Dynamic Scheduling for Maintenance Tasks Allocation supported by Genetic Algorithms

Since the first factories were created, man has always tried to maximize its production and, consequently, his profits. However, the market demands have changed and nowadays is not so easy to get the maximum yield of it. The production lines are becoming more flexible and dynamic and the amount of information going through the factory is growing more and more. This leads to a scenario where errors in the production scheduling may occur often. Several approaches have been used over the time to plan and schedule the shop-floor’s production. However, some of them do not consider some factors present in real environments, such as the fact that the machines are not available all the time and need maintenance sometimes. This increases the complexity of the system and makes it harder to allocate the tasks competently. So, more dynamic approaches should be used to explore the large search spaces more efficiently. In this work is proposed an architecture and respective implementation to get a schedule including both production and maintenance tasks, which are often ignored on the related works. It considers the maintenance shifts available. The proposed architecture was implemented using genetic algorithms, which already proved to be good solving combinatorial problems such as the Job-Shop Scheduling problem. The architecture considers the precedence order between the tasks of a same product and the maintenance shifts available on the factory. The architecture was tested on a simulated environment to check the algorithm behavior. However, it was used a real data set of production tasks and working stations

An Adaptive Modular Redundancy Technique to Self-regulate Availability, Area, and Energy Consumption in Mission-critical Applications

As reconfigurable devices\u27 capacities and the complexity of applications that use them increase, the need for self-reliance of deployed systems becomes increasingly prominent. A Sustainable Modular Adaptive Redundancy Technique (SMART) composed of a dual-layered organic system is proposed, analyzed, implemented, and experimentally evaluated. SMART relies upon a variety of self-regulating properties to control availability, energy consumption, and area used, in dynamically-changing environments that require high degree of adaptation. The hardware layer is implemented on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) to provide self-repair using a novel approach called a Reconfigurable Adaptive Redundancy System (RARS). The software layer supervises the organic activities within the FPGA and extends the self-healing capabilities through application-independent, intrinsic, evolutionary repair techniques to leverage the benefits of dynamic Partial Reconfiguration (PR). A SMART prototype is evaluated using a Sobel edge detection application. This prototype is shown to provide sustainability for stressful occurrences of transient and permanent fault injection procedures while still reducing energy consumption and area requirements. An Organic Genetic Algorithm (OGA) technique is shown capable of consistently repairing hard faults while maintaining correct edge detector outputs, by exploiting spatial redundancy in the reconfigurable hardware. A Monte Carlo driven Continuous Markov Time Chains (CTMC) simulation is conducted to compare SMART\u27s availability to industry-standard Triple Modular Technique (TMR) techniques. Based on nine use cases, parameterized with realistic fault and repair rates acquired from publically available sources, the results indicate that availability is significantly enhanced by the adoption of fast repair techniques targeting aging-related hard-faults. Under harsh environments, SMART is shown to improve system availability from 36.02% with lengthy repair techniques to 98.84% with fast ones. This value increases to five nines (99.9998%) under relatively more favorable conditions. Lastly, SMART is compared to twenty eight standard TMR benchmarks that are generated by the widely-accepted BL-TMR tools. Results show that in seven out of nine use cases, SMART is the recommended technique, with power savings ranging from 22% to 29%, and area savings ranging from 17% to 24%, while still maintaining the same level of availability

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

Организация интеллектуальных пространств на основе платформы Smart-M3 с использованием устройств на базе операционной системы DD-WRT

