Advanced Tree-Seed Algorithm for Large Sized JSP Problems

Yıl 2022, Cilt: 8 Sayı: 2, 201 - 214, 01.09.2022

Mehmet Akif Şahman

Öz

Globalizing economies force manufacturing companies to develop themselves and take new measures. Planning the production process is indispensable and the Job shop scheduling (JSP) problem has a great role in planning the production accurately. In JSP, jobs have to run in the correct order on the appropriate machines, and planning to be completed in the shortest possible time is a combinatorial hard optimization problem. Meta-heuristic algorithms are frequently used in solving JSP problems, which is an NP-Hard optimization problems. In this study, the exploration and exploitation abilities in the Tree-Seed Algorithm (TSA) are enhanced with the swap, symmetry, and shift mutation operators. The proposed new TSA (Advanced TSA-ATSA) algorithm is compared with well-known meta-heuristic algorithms in the literature in large-size JSP problems. According to the results obtained from the experimental studies, the proposed ATSA has shown promising performance.

Anahtar Kelimeler

Tree-Seed Algorithm, Job Shop Sch. Prob., Meta-Heuristic Alg., Mutation Operators

Büyük Boyutlu JSP Problemlerinde Gelişmiş Ağaç-Tohum Algoritması

Öz

Küreselleşen ekonomiler, imalatçı firmaları kendilerini geliştirmeye ve yeni önlemler almaya zorlamaktadır. Üretim sürecinin planlanması vazgeçilmezdir ve üretimin doğru planlanmasında Job shop çizelgeleme (JSP) probleminin büyük rolü vardır. JSP'de, işlerin uygun makinelerde doğru sırada çalışması gerekir ve mümkün olan en kısa sürede tamamlanması için hazırlanan planlama ise kombinatoryal zorlu bir optimizasyon problemidir. NP-Zor bir optimizasyon problemi olan JSP problemlerinin çözümünde meta-sezgisel algoritmalar sıklıkla kullanılmaktadır. Bu çalışmada, Ağaç Tohum Algoritması'ndaki (TSA) keşif ve sömürü yetenekleri, takas, simetri ve kaydırma mutasyon operatörleri ile geliştirilmiştir. Önerilen yeni TSA (Gelişmiş TSA-GTSA) algoritması, büyük boyutlu JSP problemlerinde literatürde iyi bilinen meta-sezgisel algoritmalarla karşılaştırılmıştır. Deneysel çalışmalardan elde edilen sonuçlara göre önerilen GTSA'nın umut verici performans sağladığını göstermiştir.

Anahtar Kelimeler

Ağaç-Tohum Alg., İş Atl. Plan. Problemi, Meta-Sezgisel Alg., Mutasyon Operatörleri

Kaynakça

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