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.

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.

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.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

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.

Leis de potência em sistemas naturais e artificiais

A Lei de Potência é uma particularidade de um sistema não linear, revelando um sistema complexo próximo da auto-organização. Algumas características de sistemas naturais e artificiais, tais como dimensão populacional das cidades, valor dos rendimentos pessoais, frequência de ocorrência de palavras em textos e magnitude de sismos, seguem distribuições do tipo Lei de Potência. Estas distribuições indicam que pequenas ocorrências são muito comuns e grandes ocorrências são raras, podendo porém verificar-se com razoável probabilidade. A finalidade deste trabalho visa a identificação de fenómenos associados às Leis de Potência. Mostra-se o comportamento típico destes fenómenos, com os dados retirados dos vários casos de estudo e com a ajuda de uma meta-análise. As Leis de Potência em sistemas naturais e artificiais apresentam uma proximidade a um padrão, quando os valores são normalizados (frequências relativas) para dar origem a um meta-gráfico.

After Bologna: does the shift in structure bring forth a shift in a attitude?

With this case-study, we (i) intend to show how a semester project on creating a Multimedia CV could, to some extent, help Portuguese final-year students develop some generic competences, change their attitude towards the challenge of "How to Apply fro a Job" and increase their self-marketing strategies, creativity and entrepreneurship cannot answer the question of the paper, but intend onlu to raise it fot further and better studies now that Bologna design is implementes in almost all HEIs Europe.

Can ERASMUS mobility really help crossing borders? the in and out of a case-study

As lectures, but above all, as Erasmus....

Gold electrode modified by self-assembled monolayers of thiols to determine DNA sequences hybridization

The process of immobilization of biological molecules is one of the most important steps in the construction of a biosensor. In the case of DNA, the way it exposes its bases can result in electrochemical signals to acceptable levels. The use of self-assembled monolayer that allows a connection to the gold thiol group and DNA binding to an aldehydic ligand resulted in the possibility of determining DNA hybridization. Immobilized single strand of DNA (ssDNA) from calf thymus pre-formed from alkanethiol film was formed by incubating a solution of 2-aminoethanothiol (Cys) followed by glutaraldehyde (Glu). Cyclic voltammetry (CV) was used to characterize the self-assembled monolayer on the gold electrode and, also, to study the immobilization of ssDNA probe and hybridization with the complementary sequence (target ssDNA). The ssDNA probe presents a well-defined oxidation peak at +0.158 V. When the hybridization occurs, this peak disappears which confirms the efficacy of the annealing and the DNA double helix performing without the presence of electroactive indicators. The use of SAM resulted in a stable immobilization of the ssDNA probe, enabling the hybridization detection without labels. This study represents a promising approach for molecular biosensor with sensible and reproducible results.

Self-mated tribological systems based on multilayer micro/nanocrystalline CVD diamond coatings

The tribological response of multilayer micro/nanocrystalline diamond coatings grown by the hot filament CVD technique is investigated. These multigrade systems were tailored to comprise a starting microcrystalline diamond (MCD) layer with high adhesion to a silicon nitride (Si3N4) ceramic substrate, and a top nanocrystalline diamond (NCD) layer with reduced surface roughness. Tribological tests were carried out with a reciprocating sliding configuration without lubrication. Such composite coatings exhibit a superior critical load before delamination (130–200 N), when compared to the mono- (60–100 N) and bilayer coatings (110 N), considering ∼10 µm thick films. Regarding the friction behaviour, a short-lived initial high friction coefficient was followed by low friction regimes (friction coefficients between 0.02 and 0.09) as a result of the polished surfaces tailored by the tribological solicitation. Very mild to mild wear regimes (wear coefficient values between 4.1×10−8 and 7.7×10−7 mm3 N−1 m−1) governed the wear performance of the self-mated multilayer coatings when subjected to high-load short-term tests (60–200 N; 2 h; 86 m) and medium-load endurance tests (60 N; 16 h; 691 m).

Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.

Self-adaptive combination of global tabu search and local search for nonlinear equations

Solving systems of nonlinear equations is a very important task since the problems emerge mostly through the mathematical modelling of real problems that arise naturally in many branches of engineering and in the physical sciences. The problem can be naturally reformulated as a global optimization problem. In this paper, we show that a self-adaptive combination of a metaheuristic with a classical local search method is able to converge to some difficult problems that are not solved by Newton-type methods.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). 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

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module 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. Finally, some conclusions are reached and future work outlined.