Дисертації з теми "Genetic Algorithm Heuristic"
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Komínek, Jan. "Heuristické algoritmy pro optimalizaci." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2012. http://www.nusl.cz/ntk/nusl-230306.
Повний текст джерелаBilal, Mohd. "A Heuristic Search Algorithm for Asteroid Tour Missions." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71361.
Повний текст джерелаMa, Jiya. "A Genetic Algorithm for Solar Boat." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3488.
Повний текст джерелаLianjie, Shen. "Optimization and Search in Model-Based Automotive SW/HW Development." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105394.
Повний текст джерелаHan, Limin. "An investigation of a genetic algorithm based hyper-heuristic applied to scheduling problems." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422736.
Повний текст джерелаCheng, Lin. "A genetic algorithm for the vehicle routing problem with time windows /." Electronic version (PDF), 2005. http://dl.uncw.edu/etd/2005/chengl/lincheng.pdf.
Повний текст джерелаWoodside-Oriakhi, Maria. "Portfolio optimisation with transaction cost." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5839.
Повний текст джерелаHanek, Petr. "Implementace problému směrování vozidel pomocí algoritmu mravenčích kolonií a částicových rojů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400931.
Повний текст джерелаDemirbas, Korkut. "Optimal Management Of Coastal Aquifers Using Heuristic Algorithms." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613135/index.pdf.
Повний текст джерелаAlternating Constraints Method&rdquo
is introduced to improve the solution for the cases with variable location. The results show that both proposed method and the regular solution with GA or SA prove to be successful methods for the optimal management of coastal aquifers.
Hassan, Fadratul Hafinaz. "Heuristic search methods and cellular automata modelling for layout design." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7581.
Повний текст джерелаŠebek, Petr. "Heuristiky v optimalizačních úlohách třídy RCPSP." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234904.
Повний текст джерелаUzer, Cevdet Can. "Shape Optimization Of An Excavator Boom By Using Genetic Algorithm." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609575/index.pdf.
Повний текст джерелаBurdová, Jana. "Heuristické a metaheuristické metody řešení úlohy obchodního cestujícího." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75095.
Повний текст джерелаHerrington, Hira B. "A Heuristic Evolutionary Method for the Complementary Cell Suppression Problem." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/28.
Повний текст джерелаHorký, Aleš. "Systém pro pokročilé plánování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234893.
Повний текст джерелаOlsson, Jonas. "Solving a highly constrained multi-level container loading problem from practice." Thesis, Linköpings universitet, Optimeringslära, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-134430.
Повний текст джерелаHillblom, Jonathan. "Evaluating Different Genetic Algorithms for a State-machine Combining Assignment Problem." Thesis, Karlstads universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-79019.
Повний текст джерелаDeep packet inspection (DPI) ̈ar användbart som ett verktyg f ̈or att analysera internettrafik. Regular expressions (regexps) kan användas för att detektera trafik mönster somDPI:n kan identifiera. De här regexps kan representeras som state-machines, och ibland så kan kombinationen av mindre state-machines till större state-machines resultera i mer effektiv bearbetning. Den här tesen undersöker hur man kan bestämma vilka state-machines som används iDPI-klassen bör bli kombinerade på ett effektivt sätt med genetiska algoritmer. Målet är att skapa så fǻ resulterande state-machines från kombineringen på ett sådant sätt att storleken på alla resulterande state-machines håller sig under en övre gräns. Problemet är modellerat som ett assignment problem för vilket ett emulerat surrogatproblem används för att tillåta experiment att utföras. Ett flertal genetiska algoritmer är föreslagna i ett försök att undersöka en bred räckvidd av parametrar. Det är också undersökt om olika genetiska algoritmer har olika prestanda beroende på om state-machines representerar DPI-klasser använda för UDP eller TCP trafik. En 2-dimensionell representation som fångar det underliggande problemet på ett bras sätt är använd. Olika tillvägagångssätt för att representera fitness är undersökta och är upptäckta att ha olika effektivitet i olika situationer. Ett flertal genetiska algoritm operatorer är föreslagna för olika situationer och en skillnad är hittad mellan vad som fungerar för UDP och vad som fungerar för TCP.
