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Emel Kızılkaya Aydoğan
Researcher at Erciyes University
Publications - 48
Citations - 776
Emel Kızılkaya Aydoğan is an academic researcher from Erciyes University. The author has contributed to research in topics: Computer science & Particle swarm optimization. The author has an hindex of 10, co-authored 40 publications receiving 559 citations.
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Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment
TL;DR: In this paper, a conceptual performance measurement framework that takes into account company-level factors is presented for a real world application problem and an integrated approach of analytic hierarchy process improved by rough sets theory and fuzzy TOPSIS method is proposed to obtain final ranking.
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A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing
TL;DR: A new modified particle swarm optimization algorithm with negative knowledge is proposed to solve the mixed-model two-sided assembly line balancing problem and results show that the proposed approach can be acquired distinguished results than the existing solution approaches.
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hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems
TL;DR: The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems.
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Stochastic two-sided U-type assembly line balancing: a genetic algorithm approach
TL;DR: In this paper, a stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed.
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Balancing stochastic U-lines using particle swarm optimization
TL;DR: A novel particle swarm optimization algorithm is proposed to solve the U-line balancing problem with stochastic task times and yields good solutions for all test problems within a short computational time.