Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2021Scopus SJRQ2/r/bayburt/13399
  Hybrid tabu search algorithm for weight optimization of planar steel frames

  Tayfur, Bilal; Yilmaz, Hamid; Daloğlu, Ayşe T.

  In this study, a hybrid metaheuristic algorithm called Hybrid Tabu Search (HTS) is proposed for the weight optimization of planar steel structures. The effectiveness of HTS lies in improving the initial conditions of a tabu search algorithm with a greedy search algorithm and a swap operator used in the phase of section selection. The effectiveness of the proposed method is tested and validated on three different benchmark examples. C♯ software is developed to perform the structural analyses. Numerical results obtained by using HTS showed that the proposed method achieved significantly lighter frame weights in most of the benchmark examples. When the method was examined in terms of computational efficiency, HTS was at a level that can compete with other algorithms examined within this study, although it did not provide a significant advantage.

  AtıfScopus17·WoS15·OpenAlex15

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• MakaleAçık Erişim2021Scopus SJRQ2/r/bayburt/13392
  An I-optimal experimental design-embedded nonlinear lexicographic goal programming model for optimization of controllable design factors

  Ozdemir, Akin

  Optimal experimental designs are effective offline quality improvement techniques used to enhance existing products and develop new products for a constrained design region. For some practical situations, I-optimal experimental designs may be the most suitable way to construct optimal experimental design points, by addressing prediction variance to generate a measure of prediction performance over the non-standard experimental design region. The purpose of this article is three-fold: (1) the I-optimality criterion is selected for minimizing the average prediction variance; (2) fitted mean and variance response models are found; and (3) a nonlinear lexicographic goal programming model incorporating an I-optimal experimental design is proposed to find the optimum settings of controllable design factors. Comparison studies and a numerical example are provided to illustrate the effectiveness of the proposed methodology for improvement of the quality of products.

  AtıfScopus15·WoS15·OpenAlex16

  İncele
• MakaleAçık Erişim2020Scopus SJRQ2/r/bayburt/13265
  A mathematical model and tabu search algorithm for multi-manned assembly line balancing problems with assignment restrictions

  Yilmaz, Hamid; Yilmaz, Mustafa

  A multi-manned assembly line is a set of workstations where task operations can be performed on multiple sides of the line. Such assembly lines are important for large products, such as buses, cars and trucks. In this study, a mathematical model for multi-manned assembly lines with assignment restrictions is proposed. The purpose of the mathematical model is to minimize the total number of workers and open multi-manned workstations along the line for a given cycle time simultaneously under various assignment restrictions. The model provides a more realistic situation for multi-manned assembly line problems. The tabu search algorithm (TSA) is also used to solve the problem. The performances of both methods on well-known data-set problems are analysed. Based on the computational experiments, the performance of the proposed TSA is compared with the mathematical model solutions on various problem instances. The experimental results verify the effectiveness and efficiency of the proposed TSA.

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  AtıfScopus33·WoS28·OpenAlex33

  İncele
• MakaleAçık Erişim2019Scopus SJRQ1/r/bayburt/13192
  Response surface optimization for a nonlinearly constrained irregular experimental design space

  Ozdemir, Akin; Cho, Byung Rae

  Finding optimum conditions for process factors in an engineering optimization problem with response surface functions requires structured data collection using experimental design. When the experimental design space is constrained owing to external factors, its design space may form an asymmetrical and irregular shape and thus standard experimental design methods become ineffective. Computer-generated optimal designs, such as D-optimal designs, provide alternatives. While several iterative exchange algorithms for D-optimal designs are available for a linearly constrained irregular design space, it has not been clearly understood how D-optimal design points need to be generated when the design space is nonlinearly constrained and how response surface models are optimized. This article proposes an algorithm for generating the D-optimal design points that satisfy both feasibility and optimality conditions by using piecewise linear functions on the design space. The D-optimality-based response surface design models are proposed and optimization procedures are then analysed.

  AtıfScopus18·WoS13·OpenAlex16

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