Rámec pro plánování problémy

Import 22/07/2015Scheduling problems form an important subclass of combinatorial optimisation problems with many applications in manufacturing and logistics. Predominately these problems are NP-complete (decision based) and NP-hard (optimisation based), hence the main course of research in solving them concentrates on the design of efficient heuristic algorithms. Two main categories of these algorithms exist: deterministic algorithms and evolutionary metaheuristics. The deterministic algorithms comprise local improvement techniques, such as k-opt algorithm, which try to improve existing feasible solution, and constructive heuristics, such as NEH, which build a solution starting from scratch, adding one job at a time. Evolutionary metaheuristics have prospered in the past decades, owing to their efficiency and flexibility. Drawing inspiration from the theory of natural evolution or swarm behavioural patterns, the most popular of these algorithms in practice include for instance Genetic Algorithms, Differential Evolution, Particle Swarm Optimisation, amongst others. However, even though these heuristics provide in most cases close to optimal solution at reasonable execution time, this time is still impractically long for many applications. Therefore much effort has been dedicated to accelerating these algorithms. Since the development of hardware turns away from increasing the clock speed towards the parallel processing units, owing to reaching the limits of technology due to the increased power consumption and heat dissipation, this effort goes into parallelisation of the existing algorithms, to enable exploitation of the computing power of multi-core or many-core platforms. This is the goal of the first part of the thesis, accelerating two of the deterministic algorithms, NEH and 2-opt, with interesting results. Another approach has been taken in the second part, with the core premise of exploring the influence of stochasticity on the performance of an evolutionary algorithm, selecting the relatively recent and promising Discrete Artificial Bee Colony algorithm. The pseudo-random number generator has been replaced with the different types of dissipative chaos maps, with some of them improving the algorithm significantly. It has been shown that the population based evolutionary algorithms often form complex networks, taken from the point of view of the information exchange between individual solutions during the course of population development. The final part of this thesis puts this observation into practice by embedding the complex network analysis based self-adaptive mechanism into the ABC algorithm, a continuous optimisation problems solving evolutionary algorithm, which is however the basis for the afore mentioned DABC algorithm, and proving the effectiveness for some of the developed versions, currently on the standard continuous optimisation test functions, with the possibility to extend this modification to the combinatorial optimisations problems in the future being discussed in the conclusion.Rozvrhovací problémy jsou důležitou podtřídou úloh kombinatorické optimalizace s řadou aplikací ve výrobě a logistice. Většina těchto problémů je NP-úplných (rozhodovací forma) a NP-těžkých (optimalizační forma), proto se výzkum zaměřuje na návrh efektivních heuristických algoritmů. Dvě hlavní kategorie těchto algoritmů jsou deterministické algoritmy a evoluční metaheuristiky. Deterministické algoritmy zahrnují techniky lokálního prohledávání, například algoritmus k-opt, jejichž cílem je zlepšení existujícího přípustného řešení problému, dále pak konstruktivní heuristiky, jejichž příkladem je algoritmus NEH, které hledané řešení vytvářejí inkrementálně, bez potřeby znalosti vstupního bodu v prohledávaném prostoru řešení. Evoluční metaheuristiky mají za sebou historii úspěšného vývoje v posledních desetiletích, zejména díky jejich efektivitě a flexibilitě. Jejich inspirací jsou poznatky převzaté z biologie, teorie evoluce a inteligence hejna. Mezi nejpopulárnějšími z těchto algoritmů jsou, mimo jiné, genetické algoritmy, diferenciální evoluce, rojení částic (Particle Swarm Optimisation). Ačkoli tyto heuristiky nalézají ve většině případů řešení blížící se globálnímu optimu v přípustném výpočetním čase, pro řadu aplikací mohou být stále ještě nepřijatelně pomalé. Velké úsilí bylo věnováno zrychlení těchto algoritmů. Protože se vývoj hardware díky dosažení technologických limitů, vzhledem ke zvyšující se spotřebě energie a tepelnému vyzařování, obrací od zvyšování frekvence jednojádrového procesoru k vícejádrovým procesorům a paralelnímu zpracování, je tato snaha většinou orientovaná na paralelizaci existujících algoritmů, aby bylo umožněno využití výpočetní síly vícejádrových platforem (multi-core a many-core). Prvním cílem této práce je tudíž akcelerace dvou deterministických algoritmů, NEH a 2-opt, přičemž bylo dosaženo zajímavých výsledků. Jiný přístup byl zvolen ve druhé části, s hlavní myšlenkou prozkoumání vlivu náhodnosti na výkon evolučního algoritmu. Za tímto účelem byl zvolen relativně nový a slibný algoritmus Discrete Artificial Bee Colony. Generátor pseudonáhodných čísel byl nahrazen několika různými chaotickými mapami, z nichž některé znatelně zlepšily výsledky algoritmu. Bylo ukázáno, že evoluční algoritmy založené na populaci často formují komplexní sítě, vzato z pohledu výměny informací mezi jednotlivými řešeními v populaci během jejího vývoje. Závěrečná část práce aplikuje toto pozorování vložením samo přizpůsobivého mechanismu založeném na analýze komplexní sítě do algoritmu ABC, který je evolučním algoritmem pro spojitou optimalizaci a zároveň základem dříve zmíněného DABC algoritmu. Efektivita několika verzí algoritmu založeném na této myšlence je dokázána na standardní sadě testovacích funkcí pro spojitou optimalizaci. Možnost rozšíření této modifikace na kombinatorické optimalizační problémy je diskutována v závěru práce.460 - Katedra informatikyvýborn