Burnett, Linda Dee. "Heuristic Optimization of Boolean Functions and Substitution Boxes for Cryptography." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16023/1/Linda_Burnett_Thesis.pdf.
Повний текст джерелаBurnett, Linda Dee. "Heuristic Optimization of Boolean Functions and Substitution Boxes for Cryptography." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16023/.
Повний текст джерелаAllard, David M. "A Multi-Objective Genetic Algorithm to Solve Single Machine Scheduling Problems Using a Fuzzy Fitness Function." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1180968613.
Повний текст джерелаShakya, Siddhartha. "DEUM : a framework for an estimation of distribution algorithm based on Markov random fields." Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/39.
Повний текст джерелаKingry, Nathaniel. "Heuristic Optimization and Sensing Techniques for Mission Planning of Solar-Powered Unmanned Ground Vehicles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523874767812408.
Повний текст джерелаDoungsa-ard, Chartchai. "Generation of Software Test Data from the Design Specification Using Heuristic Techniques. Exploring the UML State Machine Diagrams and GA Based Heuristic Techniques in the Automated Generation of Software Test Data and Test Code." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5380.
Повний текст джерелаAyo, Babatope S. "Data-driven flight path rerouting during adverse weather: Design and development of a passenger-centric model and framework for alternative flight path generation using nature inspired techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17387.
Повний текст джерелаŠkrabal, Ondřej. "Genetické algoritmy a rozvrhování." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-229180.
Повний текст джерелаNevrlý, Vlastimír. "Modely a metody pro svozové úlohy." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2016. http://www.nusl.cz/ntk/nusl-242880.
Повний текст джерелаWagner, Stefan. "Looking inside genetic algorithms /." Linz : Trauner, 2005. http://aleph.unisg.ch/hsgscan/hm00116856.pdf.
Повний текст джерелаŠvadlenka, Jiří. "Informační systém pro školy s automatickou tvorbou rozvrhů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235924.
Повний текст джерелаAmeli, Mostafa. "Heuristic Methods for Calculating Dynamic Traffic Assignment Simulation-based dynamic traffic assignment: meta-heuristic solution methods with parallel computing Non-unicity of day-to-day multimodal user equilibrium: the network design history effect Improving traffic network performance with road banning strategy: a simulation approach comparing user equilibrium and system optimum." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSET009.
Повний текст джерелаTransport systems are dynamically characterized not only by nonlinear interactions between the different components but also by feedback loops between the state of the network and the decisions of users. In particular, network congestion affects both the distribution of local demand by modifying route choices and overall multimodal demand. Depending on the conditions of the network, they may decide to change for example their transportation mode. Several equilibria can be defined for transportation systems. The user equilibrium corresponds to the situation where each user is allowed to behave selfishly and to minimize his own travel costs. The system optimum corresponds to a situation where the total transport cost of all the users is minimum. In this context, the study aims to calculate route flow patterns in a network considering different equilibrium conditions and study the network equilibrium in a dynamic setting. The study focuses on traffic models capable of representing large-scale urban traffic dynamics. Three main issues are addressed. First, fast heuristic and meta-heuristic methods are developed to determine equilibria with different types of traffic patterns. Secondly, the existence and uniqueness of user equilibria is studied. When there is no uniqueness, the relationship between multiple equilibria is examined. Moreover, the impact of network history is analyzed. Thirdly, a new approach is developed to analyze the network equilibrium as a function of the level of demand. This approach compares user and system optimums and aims to design control strategies in order to move the user equilibrium situation towards the system optimum
Svensson, Philip. "Designing bus route networks with algorithms." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276961.
