Percorrer por autor "Rocha, Ana Maria A.C."
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- Analyzing the mathE platform through clustering algorithmsPublication . Azevedo, Beatriz Flamia; Amoura, Yahia; Rocha, Ana Maria A.C.; Fernandes, Florbela P.; Pacheco, Maria F.; Pereira, Ana I.University lecturers have been encouraged to adopt innovative methodologies and teaching tools in order to implement an interactive and appealing educational environment. The MathE platform was created with the main goal of providing students and teachers with a new perspective on mathematical teaching and learning in a dynamic and appealing way, relying on digital interactive technologies that enable customized study. The MathE platform has been online since 2019, having since been used by many students and professors around the world. However, the necessity for some improvements on the platform has been identified, in order to make it more interactive and able to meet the needs of students in a customized way. Based on previous studies, it is known that one of the urgent needs is the reorganization of the available resources into more than two levels (basic and advanced), as it currently is. Thus, this paper investigates, through the application of two clustering methodologies, the optimal number of levels of difficulty to reorganize the resources in the MathE platform. Hierarchical Clustering and three Bio-inspired Automatic Clustering Algorithms were applied to the database, which is composed of questions answered by the students on the platform. The results of both methodologies point out six as the optimal number of levels of difficulty to group the resources offered by the platform.
- Automatic nurse allocation based on a population algorithm for home health carePublication . Alves, Filipe; Rocha, Ana Maria A.C.; Pereira, Ana I.; Leitão, PauloThe 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.
- Build orientation optimization of car hoodvent with additive manufacturingPublication . Matos, Marina A.; Rocha, Ana Maria A.C.; Costa, Lino; Pereira, Ana I.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.
- Build orientation optimization problem in additive manufacturingPublication . Rocha, Ana Maria A.C.; Pereira, Ana I.; Vaz, A. Ismael F.Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used by engineers and designers. Build orientation is a critical issue in AM since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduced significantly the building costs and will improve the object accuracy. This paper presents an optimization approach to solve the part build orientation problem considering the staircase effect, support area characteristics and the build time. Two global optimization methods, the Electromagnetism-like and the Stretched Simulated Annealing algorithms, are used to study the optimal orientation of four models. Preliminary experiments show that both optimization methods can effectively solve the build orientation problem in AM, finding several global solutions.
- Capacitated waste collection problem solution using an open-source toolPublication . Silva, Adriano S.; Alves, Filipe; Díaz de Tuesta, Jose Luis; Rocha, Ana Maria A.C.; Pereira, Ana I.; Silva, Adrián; Gomes, HelderPopulation in cities is growing worldwide, which puts the systems that offer basic services to citizens under pressure. Among these systems, the Municipal Solid Waste Management System (MSWMS) is also affected. Waste collection and transportation is the first task in an MSWMS, carried out traditionally in most cases. This approach leads to inefficient resource and time expense since routes are prescheduled or defined upon drivers’ choices. The waste collection is recognized as an NP-hard problem that can be modeled as a Capacitated Waste Collection Problem (CWCP). Despite the good quality of works currently available in the literature, the execution time of algorithms is often forgotten, and faster algorithms are required to increase the feasibility of the solutions found. In this paper, we show the performance of the open-source Google OR-Tools to solve the CWCP in Bragança, Portugal (inland city). The three metaheuristics available in this tool were able to reduce significantly the cost associated with waste collection in less than 2 s of execution time. The result obtained in this work proves the applicability of the OR-Tools to be explored for waste collection problems considering bigger systems. Furthermore, the fast response can be useful for developing new platforms for dynamic vehicle routing problems that represent scenarios closer to the real one. We anticipate the proven efficacy of OR-Tools to solve CWCP as the starting point of developments toward applying optimization algorithms to solve real and dynamic problems.
- Cluster analysis for breast cancer patterns identificationPublication . Azevedo, Beatriz Flamia; Alves, Filipe; Rocha, Ana Maria A.C.; Pereira, Ana I.Safety in patient decision-making is one of the major health care challenges. Computational support in establishing diagnoses and preventing errors will contribute to an enhancement in doctor-patient communication. This work performs a three-dimensional cluster analysis, using k-means algorithm, to identify patterns in a breast cancer database. The methodology proposed can be useful to identify patterns in the database that are normally difficult to be noted by classical methods, such as statistical methods. The three-dimensional cluster approach was explored combining three variables at once. The k-means algorithm is used to recognize the hidden patterns on the database. Sub-clusters are used to separate the benign and malignant tumors inside the global cluster. The results present effective analyses of three different clusters based on different combinations between variables. Thus, health professionals can obtain a better understanding of the properties of different types of tumor, identifying the mined abstract tumor features, through the cluster data analysis.
- Conceptual multi-agent system design for distributed scheduling systemsPublication . Alves, Filipe; Rocha, Ana Maria A.C.; Pereira, Ana I.; Leitão, PauloWith the progressive increase in the complexity of dynamic environments, systems require an evolutionary configuration and optimization to meet the increased demand. In this sense, any change in the conditions of systems or products may require distributed scheduling and resource allocation of more elementary services. Centralized approaches might fall into bottleneck issues, becoming complex to adapt, especially in case of unexpected events. Thus, Multi-agent systems (MAS) can extract their automatic and autonomous behaviour to enhance the task effort distribution and support the scheduling decision-making. On the other hand, MAS is able to obtain quick solutions, through cooperation and smart control by agents, empowered by their coordination and interoperability. By leveraging an architecture that benefits of a collaboration with distributed artificial intelligence, it is proposed an approach based on a conceptual MAS design that allows distributed and intelligent management to promote technological innovation in basic concepts of society for more sustainable in everyday applications for domains with emerging needs, such as, manufacturing and healthcare scheduling systems.
- Distributed scheduling based on multi-agent systems and optimization methodsPublication . Alves, Filipe; Rocha, Ana Maria A.C.; Pereira, Ana I.; Leitão, PauloThe increasing relevance of complex systems in dynamic environments has received special attention during the last decade from the researchers. Such systems need to satisfy products or clients desires, which, after accomplished might change, becoming a very dynamic situation. Currently, decentralized approaches could assist in the automation of dynamic scheduling, based on the distribution of control functions over a swarm network of decision-making entities. Distributed scheduling, in an automatic manner, can be answered by a service coordination architecture of the different schedule components. However, it is necessary to introduce the control layer in the solution, encapsulating an intelligent service that merge agents with optimization methods. Multi-agent systems (MAS) can be combined with several optimization methods to extract the best of the two worlds: the intelligent control, cooperation and autonomy provided by MAS solutions and the optimum offered by optimization methods. The proposal intends to test the intelligent management of the schedule composition quality, in two case studies namely, manufacturing and home health care.
- Evaluating student behaviour on the mathE platform - clustering algorithms approachesPublication . Azevedo, Beatriz Flamia; Rocha, Ana Maria A.C.; Fernandes, Florbela P.; Pacheco, Maria F.; Pereira, Ana I.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.
- A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustmentPublication . Alves, Filipe; Varela, Maria Leonilde R.; Rocha, Ana Maria A.C.; Pereira, Ana I.; Leitão, PauloManufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurance of unexpected events.