processamento de frutas, o estudo da distribuição dos tempos de residência (DTR) torna-se útil para determinar o melhor grau de mistura e garantir uma saída uniforme dos pedaços de fruta (morangos). A DTR de partículas sólidas num fluido varia com vários factores tais como a reologia do meio, a velocidade de agitação, a concentração de sólidos em suspensão, as dimensões do tanque, o tipo de agitador e o caudal de saída. Os melhores ensaios resultaram na combinação de um tanque de 50 L agitado com um impulsor em âncora independentemente da reologia do meio. Para o fluido Newtoniano (água), a distribuição tornase mais uniforme quando se combina um caudal de 2,50 L/s, uma velocidade de agitação de 78 rpm e uma concentração de sólidos em suspensão de 6 %. No fluido não-Newtoniano estudado (alginato 0,67 % (m/v)), com reologia semelhante ao da polpa de morango, a melhor distribuição é obtida com um caudal de 0,31 L/s, uma velocidade de agitação de 80 rpm e uma concentração de sólidos de 2 %. Pela comparação dos resultados obtidos para o tanque em estudo com casos ideais, verificou-se que este tende a assemelhar-se a um reactor pistão com dispersão axial ou a uma combinação de um reactor perfeitamente agitado com um reactor pistão e volumes mortos

Hybrid approaches to optimization and machine learning methods

This paper conducts a comprehensive literature review concerning hybrid techniques that combine optimization and machine learning approaches for clustering and classification problems. The aim is to identify the potential benefits of integrating these methods to address challenges in both fields. The paper outlines optimization and machine learning methods and provides a quantitative overview of publications since 1970. Additionally, it offers a detailed review of recent advancements in the last three years. The study includes a SWOT analysis of the top ten most cited algorithms from the collected database, examining their strengths and weaknesses as well as uncovering opportunities and threats explored through hybrid approaches. Through this research, the study highlights significant findings in the realm of hybrid methods for clustering and classification, showcasing how such integrations can enhance the shortcomings of individual techniques.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), Algoritmi (UIDB/00319/2020) and SusTEC (LA/P/0007/2021). Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021

A collaborative multi-objective approach for clustering task based on distance measures and clustering validity indices

First Online: 28 December 2023Clustering algorithm has the task of classifying a set of elements so that the elements within the same group are as similar as possible and, in the same way, that the elements of different groups (clusters) are as different as possible. This paper presents the Multi-objective Clustering Algorithm (MCA) combined with the NSGA-II, based on two intra- and three inter-clustering measures, combined 2-to-2, to define the optimal number of clusters and classify the elements among these clusters. As the NSGA-II is a multi-objective algorithm, the results are presented as a Pareto front in terms of the two measures considered in the objective functions. Moreover, a procedure named Cluster Collaborative Indices Procedure (CCIP) is proposed, which aims to analyze and compare the Pareto front solutions generated by different criteria (Elbow, Davies-Bouldin, Calinski-Harabasz, CS, and Dumn indices) in a collaborative way. The most appropriate solution is suggested for the decision-maker to support their final choice, considering all solutions provided by the measured combination. The methodology was tested in a benchmark dataset and also in a real dataset, and in both cases, the results were satisfactory to define the optimal number of clusters and to classify the elements of the dataset.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope UIDB/00319/2020, UIDB/05757/2020, UIDP/05757/2020 and Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021

Implementation of robust multi-objective optimization in the build orientation problem

Additive manufacturing (AM) is an emerging technology to create 3D objects layer-by-layer directly from a 3D CAD model. The build orientation is a critical issue in AM and its optimization will significantly reduce the building costs and improve object accuracy. This paper aims to optimize the build orientation problem of a 3D CAD model using a robust multi-objective approach, taking into account the staircase effect and the support area characteristics. Thus, themain objective is to obtain a robust Pareto optimal front, composed of solutions that are not quite sensitive to perturbations in the variables. In this manner, a set of robust solutions is presented as alternatives and the decision-maker can identify the compromise solutions and choose according to his/her preferences.This work has been developed under the FIBR3D project - Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Periodic vehicle routing problem in a health unit

In logistics of home health care services in the Health Units, the managers and nurses need to carry out the schedule and the vehicles routes for the provision of care at the patients' homes. Currently, in Portugal, these services are increasingly used but the problem is still, usually, solved manually and without computational resources. The increased demand for home health care due to the boost of the elderly people number entails a high associated cost which, sometimes, does not guarantee the quality of the service. In this sense, the periodic vehicle routing problem is a generalization of the classical vehicle routing problem in which routes are determined for a time horizon of several days. In this work, it is provided a periodic vehicle routing problem applied in the Health Unit in Bragança. An integer linear programming formulation for the real database, allowed to solve the problem in an efficient and optimized way using the CPLEXR software.Programa Operacional Temático Factores de Competitividade(POCI-01-0145-FEDER-007043

