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.

Negotiation mechanism for self-organized scheduling system

This paper presents a negotiation mechanism for Dynamic Scheduling based on Swarm Intelligence (SI). Under the new negotiation mechanism, agents must compete to obtain a global schedule. SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviors of insects and other animals. This work is concerned with negotiation, the process through which multiple selfinterested agents can reach agreement over the exchange of operations on competitive resources.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization.

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm 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.

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.

Self-optimization aspects for dynamic scheduling

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. At this scenario, self-optimizing arise as the ability of the agent to monitor its state and performance and proactively tune itself to respond to environmental stimuli.

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.

Cooperative intelligent system for manufacturing scheduling

Hybridization 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.

Self-managing agents for dynamic scheduling in manufacturing

The main purpose of this paper is to propose a Multi-Agent Autonomic and Bio-Inspired based framework with selfmanaging capabilities to solve complex scheduling problems using cooperative negotiation. Scheduling resolution requires the intervention of highly skilled human problem-solvers. 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 (AC) evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference.

Scheduling in holonic manufacturing systems

This chapter presents some of the issues with holonic manufacturing systems. It starts by presenting the current manufacturing scenario and trends and then provides some background information on the holonic concept and its application to manufacturing. The current limitations and future trends of manufacturing suggest more autonomous and distributed organisations for manufacturing systems; holonic manufacturing systems are proposed as a way to achieve such autonomy and decentralisation. After a brief literature survey a specific research work is presented to handle scheduling in holonic manufacturing systems. This work is based on task and resource holons that cooperate with each other based on a variant of the contract net protocol that allow the propagation of constraints between operations in the execution plan. The chapter ends by presenting some challenges and future opportunities of research.

Cooperation mechanism for team-work based multi-agent system in dynamic scheduling through meta-heuristics

This 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.

Sistema de aquisição de dados em tempo real

Neste trabalho propus-me realizar um Sistema de Aquisição de Dados em Tempo Real via Porta Paralela. Para atingir com sucesso este objectivo, foi realizado um levantamento bibliográfico sobre sistemas operativos de tempo real, salientando e exemplificando quais foram marcos mais importantes ao longo da sua evolução. Este levantamento permitiu perceber o porquê da proliferação destes sistemas face aos custos que envolvem, em função da sua aplicação, bem como as dificuldades, científicas e tecnológicas, que os investigadores foram tendo, e que foram ultrapassando com sucesso. Para que Linux se comporte como um sistema de tempo real, é necessário configura-lo e adicionar um patch, como por exemplo o RTAI ou ADEOS. Como existem vários tipos de soluções que permitem aplicar as características inerentes aos sistemas de tempo real ao Linux, foi realizado um estudo, acompanhado de exemplos, sobre o tipo de arquitecturas de kernel mais utilizadas para o fazer. Nos sistemas operativos de tempo real existem determinados serviços, funcionalidades e restrições que os distinguem dos sistemas operativos de uso comum. Tendo em conta o objectivo do trabalho, e apoiado em exemplos, fizemos um pequeno estudo onde descrevemos, entre outros, o funcionamento escalonador, e os conceitos de latência e tempo de resposta. Mostramos que há apenas dois tipos de sistemas de tempo real o ‘hard’ que tem restrições temporais rígidas e o ‘soft’ que engloba as restrições temporais firmes e suaves. As tarefas foram classificadas em função dos tipos de eventos que as despoletam, e evidenciando as suas principais características. O sistema de tempo real eleito para criar o sistema de aquisição de dados via porta paralela foi o RTAI/Linux. Para melhor percebermos o seu comportamento, estudamos os serviços e funções do RTAI. Foi dada especial atenção, aos serviços de comunicação entre tarefas e processos (memória partilhada e FIFOs), aos serviços de escalonamento (tipos de escalonadores e tarefas) e atendimento de interrupções (serviço de rotina de interrupção - ISR). O estudo destes serviços levou às opções tomadas quanto ao método de comunicação entre tarefas e serviços, bem como ao tipo de tarefa a utilizar (esporádica ou periódica). Como neste trabalho, o meio físico de comunicação entre o meio ambiente externo e o hardware utilizado é a porta paralela, também tivemos necessidade de perceber como funciona este interface. Nomeadamente os registos de configuração da porta paralela. Assim, foi possível configura-lo ao nível de hardware (BIOS) e software (módulo do kernel) atendendo aos objectivos do presente trabalho, e optimizando a utilização da porta paralela, nomeadamente, aumentando o número de bits disponíveis para a leitura de dados. No desenvolvimento da tarefa de hard real-time, foram tidas em atenção as várias considerações atrás referenciadas. Foi desenvolvida uma tarefa do tipo esporádica, pois era pretendido, ler dados pela porta paralela apenas quando houvesse necessidade (interrupção), ou seja, quando houvesse dados disponíveis para ler. Desenvolvemos também uma aplicação para permitir visualizar os dados recolhidos via porta paralela. A comunicação entre a tarefa e a aplicação é assegurada através de memória partilhada, pois garantindo a consistência de dados, a comunicação entre processos do Linux e as tarefas de tempo real (RTAI) que correm ao nível do kernel torna-se muito simples. Para puder avaliar o desempenho do sistema desenvolvido, foi criada uma tarefa de soft real-time cujos tempos de resposta foram comparados com os da tarefa de hard real-time. As respostas temporais obtidas através do analisador lógico em conjunto com gráficos elaborados a partir destes dados, mostram e comprovam, os benefícios do sistema de aquisição de dados em tempo real via porta paralela, usando uma tarefa de hard real-time.

Parallel implementation of vertex component analysis for hyperspectral endmember extraction

International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July 2012, Munich, Germany