Повний текст джерелаMålet med denna studie är att använda verklig resedata och efterfrågan och implementera en algoritm som designar busslinjenät med avseende på passagerar -och operatörsintressen. Därefter svara på frågorna: Hur bra presterar algoritmen när den tillämpas på Södertälje, Sverige? Kan den föreslagna algoritmen bidra i designfasen av ett verkligt busslinjenät? Heuristik och den multiobjektiva genetiska algoritmen NSGA-II (Non-dominated Sorting Genetic Algorithm II) användes. Tre olika problem ställdes upp. Det framkom att den långa beräkningstiden är ett stort hinder, över 80 timmar för ett busslinjenät med 58 stationer och 18 busslinjer. Den begränsande faktorn var den långa körtiden, bättre lösningar hade kunnat hittas om programmet fått fortsätta köra. Endast ett mindre nätverk, 24 stationer med fyra busslinjer, baserades på verkliga busslinjer och kunde jämföras. Det resulterade i lösningar som var bättre än de verkliga busslinjerna inom ramen för modellen. Det kan dock inte betraktas som bättre än det verkliga nätverket i mån av att ersätta det, då endast ett subsystem modellerades. Det anses att den föreslagna algoritmen kan vara av assistans för trafikplanerare genom att föreslå länkar mellan busstationer eller hela busslinjer, däremot inte ersätta den nuvarande processen av att designa bussnätverk.
Lü, Haili, and 吕海利. "A comparative study of assembly job shop scheduling using simulation, heuristics and meta-heuristics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47029018.
Повний текст джерелаMansouri, Abdelkhalek. "Generic heuristics on GPU to superpixel segmentation and application to optical flow estimation." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA012.
Повний текст джерелаFinding clusters in point clouds and matching graphs to graphs are recurrent tasks in computer science domain, data analysis, image processing, that are most often modeled as NP-hard optimization problems. With the development and accessibility of cheap multiprocessors, acceleration of the heuristic procedures for these tasks becomes possible and necessary. We propose parallel implantation on GPU (graphics processing unit) system for some generic algorithms applied here to image superpixel segmentation and image optical flow problem. The aim is to provide generic algorithms based on standard decentralized data structures to be easy to improve and customized on many optimization problems and parallel platforms.The proposed parallel algorithm implementations include classical k-means algorithm and application of minimum spanning forest computation for super-pixel segmentation. They include also a parallel local search procedure, and a population-based memetic algorithm applied to optical flow estimation based on superpixel matching. While data operations fully exploit GPU, the memetic algorithm operates like a coalition of processes executed in parallel on the multi-core CPU and requesting GPU resources. Images are point clouds in 3D Euclidean space (space-gray value domain), and are also graphs to which are assigned processor grids. GPU kernels execute parallel transformations under CPU control whose limited role only consists in stopping criteria evaluation or sequencing transformations.The presented contribution contains two main parts. Firstly, we present tools for superpixel segmentation. A parallel implementation of the k-means algorithm is presented with application to 3D data. It is based on a cellular grid subdivision of 3D space that allows closest point findings in constant optimal time for bounded distributions. We present an application of the parallel Boruvka minimum spanning tree algorithm to compute watershed minimum spanning forest. Secondly, based on the generated superpixels and segmentation, we derive parallel optimization procedures for optical flow estimation with edge aware filtering. The method includes construction and improvement heuristics, as winner-take-all and parallel local search, and their embedding into a population-based metaheuristic framework. The algorithms are presented and evaluated in comparison to state-of-the-art algorithms
Ayechew, Mossie A. "Genetic algorithms and Lagrangean based heuristics for vehicle routing." Thesis, Coventry University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324142.