Automatic nurse allocation based on a population algorithm for home health care

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing and it is necessary to perform home care services. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurses teams from Health Units, requiring some logistics for this purpose. Usually, the visits are manually planned and without computational support. The main goal of this work is to carry out the automatic nurse’s allocation of home care visits, of one Bragança Health Unit, in order to minimize the nurse’s workload balancing, spent time in all home care visits and, consequently, reduce the costs involved. The developed methodology was coded in MatLab Software and the problems were efficiently solved by the particle swarm optimization method. The nurse’s allocation solution of home care visits for the presented case study shows a significant improvement and reduction in the maximum time, in the nurse workload balancing, as well as the patients waiting time.This work has been supported by COMPETE:POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the project UID/CEC/00319/2019

Multi-agent system specification for distributed scheduling in home health care

Nowadays, scheduling and allocation of resources and tasks becomes a huge and complex challenge to the most diverse industrial areas, markets, services and health. The problem with current scheduling systems is that their management is still done manually or using classical optimization methods (usually static, time-consuming) and centralized approaches. However, opportunities arise to decentralize solutions with smart systems, which enable the distribution of the computational effort, the flexibility of behaviours and the minimization of operating times and operational planning costs. The paper proposes the specification of a Multi-agent System (MAS) for the Home Health Care (HHC) scheduling and allocation. The MAS technology enables the scheduling of intelligent behaviours and functionalities based on the interaction of agents, and allows the evolution of current strategies and algorithms, as it can guarantee the fast response to condition changes, flexibility and responsiveness in existing planning systems. An experimental HHC case study was considered to test the feasibility and effectiveness of the proposed MAS approach, the results demonstrating promising qualitative and quantitative indicators regarding the efficiency and responsiveness of the HHC scheduling.This work has been supported by FCT—Fundação para a Ciência e a Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020 and UIDB/05757/2020. Filipe Alves is supported by FCT Doctorate Grant Reference SFRH/BD/143745/2019

Build orientation optimization of car hoodvent with additive manufacturing

Additive manufacturing is a widely used process consisting in the building of a three-dimensional (3D) object from a model projected on a computer, adding the material layer-by-layer. This technology allows the printing of complex shape objects and is being increasingly adopted by the aircraft industry, medical implants, jewelry, footwear, automotive, fashion products, among others. The build orientation optimization of 3D models has a great influence on costs and surface quality when printing three-dimensional objects. In this work, three build orientation optimization problems are studied: single objective problem, bi-objective problem and many-objective problem. To this end, three quality measures are applied: the support area, the build time and the surface roughness, for the Car Hoodvent model. First, a single-objective optimization problem is presented and solved by the genetic algorithm, obtaining optimal solutions for each objective function. Then, the study of the bi-objective optimization problem is carried out for each pair of two objectives and some representative trade-off solutions are identified. Finally, the study of the many objective optimization problem, considering the three measures optimized simultaneously, is presented with some more optimal solutions found. The bi-objective and many-objective problems are solved by a multi-objective genetic algorithm. For a better analysis and comparison of the solutions found, the Pareto fronts are used, enabling a better visualization of the solutions between the objectives. This study aims to assist the decision-maker in choosing the best part print orientation angles according to his/her preferences. The optimal solutions found confirmed the effectiveness of the proposed approach.info:eu-repo/semantics/publishedVersio

Evaluating student behaviour on the MathE Platform - clustering algorithms approaches

The MathE platform is an online educational platform that aims to help students who struggle to learn college mathematics as well as students who wish to deepen their knowledge on subjects that rely on a strong mathematical background, at their own pace. The MathE platform is currently being used by a significant number of users, from all over the world, as a tool to support and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills. This paper is addressed to evaluate the students’ performance on the Linear Algebra topic, which is a specific topic of the MathE platform. In order to achieve this goal, four clustering algorithms were considered; three of them based on different bio-inspired techniques and the k-means algorithm. The results showed that most students choose to answer only basic level questions, and even within that subset, they make a lot of mistakes. When students take the risk of answering advanced questions, they make even more mistakes, which causes them to return to the basic level questions. Considering these results, it is now necessary to carry out an in-depth study to reorganize the available questions according to other levels of difficulty, and not just between basic and advanced levels as it is.FCT - Fundação para a Ciência e a Tecnologia(2021-1-PT01-KA220-HED-000023288)This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and UIDB/05757/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/202

An integer programming approach for sensor location in a forest fire monitoring system

Forests worldwide have been devastated by fires. Forest fires cause incalculable damage to fauna and flora. In addition, a forest fire can lead to the death of people and financial damage in general, among other problems. To avoid wildfire catastrophes is fundamental to detect fire ignitions in the early stages, which can be achieved by monitoring ignitions through sensors. This work presents an integer programming approach to decide where to locate such sensors to maximize the coverage provided by them, taking into account different types of sensors, fire hazards, and technological and budget constraints. We tested the proposed approach in a real-world forest with around 7500 locations to be covered and about 1500 potential locations for sensors, showing that it allows obtaining optimal solutions in less than 20 min.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and within project PCIF/GRF/0141/2019 “O3F - An Optimization Framework to reduce Forest Fire” and also the project UIDB/05757/2020 and Forest Alert Monitoring System (SAFe) Project through PROMOVE - Funda¸c˜ao La Caixa. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021, Thadeu Brito was supported by FCT PhD grant SFRH/BD/08598/2020