Browsing by Author "Madureira, Ana Maria"
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- An Evolutionary Based Algorithm for Resources System Selection Problem in Agile/Virtual EnterprisesPublication . Ávila, Paulo; Pires, António; Madureira, Ana MariaThe problem of resources systems selection takes an important role in Agile/Virtual Enterprises (A/VE) integration. However, the resources systems selection problem is difficult to solve in A/VE because: it can be of exponential complexity resolution; it can be a multi criteria problem; and because there are different types of A/V Es with different requisites that have originated the development of a specific resources selection model for each one of them. In this work we have made some progress in order to identify the principal gaps to be solved. This paper will show one of those gaps in the algorithms area to be applied for its resolution. In attention to that gaps we address the necessity to develop new algorithms and with more information disposal, for its selection by the Broker. In this paper we propose a genetic algorithm to deal with a specific case of resources system selection problem when the space solution dimension is high.
- Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problemPublication . Madureira, Ana Maria; Falcão, Diamantino; Pereira, IvoThe paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.
- Auto-parametrização de meta-heurísticas para escalonamento dinâmicoPublication . Pereira, Ivo; Madureira, Ana MariaEste artigo aborda o problema da parametrização de Técnicas de Optimização Inspiradas na Biologia (BIT - Biological Inspired Optimization Techniques), também conhecidas como Meta-heurísticas, considerando a importância que estas técnicas têm na resolução de situações de mundo real, sujeitas a perturbações externas. É proposto um módulo de aprendizagem com o objectivo de permitir que um Sistema Multi-Agente (SMA) para Escalonamento seleccione automaticamente uma Metaheurística e escolha a parametrização a usar no processo de optimização. Para o módulo de aprendizagem foi usado o Raciocínio baseado em Casos (RBC), permitindo ao sistema aprender a partir da experiência acumulada na resolução de problemas similares. Através da análise dos resultados obtidos é possível concluir acerca das vantagens da sua utilização.
- Autonomic computing for scheduling in manufacturing systemsPublication . Madureira, Ana Maria; Pereira, Ivo; Sousa, Nelson; Ávila, Paulo; Bastos, JoãoWe 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.
- Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling systemPublication . Pereira, Ivo; Madureira, Ana Maria; Moura, Paulo OliveiraA 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 behaviorPublication . Pereira, Ivo; Madureira, Ana MariaIn 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.
- Collective intelligence on dynamic manufacturing scheduling optimizationPublication . Madureira, Ana Maria; Pereira, Ivo; Sousa, NelsonSwarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.
- Computational Intelligence and Decision Making: Trends and ApplicationsPublication . Madureira, Ana Maria; Reis, Cecília; Marques, ViriatoAdvances in Computational Intelligence and Decision Making: Trends and Applications provides an overview and original analysis of new developments and applications in several areas of Computational Intelligence and Information Systems, in general. Computational Intelligence became the roadmap for engineers to develop and analyze novel techniques to solve problems in basic sciences such as physics, chemistry, biology, engineering, environment and social sciences. The material contained in this book addresses the foundations and applications of Artificial Intelligence and Decision Support Systems, Complex and Biological Inspired Systems, Simulation and Evolution of real and artificial life forms, Intelligent Models and Control Systems, Knowledge and Learning Technologies, Web Semantics and Ontologies, Intelligent Tutoring Systems, Intelligent Power Systems, Self-Organized and Distributed Systems, Intelligent Manufacturing Systems and Affective Computing. The contributions are written by international experts, who provide up-to-date aspects of the topics discussed and present recent, original insights of their own experience in these fields. Its aim is the presentation of state-of-the-art technologies in the field of Computational Intelligence as well as the discussion of new research findings in this field. The book is suitable for scientists, engineers, educators and students, as it addresses a large diversity of subjects presented in a broad band of complexity, ranging from simple natural language, found in some state-of-the-art articles, to some more complex mathematical issues, found in control applications, robotics and power systems, such as fractional calculus, fuzzy systems and rough-sets theory. Data Mining techniques, such as Support-Vector Machines and Neural Networks, are presented in application of the Biomedical and Bioinformatics fields, among others. Briefly, we believe that this book provides a good window on most of the subjects that directly or indirectly make use of Computational Intelligence, also showing the contributions that it is already giving, or can be given in a near future, for solving some of the more pressing problems of today’s world, such as energy and environment, society and economy.
- Cooperation mechanism for team-work based multi-agent system in dynamic scheduling through meta-heuristicsPublication . Madureira, Ana Maria; Gomes, Nuno; Santos, Joaquim; Ramos, CarlosThis paper describes a Multi-agent Scheduling System that assumes the existence of several Machines Agents (which are decision-making entities) distributed inside the Manufacturing System that interact and cooperate with other agents in order to obtain optimal or near-optimal global performances. Agents have to manage their internal behaviors and their relationships with other agents via cooperative negotiation in accordance with business policies defined by the user manager. Some Multi Agent Systems (MAS) organizational aspects are considered. An original Cooperation Mechanism for a Team-work based Architecture is proposed to address dynamic scheduling using Meta-Heuristics.
- Cooperative intelligent system for manufacturing schedulingPublication . Madureira, Ana Maria; Santos, Joaquim; Pereira, IvoHybridization of intelligent systems is a promising research field of computational intelligence focusing on combinations of multiple approaches to develop the next generation of intelligent systems. In this paper we will model a Manufacturing System by means of Multi-Agent Systems and Meta-Heuristics technologies, where each agent may represent a processing entity (machine). The objective of the system is to deal with the complex problem of Dynamic Scheduling in Manufacturing Systems.