Implementação ferramenta LEAN – TPM - serrote mecânico alternativo

Presentemente, com a economia cada vez mais globalizada e com a grande competitividade do mercado, as empresas de produção procuram cada vez mais ajustar-se às exigências dos clientes. Por esse motivo, o controlo do fluxo produtivo torna-se imprescindível para a resolução de problemas e para a própria melhoria contínua do processo. O sistema “Lean Manufacturing”, é um conjunto de atividades que tem como meta o aumento da capacidade de resposta às mudanças e à minimização dos desperdícios na produção, constituindo-se num verdadeiro empreendimento de gestão inovadora. O TPM – Total Productive Maintenance, é uma ferramenta de melhoria continua cada vez mais utilizada nas empresas com o objetivo de melhorar a eficiência dos seus equipamentos e atingir metas para a redução de desperdícios, incluindo a restauração e manutenção de condições padrão de funcionamento. O presente trabalho visa a implementação da ferramenta TPM num equipamento (Serrote Mecânico Alternativo) instalado no Laboratório das Oficinas Mecânicas do Instituto Superior de Engenharia do Porto. No contexto prático, este trabalho consistiu numa primeira fase por implementar a ferramenta 5S´s no posto de trabalho do equipamento em estudo. Durante esta implementação foi possível detetar algumas anomalias no equipamento, tendo sido sujeitas a uma análise para encontrar as suas causas raiz. Posteriormente foi implementada a ferramenta TPM, de modo, a criar melhores condições de acesso e simplificação das atividades de inspeção, lubrificação e limpeza. Além disso, foi executado e proposto algumas oportunidades de melhoria em alguns elementos, de forma a reduzir tempos de operação e tempos de setup, contribuindo para o aumento da eficiência do equipamento.

O professor e a sua prática - Estratégias Pedagógicas

As estratégias pedagógicas fazem parte das preocupações do quotidiano do professor e são também uma preocupação pessoal que trouxe para reflexão no presente relatório, partindo da experiência pedagógica (estágio) desenvolvida nos 1º, 2º e 3º Ciclos do Ensino Básico. Os Estágios Pedagógicos desenvolveram-se em duas escolas. No 1º Ciclo [Educação Artística], realizou-se na Escola EB 1 do Cedro, em Vila Nova de Gaia. No 2º Ciclo [Educação Visual e Educação Tecnológica] e no 3º Ciclo [Educação Visual] realizou-se na Escola EB 2,3 de Vilar de Andorinho, também no concelho de Vila Nova de Gaia. Este estudo incidiu, sobretudo, sobre as Prática Educativa Supervisionada II e III, onde são descritas vivências, identificadas estratégias pedagógicas, nomeadamente, estratégias de ensino e aprendizagem e estratégias de comportamentos e de atuação na sala de aula. Neste sentido, procura-se conhecer, analisar e compreender a importância da adequação das estratégias de ensino e aprendizagem, comportamentos e atuações nas salas de aulas de Educação Visual e Educação Tecnológica, procurando sempre desenvolver competências profissionais e pessoais, bem como atitudes proactivas na identificação e resolução de problemas pedagógicos em práticas futuras.

Classification of some penalty methods

Optimization problems arise in science, engineering, economy, etc. and we need to find the best solutions for each reality. The methods used to solve these problems depend on several factors, including the amount and type of accessible information, the available algorithms for solving them, and, obviously, the intrinsic characteristics of the problem. There are many kinds of optimization problems and, consequently, many kinds of methods to solve them. When the involved functions are nonlinear and their derivatives are not known or are very difficult to calculate, these methods are more rare. These kinds of functions are frequently called black box functions. To solve such problems without constraints (unconstrained optimization), we can use direct search methods. These methods do not require any derivatives or approximations of them. But when the problem has constraints (nonlinear programming problems) and, additionally, the constraint functions are black box functions, it is much more difficult to find the most appropriate method. Penalty methods can then be used. They transform the original problem into a sequence of other problems, derived from the initial, all without constraints. Then this sequence of problems (without constraints) can be solved using the methods available for unconstrained optimization. In this chapter, we present a classification of some of the existing penalty methods and describe some of their assumptions and limitations. These methods allow the solving of optimization problems with continuous, discrete, and mixing constraints, without requiring continuity, differentiability, or convexity. Thus, penalty methods can be used as the first step in the resolution of constrained problems, by means of methods that typically are used by unconstrained problems. We also discuss a new class of penalty methods for nonlinear optimization, which adjust the penalty parameter dynamically.

Complex dynamics in the trajectory control of redundant manipulators

Redundant manipulators allow the trajectory optimization, the obstacle avoidance, and the resolution of singularities. For this type of manipulators, the kinematic control algorithms adopt generalized inverse matrices that may lead to unpredictable responses. Motivated by these problems this paper studies the complexity revealed by the trajectory planning scheme when controlling redundant manipulators. The results reveal fundamental properties of the chaotic phenomena and give a deeper insight towards the development of superior trajectory control algorithms.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

MASDSheGATS – Scheduling System for Dynamic Manufacturing Environments

This chapter addresses the resolution of scheduling in manufacturing systems subject to perturbations. The planning of Manufacturing Systems involves frequently the resolution of a huge amount and variety of combinatorial optimisation problems with an important impact on the performance of manufacturing organisations. Examples of those problems are the sequencing and scheduling problems in manufacturing management, routing and transportation, layout design and timetabling problems.

MASDScheGATS: a prototype system for dynamic scheduling

A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics and Multi-Agent Systems to the resolution of this class of real world scheduling problems seems really promising. This paper presents a prototype for MASDScheGATS (Multi-Agent System for Distributed Manufacturing Scheduling with Genetic Algorithms and Tabu Search).

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Autonomic computing for scheduling in manufacturing systems

We describe a novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Autonomic Computing has emerged as paradigm aiming at embedding applications with a management structure similar to a central nervous system. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. In this paper we envisage the use of Multi-Agent Systems paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with Autonomic properties, in order to reduce the complexity of managing systems and human interference. Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems.

Sistema de correlação de eventos e notificações - SCEN

Mestrado em Engenharia Electrotécnica e de Computadores