Estado del arte de las aplicaciónes del concepto de Lot Streaming a la secuenciación en talleres de flujo

[ES] El presente estudio, versa en una revisión de los articulaos publicados sobre la aplicación del concepto de Lot Streaming y su aplicación en la secuenciación de talleres de flujo. Los documentos se clasificaron de acuerdo a los dimensionamientos que se asocian a evidenciar las combinaciones y comparaciones de diversos algoritmos utilizados para mejorar los talleres de flujo a través de la división de Sub-lotes. Lo cual representa un particular enfoque para la revisión de artículos e investigadores que han abordado esta temática, aplicando una metodología con un enfoque cualitativo, por cuanto la información se sistematizó a través del software Atlas_ti en correspondencia a las variables exploradas en cada estudio que identifican la eficacia de la división de lotes en la solución de problemas de talleres de flujo. La mayoría de los estudios identificaron algoritmos genéticos y genéticos híbridos, mostrando algunas desventajas frente a los tradicionales y en pocos estudios evidenciando la eficacia en su aplicación.[EN] The present study is based on a review of the articles published on the application of the Lot Streaming concept and its application in the sequencing of flow workshops. The documents were classified according to the sizing that is associated to evidence the combinations and comparisons of different algorithms used to improve the workshops of flow through the division of sublots. This represents a particular approach to the review of articles and researchers that have addressed this issue, applying a methodology with a qualitative approach, as the information is systematized through the Atlas_ti software in correspondence to the variables explored in each study that identify the efficiency of the division of batches in the solution of problems of flow workshops. The majority of the studies identified hybrid genetic and genetic algorithms, showing some disadvantages compared to the traditional ones and in few studies showing the efficacy in their application.Velecela Rojas, SJ. (2018). Estado del arte de las aplicaciónes del concepto de Lot Streaming a la secuenciación en talleres de flujo. http://hdl.handle.net/10251/110033TFG

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Scheduling the flow shop with blocking problem with the chaos-induced Discrete Self Organising Migrating Algorithm

The dissipative Lozi chaotic map is embedded in the Discrete Self Organising Migrating Algorithm (DSOMA) algorithm, as a pseudorandom number generator (PRNG). This novel chaotic based algorithm is applied to the flow shop with blocking scheduling problem. The algorithm is tested on the Tail lard problem sets and compared favourably with published heuristics

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

How To Touch a Running System

The increasing importance of distributed and decentralized software architectures entails more and more attention for adaptive software. Obtaining adaptiveness, however, is a difficult task as the software design needs to foresee and cope with a variety of situations. Using reconfiguration of components facilitates this task, as the adaptivity is conducted on an architecture level instead of directly in the code. This results in a separation of concerns; the appropriate reconfiguration can be devised on a coarse level, while the implementation of the components can remain largely unaware of reconfiguration scenarios. We study reconfiguration in component frameworks based on formal theory. We first discuss programming with components, exemplified with the development of the cmc model checker. This highly efficient model checker is made of C++ components and serves as an example for component-based software development practice in general, and also provides insights into the principles of adaptivity. However, the component model focuses on high performance and is not geared towards using the structuring principle of components for controlled reconfiguration. We thus complement this highly optimized model by a message passing-based component model which takes reconfigurability to be its central principle. Supporting reconfiguration in a framework is about alleviating the programmer from caring about the peculiarities as much as possible. We utilize the formal description of the component model to provide an algorithm for reconfiguration that retains as much flexibility as possible, while avoiding most problems that arise due to concurrency. This algorithm is embedded in a general four-stage adaptivity model inspired by physical control loops. The reconfiguration is devised to work with stateful components, retaining their data and unprocessed messages. Reconfiguration plans, which are provided with a formal semantics, form the input of the reconfiguration algorithm. We show that the algorithm achieves perceived atomicity of the reconfiguration process for an important class of plans, i.e., the whole process of reconfiguration is perceived as one atomic step, while minimizing the use of blocking of components. We illustrate the applicability of our approach to reconfiguration by providing several examples like fault-tolerance and automated resource control

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration