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CUDA-Based Modified Genetic Algorithms for Solving Fuzzy Flow Shop Scheduling ProblemsHuang, Yi-chen 23 August 2010 (has links)
The flow shop scheduling problems with fuzzy processing times and fuzzy due dates are investigated in this paper. The concepts of earliness and tardiness are interpreted by using the concepts of possibility and necessity measures that were developed in fuzzy sets theory. And the objective function will be taken into account through the different combinations of possibility and necessity measures. The genetic algorithm will be invoked to tackle these objective functions. A new idea based on longest common substring will be introduced at the best-keeping step. This new algorithm reduces the number of generations needed to reach the stopping criterion. Also, we implement the algorithm on CUDA. The numerical experiments show that the performances of the CUDA program on GPU compare favorably to the traditional programs on CPU.
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[en] A SIMHEURISTIC ALGORITHM FOR THE STOCHASTIC PERMUTATION FLOW-SHOP SCHEDULING PROBLEM WITH DELIVERY DATES AND CUMULATIVE PAYOFFS / [pt] UM ALGORITMO DE SIM-HEURISTICA PARA UM PROBLEMA ESTOCÁSTICO DE PERMUTATION FLOW-SHOP SCHEDULING COM DATAS DE ENTREGA E GANHOS CUMULATIVOS19 October 2020 (has links)
[pt] Esta dissertação de mestrado analisa um problema de programação de máquinas
em série com datas de entrega e ganhos cumulativos sob incerteza.
Em particular, este trabalho considera situações reais na quais os tempos
de processamento e datas de liberação são estocásticos. O objetivo principal
deste trabalho é a resolução deste problema de programação de máquinas
em série em um ambiente estocástico buscando analisar a relação entre diferentes
niveis de incerteza e o benefício esperado. Visando atingir este objetivo,
primeiramente uma heurística é proposta utilizando-se da técnica de
biased-randomization para a versão determinística do problema. Então, esta
heurística é extendida para uma metaheurística a partir do encapsulamento
dentro da estrutura de um variable neighborhood descend. Finalmente, a metaheurística é extendida para uma simheurística a partir da incorporação
da simulação de Monte Carlo. De acordo com os experimentos computacionais,
o nível de incerteza tem um impacto direto nas soluções geradas pela
simheurística. Além disso, análise de risco foram desenvolvidas utilizando
as conhecidas métricas de risco: value at risk e conditional value at risk. / [en] This master s thesis analyzes the Permutation Flow-shop Scheduling
Problem with Delivery Dates and Cumulative Payoffs under uncertainty
conditions. In particular, the work considers the realistic situation in which
processing times and release dates are stochastics. The main goal is to
solve this Permutation Flow-shop problem in the stochastic environment
and analyze the relationship between different levels of uncertainty and
the expected payoff. In order to achieve this goal, first a biased-randomized
heuristic is proposed for the deterministic version of the problem. Then, this
heuristic is extended into a metaheuristic by encapsulating it into a variable
neighborhood descent framework. Finally, the metaheuristic is extended
into a simheuristic by incorporating Monte Carlo simulation. According
to the computational experiments, the level of uncertainty has a direct
impact on the solutions provided by the simheuristic. Moreover, a risk
analysis is performed using two well-known metrics: the value at risk and
the conditional value at risk.
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