Yazar "Şahman, Mehmet Akif" seçeneğine göre listele

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    The binary differential search algorithm approach for solving uncapacitated facility location problems
    (American Scientific Publishers, 2017) Şahman, Mehmet Akif; Altun, Adem Alpaslan; Dündar, Abdullah Oktay
    Recently, many Computational-Intelligence algorithms have been proposed for solving continuous problem. The Differential Search Algorithm (DSA), a computational-intelligence based algorithm inspired by the migration movements of superorganisms, is developed to solve continuous problems. However, DSA proposed for solving problems with continuous search space proposed for solving should be modified for solving binary structured problems. When the DSA is intended for use in binary problems, continuous variables need to be converted into binary format due to solution space structure of this type of problem. In this study, the DSA is modified to solve binary optimization problems by using a conversion approach from continuous values to binary values. The new algorithm has been designated as the binary DSA or BDSA for short. First, when finding donors with the BDSA, four search methods (Bijective, Surjective, Elitist1 and Elitist2) with different iteration numbers are used and tested on 15 UFLP benchmark problems. The Elitist2 approach, which provides the best solution of the four methods, is used in the BDSA, and the results are compared with Continuous Particle Swarm Optimization (CPSO), Continuous Artificial Bee Colony (ABCbin, Improved Binary Particle Swarm Optimization (IBPSO), Binary Artificial Bee Colony (binABC) and Discrete Artificial Bee Colony (DisABC) algorithms using UFLP benchmark problems. Results from the tests and comparisons show that the BDSA is fast, effective and robust for binary optimization. © 2017 American Scientific Publishers.
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    Karma yemlerin genetik algoritmayla maliyet optimizasyonu
    (Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2008-08-29) Şahman, Mehmet Akif; Çunkaş, Mehmet
    Hayvanların sağlıklı ve verimli olması için dengeli ve yeterli beslenmeleri gerekmektedir. Hayvanların tüketim kapasitesi de göz önüne alınarak, besin ihtiyaçlarını karşılayacak bileşimde karma yemler hazırlanmalıdır. Karma yemlerin en uygun fiyatla hazırlanması, yani maliyet optimizasyonu gerek üretici ve gerekse tüketici açısından son derece önemlidir. Bu çalışmada, hayvanların yetiştirme şeklini, türünü, yaşını, ihtiyaçlarını ve yem maliyetlerini dikkate alarak yem karışımını genetik algoritma ile optimize eden bir program hazırlanmıştır. Böylece kaynaklar verimli bir şekilde kullanılarak hayvanların ihtiyaçlarını karşılayacak, uygun üretim yapılması için gerekli yem karışımı sağlanacaktır. Yazılım olarak, nesne yönelimli görsel delphi7 programlama dilinde iki ayrı program hazırlanmıştır. Geliştirilen ilk program kanatlı hayvanlar için, ikincisi ise değişik türde hayvanlar için karma yem hazırlamak amacıyla kullanılabilmektedir. Programlarda öncelikle hayvanın ihtiyaçları belirlenmekte, sonra karışıma girecek yemler tespit edilmekte, daha sonra ise genetik parametreler ayarlanarak optimizasyon gerçekleştirilmektedir. Yapılan bu çalışmada karma yemin maliyet optimizasyonu için Genetik Algoritmalar ilk defa kullanılmış ve sonuçların kabul edilebilir düzeyde olduğu görülmüştür.
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    A new MILP model proposal in feed formulation and using a hybrid-linear binary PSO (H-LBP) approach for alternative solutions
    (SPRINGER, 2018) Şahman, Mehmet Akif; Altun, Adem Alpaslan; Dündar, Abdullah Oktay
    Large-scale feed factories may have multiple production and storage facilities. Any production facility uses its own available raw materials while performing feed formulation. However, ensuring a reasonable cost is achieved, and the desired quality criteria are met, may require obtaining a certain amount of raw material from other facilities. Selecting a specific amount of raw materials among many raw materials in different facilities requires many combinations to be tried out. Providing solutions, especially when there is a large amount of the raw material, may be costly and take more time. A new mixed-integer linear programming (MILP) model that specifies the type of material and the amount of the material to be selected from external facilities has been proposed in this study. When deterministic methods like MILP are used, only one solution result is obtained. However, when the decision-maker would like to see alternative results, solution constraints can be mitigated and a solution provided within the same or similar time. A new method named hybrid-linear binary PSO (H-LBP) has been proposed in this study for the problems that the decision-maker had limited time for and for which the solution results were required in a shorter time. Continuous particle swarm optimization, which works as a hybrid with linear programming, has been used in this method. The new model proposed in this study was tested on the mixed feeds for sheep, cattle and rabbit species by using both MILP and the proposed H-LBP methods. Raw materials determined by the model were added to the mixture, and the cost in each of the three species was observed to go down. In addition, different alternative solutions at reasonable cost and similar quality were presented to the producer/decision-maker in a shorter time.