Повний текст джерелаCarvalho, Marcia Braga de. "Aplicações de meta-heuristica genetica e fuzzy no sistema de colonia de formigas para o problema do caixeiro viajante." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261876.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-08T23:52:00Z (GMT). No. of bitstreams: 1 Carvalho_MarciaBragade_M.pdf: 2154346 bytes, checksum: caafd847980349294a73d2ad38d6414c (MD5) Previous issue date: 2007
Resumo: Dentre as várias técnicas heurísticas e exatas existentes para a resolução de problemas combinatórios, os algoritmos populacionais de otimização por colônia de formigas e genéticos têm se destacado devido à sua boa performance. Em especial os algoritmos de colônia de formigas são considerados atualmente como uma das técnicas mais bem sucedidas para a resolução de vários problemas combinatórios, dentre eles o problema do caixeiro viajante. Neste trabalho é apresentado um algoritmo híbrido que trabalha com as meta-heurísticas de sistema de colônia de formigas e genético conjuntamente aplicados no problema do caixeiro viajante simétrico. Além disso, apresentamos uma proposta para o algoritmo de formigas quando temos incertezas associadas aos parâmetros do problema. Os resultados obtidos com as metodologias propostas apresentam resultados satisfatórios para todas as instâncias utilizadas
Abstract: Amongst the several existing heuristical and accurate techniques for the resolution of combinatorial problems, the population algorithms ant colony optimization and genetic have been detached due to their good performance. In special the ant colony algorithms are considered currently as one of the techniques most succeeded for the resolution of some combinatorial problems, amongst them the travelling salesman problem. In this work is presented a hybrid algorithm which works with the ant colony system and genetic metaheuristics jointly applied in the symmetric travelling salesman problem. Moreover, we presented a proposal for the ant algorithm when we have uncertainties associated to problem parameters. The results gotten with the methodology proposals present resulted satisfactory for all the used instances
Mestrado
Automação
Mestre em Engenharia Elétrica
Onder, Ilter. "A Genetic Algorithm For Tsp With Backhauls Based On Conventional Heuristics." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608726/index.pdf.
Повний текст джерелаLee, Yin Nam. "Sequential and parallel solutions of the convoy movement problem using branch-and-bound and heuristic hybrid techniques." Thesis, University of East Anglia, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296564.
Повний текст джерелаParker, Gary B. "Genetic algorithms for the development of real-time multi-heuristic search strategies." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23911.
Повний текст джерелаUzinski, Henrique [UNESP]. "Otimização de problemas multimodais usando meta-heurísticas evolutivas." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/115780.
Повний текст джерелаNeste trabalho é proposta a resolução de problemas multimodais usando duas diferentes meta-heurísticas: Algoritmo Genético de Chu-Beasley modificado e o Algoritmo Genético de Chaves Aleatórias Viciadas (BRKGA), com foco principal nos resultados obtidos por esta última. É feita especificamente a implementação das meta-heurísticas e comparação dos resultados obtidos por estas diferentes técnicas. Uma característica muito importante do BRKGA é a estruturação que permite separar o algoritmo em duas parcelas claramente diferenciadas, uma parcela que depende exclusivamente das características do BRKGA e, portanto, independente do problema que se pretende resolver e outra parcela que depende exclusivamente das características especificas do problema que pretendemos resolver. Essa característica geral do BRKGA permite que ele seja facilmente aplicado a uma grande variedade de problemas, já que a primeira parcela pode ser integralmente aproveitada na resolução de um novo problema. Por outro lado, o Algoritmo Genético de Chu-Beasley (AGCB) é caracterizado pela substituição de um único indivíduo no ciclo geracional e pelo controle máximo de diversidade, mas isto não é suficiente para resolução de problemas complexos e multimodais, sendo assim, é apresentado o AGCB modificado, onde o critério de diversidade é estendido, a população inicial e o descendente gerado no ciclo geracional passa por uma melhoria local. Essas características tornam-o competitivo justificando a comparação com o BRKGA
In this work it is proposed the resolution of multimodal problems using two different meta- heuristics: Chu-Beasley’s Genetic Algorithm and Biased Random Key Genetic Algorithm (BRKGA), focusing mainly on the results obtained by the latter. Specifically the imple- mentation and comparison of results obtained by these different techniques is made. There are several metaheuristics, each with its own specific characteristics which have advan- tages and disadvantages for the resolution of certain problems and in several ways in the implementation and results. A very important feature of the BRKGA is the structure that allows to separate the algorithm into two clearly different parts, one part that depends exclusively on the characteristics of BRKGA and therefore independent of the problem to be solved and another part that depends exclusively on the specific characteristics of the problem we intend to solve. This general feature of the BRKGA allows it to be readily applied to a variety of problems, because the first component part can be fully utilized to solve a new problem. On the other hand, Chu-Beasley’s Genetic Algorithm (AGCB) is characterized by the replacement of a single individual in the generation cycle and by maximum control of diversity, but this is not enough to solve complex and multimodal problems, therefore it is presented the modified AGCB, where the diversity criterion is extended, the initial population and the descendant generated in the generational cycle passes through a local improvement. These features make it competitive, justifying the comparison with BRKGA
Uzinski, Henrique. "Otimização de problemas multimodais usando meta-heurísticas evolutivas /." Ilha Solteira, 2014. http://hdl.handle.net/11449/115780.
