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.

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.

Exposure to particles in Portuguese swine production

Several studies have shown that human exposures to airbome dust and microorganisms, such as bacteria and fungi, can cause respiratory diseases. Agricultural workers have been found to be at high risk of exposures to airborne particles. From a human health perspective dust exposure in pig farming is the most important risk because of the large number of workers needed in pig production and the increasing number of working hours inside enclosed buildings. In the pig buildings, particulate matters like dust play a role in not only deteriorating indoor air quality but also can cause an adverse health effect on workers. Generally, dust is recognized to adsorb and transport odorous compounds and biological agents. The aim of this study was to determine particles contamination in 7 swine farms located in Lisbon district, Portugal.

Fungi, MVOCs and dust exposure assessment in poultry production

Agricultural workers especially poultry farmers, are at increased risk of occupational respiratory diseases. In poultry production besides fungi microbial volatile organic compounds (MVOCs) are also present due to compounds released during fungal metabolism. Dust is also one of the risk factors present in animal housing and is comprised by poultry residues, fungi and feathers. A study was developed aiming to assess occupational exposure to fungi, MVOCs and dust in seven poultry units located in Portugal.

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.

Cellular hypomethylation is associated with impaired nitric oxide production by cultured human endothelial cells

Hyperhomocysteinemia (HHcy) is a risk factor for vascular disease, but the underlying mechanisms remain incompletely defined. Reduced bioavailability of nitric oxide (NO) is a principal manifestation of underlying endothelial dysfunction, which is an initial event in vascular disease. Inhibition of cellular methylation reactions by S-adenosylhomocysteine (AdoHcy), which accumulates during HHcy, has been suggested to contribute to vascular dysfunction. However, thus far, the effect of intracellular AdoHcy accumulation on NO bioavailability has not yet been fully substantiated by experimental evidence. The present study was carried out to evaluate whether disturbances in cellular methylation status affect NO production by cultured human endothelial cells. Here, we show that a hypomethylating environment, induced by the accumulation of AdoHcy, impairs NO production. Consistent with this finding, we observed decreased eNOS expression and activity, but, by contrast, enhanced NOS3 transcription. Taken together, our data support the existence of regulatory post-transcriptional mechanisms modulated by cellular methylation potential leading to impaired NO production by cultured human endothelial cells. As such, our conclusions may have implications for the HHcy-mediated reductions in NO bioavailability and endothelial dysfunction.

Mental health and psychiatry research in Brazil: scientific production from 1999 to 2003

OBJECTIVE: To assess the extent of mental health scientific production in Brazil from 1999 to 2003, and to identify the nature of the publications generated, their sources of finance and the ways of publicly disseminating the research findings. METHODS: Searches for publications were conducted in the Medline and PsychInfo databases for the period 1999-2003. A semi-structured questionnaire developed by an international team was applied to 626 mental health researchers, covering each interviewee's educational background, research experience, access to funding sources, public impact and research priorities. The sample was composed by 626 mental health researchers identified from 792 publications indexed on Medline and PsychInfo databases for the period above, and from a list of reviewers of Revista Brasileira de Psiquiatria. RESULTS: In Brazil, 792 publications were produced by 525 authors between 1999 and 2003 (441 indexed in Medline and 398 in the ISI database). The main topics were: depression (29.1%), substance misuse (14.6%), psychoses (10%), childhood disorders (7%) and dementia (6.7%). Among the 626 Brazilian mental health researchers, 329 answered the questionnaire. CONCLUSIONS: There were steadily increasing numbers of Brazilian articles on mental health published in foreign journals from 1999 to 2003: the number of articles in Medline tripled and it doubled in the ISI database. The content of these articles corresponded to the priorities within mental health, but there is a need for better interlinking between researchers and mental health policymakers.