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.

Symbolic dynamics and renormalization of non-autonomous k periodic dynamical systems

The purpose of this paper was to introduce the symbolic formalism based on kneading theory, which allows us to study the renormalization of non-autonomous periodic dynamical systems.

Family and non-family businesses on internal factors of entrepreneurship

Small firms are a major player in development. Thus, entrepreneurship is frequently attached to these rms and it must be present in daily management of factors such as planning and cooperation. We intend to analyze these factors, comparing familiar and non-familiar businesses. This study was conducted in a Portuguese region in the north of Portugal - Vale do Sousa . The results allow us to conclude that even with some managerial di erences it was not possible to identify distinct patterns between them. The main goal of this paper is to open research lines on important issues to distinguish familiar from non-familiar businesses.

Entrepreneurship Strategies in Family and Non-family Businesses

The goal of the present paper is to analyse the classic entrepreneurship strategies (Innovation, Risk and Proactivity) in small and medium-sized businesses. However as presented in the title, the study will go further by comparing the results of those strategies in familiar and nonfamiliar businesses. This study was carried on in construction and industry sectors, in the region of Vale do Sousa, in the north of Portugal. In order to classify businesses as familiar or non-familiar types two criterion were adopted: (1) Management Control, (2) Family Employability. On the opposite to some studies that present a larger percentage of familiar businesses in national and European entrepreneurial fabric, the criterion used leaded to a larger number of non-familiar businesses (53%). The results showed that in general SMEs in this region are not following entrepreneurship strategies. Analysing the entire sample without a separation of businesses by nature (familiar/non-familiar) only proactivity showed to be more present in the managerial decisions. There is a lack of innovation and risk culture. Comparing the groups only on proactivity tests was possible to verify some differences. It was concluded that non-familiar businesses are more proactive than familiar ones. Between those groups there are no statistical differences on the means of the variables innovation and risk. At the same time some tests were conducted to test the differences on the variable entrepreneurship. The results were similar to innovation and risk strategies: There are no significant differences on entrepreneurship between these groups of businesses.

Show me your body and I tell you how hold you are: a non-invasive methodto define 6 life history-classes in Risso's dolphins (Grampus griseus), using an identified trial population in the Atlantic

European Cetacean Society Conference Workshop, Galway, Ireland, 25th March 2012.

Non-coding RNAs: multi-tasking molecules in the cell

In the last years it has become increasingly clear that the mammalian transcriptome is highly complex and includes a large number of small non-coding RNAs (sncRNAs) and long noncoding RNAs (lncRNAs). Here we review the biogenesis pathways of the three classes of sncRNAs, namely short interfering RNAs (siRNAs), microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs). These ncRNAs have been extensively studied and are involved in pathways leading to specific gene silencing and the protection of genomes against virus and transposons, for example. Also, lncRNAs have emerged as pivotal molecules for the transcriptional and post-transcriptional regulation of gene expression which is supported by their tissue-specific expression patterns, subcellular distribution, and developmental regulation. Therefore, we also focus our attention on their role in differentiation and development. SncRNAs and lncRNAs play critical roles in defining DNA methylation patterns, as well as chromatin remodeling thus having a substantial effect in epigenetics. The identification of some overlaps in their biogenesis pathways and functional roles raises the hypothesis that these molecules play concerted functions in vivo, creating complex regulatory networks where cooperation with regulatory proteins is necessary. We also highlighted the implications of biogenesis and gene expression deregulation of sncRNAs and lncRNAs in human diseases like cancer.

The use of non-standard CT conversion ramps for Monte Carlo verification of 6 MV prostate IMRT plans

Monte Carlo (MC) dose calculation algorithms have been widely used to verify the accuracy of intensity-modulated radiotherapy (IMRT) dose distributions computed by conventional algorithms due to the ability to precisely account for the effects of tissue inhomogeneities and multileaf collimator characteristics. Both algorithms present, however, a particular difference in terms of dose calculation and report. Whereas dose from conventional methods is traditionally computed and reported as the water-equivalent dose (Dw), MC dose algorithms calculate and report dose to medium (Dm). In order to compare consistently both methods, the conversion of MC Dm into Dw is therefore necessary. This study aims to assess the effect of applying the conversion of MC-based Dm distributions to Dw for prostate IMRT plans generated for 6 MV photon beams. MC phantoms were created from the patient CT images using three different ramps to convert CT numbers into material and mass density: a conventional four material ramp (CTCREATE) and two simplified CT conversion ramps: (1) air and water with variable densities and (2) air and water with unit density. MC simulations were performed using the BEAMnrc code for the treatment head simulation and the DOSXYZnrc code for the patient dose calculation. The conversion of Dm to Dw by scaling with the stopping power ratios of water to medium was also performed in a post-MC calculation process. The comparison of MC dose distributions calculated in conventional and simplified (water with variable densities) phantoms showed that the effect of material composition on dose-volume histograms (DVH) was less than 1% for soft tissue and about 2.5% near and inside bone structures. The effect of material density on DVH was less than 1% for all tissues through the comparison of MC distributions performed in the two simplified phantoms considering water. Additionally, MC dose distributions were compared with the predictions from an Eclipse treatment planning system (TPS), which employed a pencil beam convolution (PBC) algorithm with Modified Batho Power Law heterogeneity correction. Eclipse PBC and MC calculations (conventional and simplified phantoms) agreed well (<1%) for soft tissues. For femoral heads, differences up to 3% were observed between the DVH for Eclipse PBC and MC calculated in conventional phantoms. The use of the CT conversion ramp of water with variable densities for MC simulations showed no dose discrepancies (0.5%) with the PBC algorithm. Moreover, converting Dm to Dw using mass stopping power ratios resulted in a significant shift (up to 6%) in the DVH for the femoral heads compared to the Eclipse PBC one. Our results show that, for prostate IMRT plans delivered with 6 MV photon beams, no conversion of MC dose from medium to water using stopping power ratio is needed. In contrast, MC dose calculations using water with variable density may be a simple way to solve the problem found using the dose conversion method based on the stopping power ratio.

Análise de risco em sequenciamento de actividades com restrições de recursos

Mestrado em Engenharia Electrotécnica e de Computadores. Área de Especialização em Sistemas e Planeamento Industrial.