Повний текст джерелаBanca: Marina Lavorato de Oliveira
Banca: Marcelo Escobar de Oliveira
Resumo: Neste trabalho é proposta a resolução de problemas multimodais usando duas diferentes meta-heurísticas: Algoritmo Genético de Chu-Beasley modificado e o Algoritmo Genético de Chaves Aleatórias Viciadas (BRKGA), com foco principal nos resultados obtidos por esta última. É feita especificamente a implementação das meta-heurísticas e comparação dos resultados obtidos por estas diferentes técnicas. Uma característica muito importante do BRKGA é a estruturação que permite separar o algoritmo em duas parcelas claramente diferenciadas, uma parcela que depende exclusivamente das características do BRKGA e, portanto, independente do problema que se pretende resolver e outra parcela que depende exclusivamente das características especificas do problema que pretendemos resolver. Essa característica geral do BRKGA permite que ele seja facilmente aplicado a uma grande variedade de problemas, já que a primeira parcela pode ser integralmente aproveitada na resolução de um novo problema. Por outro lado, o Algoritmo Genético de Chu-Beasley (AGCB) é caracterizado pela substituição de um único indivíduo no ciclo geracional e pelo controle máximo de diversidade, mas isto não é suficiente para resolução de problemas complexos e multimodais, sendo assim, é apresentado o AGCB modificado, onde o critério de diversidade é estendido, a população inicial e o descendente gerado no ciclo geracional passa por uma melhoria local. Essas características tornam-o competitivo justificando a comparação com o BRKGA
Abstract: In this work it is proposed the resolution of multimodal problems using two different meta- heuristics: Chu-Beasley's Genetic Algorithm and Biased Random Key Genetic Algorithm (BRKGA), focusing mainly on the results obtained by the latter. Specifically the imple- mentation and comparison of results obtained by these different techniques is made. There are several metaheuristics, each with its own specific characteristics which have advan- tages and disadvantages for the resolution of certain problems and in several ways in the implementation and results. A very important feature of the BRKGA is the structure that allows to separate the algorithm into two clearly different parts, one part that depends exclusively on the characteristics of BRKGA and therefore independent of the problem to be solved and another part that depends exclusively on the specific characteristics of the problem we intend to solve. This general feature of the BRKGA allows it to be readily applied to a variety of problems, because the first component part can be fully utilized to solve a new problem. On the other hand, Chu-Beasley's Genetic Algorithm (AGCB) is characterized by the replacement of a single individual in the generation cycle and by maximum control of diversity, but this is not enough to solve complex and multimodal problems, therefore it is presented the modified AGCB, where the diversity criterion is extended, the initial population and the descendant generated in the generational cycle passes through a local improvement. These features make it competitive, justifying the comparison with BRKGA
Mestre
Šandera, Čeněk. "Heuristické algoritmy pro optimalizaci." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-228326.
Повний текст джерелаOzdemir, Ersin. "Evolutionary methods for the design of digital electronic circuits and systems." Thesis, Cardiff University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326874.
Повний текст джерелаMcGarvey, William. "Evaluating Heuristics and Crowding on Center Selection in K-Means Genetic Algorithms." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/31.
