Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

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.

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.

Introducing the fractional-order Darwinian PSO

One of the most well-known bio-inspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machinelearning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian particle swarm optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival of the fittest, to enhance the ability to escape from local optima. This paper firstly presents a survey on PSO algorithms mainly focusing on the DPSO. Afterward, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed. The fractional-order optimization algorithm, denoted as FO-DPSO, is tested using several well-known functions, and the relationship between the fractional-order velocity and the convergence of the algorithm is observed. Moreover, experimental results show that the FO-DPSO significantly outperforms the previously presented FO-PSO.

Synthesis of molecular biomimetics

Biomimetics has paved the way toward new materials and technologies inspired in Nature. Biomolecules and their supramolecular organization have today a leading role in biomimetics, benefiting from the recent advances in nanotechnology. The production of biomimetic materials may be however a difficult task, because Nature does it very well. The use of several building blocks assembled in bottom-up arrangement is without doubt at the core of this process. Such building blocks include different molecules or molecular arrangements, of synthetic or natural origin, such as amino acids, lipids, carbohydrates, nucleic acids, carbon allotropes, dendrimers, or organosilanes, among others. The most common approaches to produce synthetic biomimetic materials are reported herein, with special emphasis to building blocks and their supramolecular arrangement.

Projeto, Desenvolvimento e implementação de um Robô Voador de Inspiração Biológica

Este trabalho consiste no projeto, desenvolvimento e implementação de uma máquina voadora de inspiração biológica. Para a sua construção, é elaborado um estudo do voo nos seres biológicos de forma a obter alguma informação para o seu dimensionamento e é elaborada uma análise de projetos que utilizem as mesmas características de voo. Com os dados recolhidos neste estudo, é elaborado o projeto da máquina. Numa última fase, com recurso a materiais leves, é implementada uma máquina capaz de exercer as forças envolvidas no voo, força de propulsão e força de sustentação, e são elaborados estudos a fim de perceber quais os pontos fortes e os pontos fracos da mesma.