Smart space is a service orientated infrastructure for knowledge sharing between devices. This paper describes a smart space creation process based on integration of Smart-M3 platform with DD-WRT-based device. Smart-M3 is an open source platform which implements the concept of smart space. Wi-Fi router was chosen as a DD-WRT-based device, which allows using it for smart space organization and provides wireless connection between devices at the same time. This method simplifies deployment of scenarios with several participants. The paper describes a process of compilation and installation of Smart-M3 platform to DD-WRT operating system. Testing of the main Smart-M3 operations showed that a smart space, organized in this way, can be used for considered scenarios. The authors have developed “Smart-M3 Control Panel” web-service which allows users to control the Smart-M3 platform by a graphical web interface. User of “Smart-M3 Control Panel” can view a current status of the platform; launch, stop or reload the platform; view information storage; download log files and change startup options. SocketIO protocol was used for user interaction with a web service.Интеллектуальное пространство представляет собой сервис-ориентированную инфраструктуру для возможности обеспечения общего доступа к информации различными устройствами. Статья описывает процесс организации интеллектуальных пространств путем интеграции платформы Smart-M3 и устройства функционирующего на базе операционной системы DD-WRT. Smart-M3 представляет собой платформу с открытыми исходными кодами, реализующую концепцию интеллектуального пространства. В качестве устройства для интеграции был выбран Wi-Fi маршрутизатор, что позволяет использовать его одновременно и для организации интеллектуального пространства, и для обеспечения беспроводной связи между устройствами. Использование Wi-Fi маршрутизатора упрощает развертывание сценариев с небольшим количеством участников. Статья подробно описывает процесс компиляции и установки платформы Smart-M3 для операционной системы DD-WRT. Компиляция пакетов с исходным кодом платформы происходит на маршрутизаторе. Измерение быстродействия стандартных операций платформы Smart-M3 на маршрутизаторе показало, что интеллектуальное пространство организованное таким образом может успешно использоваться для сценариев с небольшим количеством участников. Авторами был разработан веб-сервис «Smart-M3 Control Panel», который позволяет пользователям управлять платформой Smart-M3 посредствам графического веб интерфейса. С использованием этого сервиса пользователь получает возможность просматривать текущий статус платформы; запускать, останавливать или перезапускать платформу; просматривать содержимое информационного хранилища; загружать лог-файлы и менять параметры запуска платформы Smart-M3. Интерактивное взаимодействие с пользователем было реализовано при помощи протокола SocketIO

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Optimal Operation of Power Distribution Feeders with Smart Loads

Distribution systems have been going through significant changes in recent years, moving away from traditional systems with low-level control toward smart grids with high-level control, with improved technologies in communications, monitoring, computation, and real-time control. In the context of smart grids, Demand Response (DR) programs have been introduced so that customers are able to control and alter their energy consumption in consideration with distribution system operators, with benefits accruing to both customers and Local Distribution Companies (LDCs). This thesis focuses on the integration of DR with the intelligent operation of distribution system feeders. Thus, it proposes a mathematical model of an unbalanced three-phase distribution system power flow, including different kinds of loads and other components of distribution systems. In this context, an unbalanced three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which includes the models of lines, transformers, voltage-based loads, smart loads, Load Tap Changers (LTCs), and Switched Capacitors (SCs), together with their respective operating limits, to determine the optimal switching decisions for LTCs, SCs, and control signals for smart loads, in particular, Energy Hub Management System loads and Peaksaver PLUS loads. Hence, Neural-Network-based models of controllable smart loads, which are integrated into the DOPF model are proposed, developed, and tested. Since the DOPF model has different discrete variables such as LTCs and SCs, the model is a Mixed-Integer Non-Linear Programming (MINLP) problem, which presents a considerable computational challenge. In order to solve this MINLP problem without approximations and ad-hoc heuristics, a Genetic Algorithm (GA) is used to determine the optimal control decisions of controllable feeder elements and loads. Since the number of control variables in a realistic distribution system is large, solving the DOPF for real-time applications using GA is computationally expensive. Hence, a decentralized system with parallel computing nodes based on a Smart Grid Communication Middleware (SGCM) system is proposed. Using a "MapReduce" model, the SGCM system executes the DOPF model, communicates between the master and the worker computing nodes, and sends/receives data amongst different parts of the parallel computing system. When large number of nodes are involved, the SGCM system has a fast performance, is reliable, and is able to handle different fault tolerance levels with the available computing resources. The proposed approaches are tested and validated on a practical feeder with the objective of minimizing energy losses and/or energy drawn from the substation. The results demonstrate the feasibility of the developed techniques for real-time distribution feeder control, highlighting the advantages of integration of smart loads in the operation of distribution systems by LDCs

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

L'intertextualité dans les publications scientifiques

La base de données bibliographiques de l'IEEE contient un certain nombre de duplications avérées avec indication des originaux copiés. Ce corpus est utilisé pour tester une méthode d'attribution d'auteur. La combinaison de la distance intertextuelle avec la fenêtre glissante et diverses techniques de classification permet d'identifier ces duplications avec un risque d'erreur très faible. Cette expérience montre également que plusieurs facteurs brouillent l'identité de l'auteur scientifique, notamment des collectifs de chercheurs à géométrie variable et une forte dose d'intertextualité acceptée voire recherchée