Повний текст джерелаHail, Nourredine. "Méthodes algorithmiques pour les lignes de production avec des machines parallèles." Université Joseph Fourier (Grenoble), 1995. http://www.theses.fr/1995GRE10019.
Повний текст джерелаPonterosso, Pasquale. "Novel techniques of heuristically seeding genetic algorithms for engineering analysis and optimisation." Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302440.
Повний текст джерелаKovàcs, Akos. "Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3306.
Повний текст джерелаThomas, Gina M. "Weibull parameter estimation using genetic algorithms and a heuristic approach to cut-set analysis." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1178901727.
Повний текст джерелаSkidmore, Gerald. "Metaheuristics and combinatorial optimization problems /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/2319.
Повний текст джерелаViana, Monique Simplicio. "Algoritmo genético com operador de transgenia para minimização de makespan da programação reativa da produção." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/9087.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
In recent years, several studies have been carried out to minimize the production time (makespan) in a production schedule of a scenario that represents a manufacturing system. The problem of production scheduling is classified as a combinatorial problem belongs to the NP-hard class of computational problems. Furthermore, in a real world production system, there are many unexpected events (eg, review of production, entry of new products, breaking machines, etc.). To deal with the interruptions of the initial programming, we need to change any settings, which is called reactive production schedule or, simply, reactive scheduling. As a problem of combinatorial features, meta-heuristics is widely used in its resolution. This paper proposes a method that uses an evolutionary meta-heuristic Genetic Algorithm in conjunction with an operator called “Transgenics”, which allows to manipulate the genetic material of individuals adding features which are believed to be important, with the proposal to direct some population of individuals to a more favorable solution to the problem without removing the diversity of the population with a lower cost of time. The objective of this study is to use the Genetic Algorithm with transgenics operator obtain a reactive programming acceptable response time to minimize the makespan value. The objective of this study is to use the Genetic Algorithm with transgenics Operator obtain a reactive programming acceptable response time to minimize the makespan value. Experimental results show the proposed algorithm is able to bring better results than the makespan algorithm and compared in a shorter processing time due to the search direction which provides transgenic operator.
Nos últimos anos, várias pesquisas vêm sendo realizadas a fim de minimizar o tempo total de produção (makespan) em uma programação da produção de algum cenário que representa um sistema de manufatura. O problema da programação da produção é classificado como sendo um problema combinatório pertencente à classe NP-Hard dos problemas computacionais. Além disso, em um sistema de produção real, há muitos eventos inesperados (por exemplo, a revisão da produção, chegada de novos produtos, quebra máquinas, etc.). Para lidar com as interrupções da programação inicial, é preciso realizar outra programação, a qual é denominada de programação reativa da produção. Sendo um problema de recursos combinatórios, é amplamente utilizado metaheurísticas em sua resolução. Neste trabalho é proposto um método que faz uso de uma metaheurística evolutiva Algoritmo Genético em conjunto com um operador intitulado Operador de Transgenia, no qual possibilita manipular o material genético dos indivíduos acrescentando características das quais se acredita serem importantes, com a proposta de direcionar alguns indivíduos da população para uma solução mais favorável para o problema sem tirar a diversidade da população com um custo menor de tempo. O Objetivo deste trabalho é utilizando o Algoritmo Genético com Operador de Transgenia obter uma programação reativa em tempo de resposta aceitável, visando minimizar o valor de makespan. Resultados experimentais mostraram que algoritmo proposto foi capaz de trazer resultados de makespan melhores que os algoritmos comparados e em um menor tempo de processamento, devido ao direcionamento na busca que operador de transgenia proporciona.
Norman, Susan K. "HEURISTIC APPROACHES TO BATCHING JOBS IN PRINTED CIRCUIT BOARD ASSEMBLY." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin997881019.
Повний текст джерелаRemde, Stephen M., Peter I. Cowling, Keshav P. Dahal, and N. J. Colledge. "Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling." Springer-Verlag, 2007. http://hdl.handle.net/10454/2510.
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