Self-optimization for dynamic scheduling in manufacturing systems

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. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

A hybrid intelligent system for distributed dynamic scheduling

This chapter addresses the resolution of dynamic scheduling by means of meta-heuristic and multi-agent systems. Scheduling is an important aspect of automation in manufacturing systems. Several contributions have been proposed, but the problem is far from being solved satisfactorily, especially if scheduling concerns real world applications. The proposed multi-agent scheduling system assumes the existence of several resource agents (which are decision-making entities based on meta-heuristics) distributed inside the manufacturing system that interact with other agents in order to obtain optimal or near-optimal global performances.

A study on dynamic state information (DSI) around users for safe urban life

To select each node by devices and by contexts in urban computing, users have to put their plan information and their requests into a computing environment (ex. PDA, Smart Devices, Laptops, etc.) in advance and they will try to keep the optimized states between users and the computing environment. However, because of bad contexts, users may get the wrong decision, so, one of the users’ demands may be requesting the good server which has higher security. To take this issue, we define the structure of Dynamic State Information (DSI) which takes a process about security including the relevant factors in sending/receiving contexts, which select the best during user movement with server quality and security states from DSI. Finally, whenever some information changes, users and devices get the notices including security factors, then an automatic reaction can be possible; therefore all users can safely use all devices in urban computing.

MASDScheGATS: a prototype system for dynamic scheduling

A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics and Multi-Agent Systems to the resolution of this class of real world scheduling problems seems really promising. This paper presents a prototype for MASDScheGATS (Multi-Agent System for Distributed Manufacturing Scheduling with Genetic Algorithms and Tabu Search).

Multi-agent electricity market simulation with dynamic strategies & virtual power producers

Distributed energy resources will provide a significant amount of the electricity generation and will be a normal profitable business. In the new decentralized grid, customers will be among the many decentralized players and may even help to co-produce the required energy services such as demand-side management and load shedding. So, they will gain the opportunity to be more active market players. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. In this paper we propose the improvement of MASCEM, a multi-agent simulation tool to study negotiations in electricity spot markets based on different market mechanisms and behavior strategies, in order to take account of decentralized players such as VPP.

Construção de websites com ferramentas open-source: duas experiências de implementação em bibliotecas de ensino superior

As Tecnologias da Informação e da Comunicação (TIC) têm influenciado de forma inequívoca o desenvolvimento das bibliotecas à escala global. Nas últimas décadas, as TIC mudaram a dinâmica das bibliotecas permitindo a sua modernização (pelo desenvolvimento da eficiência das tarefas já realizadas), favorecendo a inovação (pela utilização das tecnologias como base para o desenvolvimento de novos serviços/técnicas) e promovendo a sua transformação (ao nível do paradigma funcional, da disponibilização de conteúdos, etc.) – criando, em suma, uma nova relação com os seus públicos

Dual source/dual energy-renal applications

Development of Dual Source Computed Tomography (Definition, Siemens Medical Solutions, Erlanger, Germany) allowed advances in temporal resolution, with the addition of a second X-ray source and an array of detectors to the TCM 64 slices. The ability to run exams on Dual Energy, allows greater differentiation of tissues, showing differences between closer attenuation coefficients. In terms of renal applications, the distinction of kidney stones and masses become one of the main advantages of the use of dual-energy technology. This article pretends to demonstrate operating principles of this equipment, as its main renal applications.

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.

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.

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.

Dynamic power- and failure-aware cloud resources allocation for Sets of independent tasks

Cloud computing is increasingly being adopted in different scenarios, like social networking, business applications, scientific experiments, etc. Relying in virtualization technology, the construction of these computing environments targets improvements in the infrastructure, such as power-efficiency and fulfillment of users’ SLA specifications. The methodology usually applied is packing all the virtual machines on the proper physical servers. However, failure occurrences in these networked computing systems can induce substantial negative impact on system performance, deviating the system from ours initial objectives. In this work, we propose adapted algorithms to dynamically map virtual machines to physical hosts, in order to improve cloud infrastructure power-efficiency, with low impact on users’ required performance. Our decision making algorithms leverage proactive fault-tolerance techniques to deal with systems failures, allied with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that our algorithms perform better targeting power-efficiency and SLA fulfillment, in face of cloud infrastructure failures.

Gestão e monitorização de uma rede departamental: um caso de estudo

Mestrado em Engenharia Electrotécnica e de Computadores

Effect of oxidative stress upon absorption of glucose by the human placenta: in vitro studies with BeWo cells

Pregnancy is a dynamic state and the placenta is a temporary organ that, among other important functions, plays a crucial role in the transport of nutrients and metabolites between the mother and the fetus, which is essential for a successful pregnancy. Among these nutrients, glucose is considered a primary source of energy and, therefore, fundamental to insure proper fetus development. Several studies have shown that glucose uptake is dependent on several morphological and biochemical placental conditions. Oxidative stress results from the unbalance between reactive oxygen species (ROS) and antioxidants, in favor of the first. During pregnancy, ROS, and therefore oxidative stress, increase, due to increased tissue oxygenation. Moreover, the relation between ROS and some pathological conditions during pregnancy has been well established. For these reasons, it becomes essential to understand if oxidative stress can compromise the uptake of glucose by the placenta. To make this study possible, a trophoblastic cell line, the BeWo cell line, was used. Experiments regarding glucose uptake, either under normal or oxidative stress conditions, were conducted using tert-butylhydroperoxide (tBOOH) as an oxidative stress inducer, and 3H-2-deoxy-D-glucose (3H-DG) as a glucose analogue. Afterwards, studies regarding the involvement of glucose facilitative transporters (GLUT) and the phosphatidylinositol 3-kinases (PI3K) and protein kinase C (PKC) pathways were conducted, also under normal and oxidative stress conditions. A few antioxidants, endogenous and from diet, were also tested in order to study their possible reversible effect of the oxidative effect of tBOOH upon apical 3H-DG uptake. Finally, transepithelial studies gave interesting insights regarding the apical-to-basolateral transport of 3H-DG. Results showed that 3H-DG uptake, in BeWo cells, is roughly 50% GLUT-mediated and that tBOOH (100 μM; 24h) decreases apical 3H-DG uptake in BeWo cells by about 33%, by reducing both GLUT- (by 28%) and non-GLUT-mediated (by 40%) 3H-DG uptake. Uptake of 3H-DG and the effect of tBOOH upon 3H-DG uptake are not dependent on PKC and PI3K. Moreover, the effect of tBOOH is not associated with a reduction in GLUT1 mRNA levels. Resveratrol, quercetin and epigallocatechin-3-gallate, at 50 μM, reversed, by at least 45%, the effect of tBOOH upon 3H-DG uptake. Transwell studies show that the apical-to-basolateral transepithelial transport of 3H-DG is increased by tBOOH.In conclusion, our results show that tBOOH caused a marked decrease in both GLUT and non-GLUT-mediated apical uptake of 3H-DG by BeWo cells. Given the association of increased oxidative stress levels with several important pregnancy pathologies, and the important role of glucose for fetal development, the results of this study appear very interesting.