Disassembly Line Balancing Problem Under The Effects of Sum-of-Logarithm-Processing-Time-Based Learning and Job Deterioration

Year 2023, Volume: 14 Issue: 4, 683 - 695, 31.12.2023

Halime Somtürk M. Duran Toksarı

https://doi.org/10.24012/dumf.1366117

Abstract

Product recovery or remanufacturing has recently received a lot of attention as a result of rising environmental consciousness, economic worries, and laws and regulations. Disassembly is one of the most important processes in the recovery phase of end-of-life products. It's crucial to develop efficient and balanced disassembly lines. In this study, disassembly line balancing (DLB) problems are investigated under the effects of simultaneous sum-of-logarithm-processing-time-based learning and job deterioration. In the DLB literature, it is the first time to study simultaneous sum-of-logarithm-processing-time-based learning and job deterioration. Any job's processing time is influenced by the logarithmic sum of the processing times of its predecessors, in the station's order. Job deterioration delays the job start time. While learning reduces the processing time of jobs, deterioration increases. In this study, the objective function is the minimization of the number of opened stations. A hybrid PSO-GA algorithm has been developed to solve the DLB problem under the effects of learning and deterioration. Results for different learning and deterioration rates were obtained and comparisons were made. When the learning and deterioration effects were taken into account in DLB problems, improvements were observed in the objective function value.

Keywords

Disassembly line balancing, sum-of-logarithm-processing-time-based learning effect, job deterioration effect, hybrid PSO-GA approach

Logaritmik İşlem Süreleri Toplamı Tabanlı Öğrenme ve İş Bozulması Etkileri Altında Demontaj Hattı Problemi

Abstract

Artan çevresel farkındalık, ekonomik kaygılar ve yasal kurallar sayesinde, son zamanlarda ürün geri kazanımı veya yeniden üretimi yoğun ilgi görmektedir. Demontaj, ömrünü tamamlamış ürünlerin geri kazanımı aşamasındaki en önemli süreçlerden biridir. Bu yüzden, etkin ve dengeli kurulmuş demontaj hatları önem taşımaktadır. Bu çalışmada, demontaj hattı dengeleme (DHD) problemleri, eş zamanlı logaritmik işlem süreleri toplamı tabanlı öğrenme ve iş bozulması etkileri altında incelenmiştir. DHD literatüründe, logaritmik işlem süreleri toplamı tabanlı öğrenme ve iş bozulması eş zamanlı olarak ilk kez çalışılmıştır. Herhangi bir iş, istasyondaki sırasına göre, kendisinden önceki işlerin işlem sürelerinin logaritmik toplamından etkilenmektedir. İş bozulması ise, işin işleme başlamasını geciktiren bozulmalardır. Öğrenme işlerin işlem sürelerinin azaltırken, bozulma artırmaktadır. Bu çalışmada amaç fonksiyonu, açılan istasyon sayısı minimizasyonudur. Öğrenme ve bozulma etkisi altındaki DHD probleminin çözümü için hibrit (Parçacık Sürüsü Optimizasyonu- Genetik Algoritma) PSO-GA algoritması geliştirilmiş. Farklı öğrenme ve bozulma oranları için sonuçlar elde edilip karşılaştırmalar yapılmıştır. Öğrenme ve bozulma etkisinin, DHD problemlerinde dikkate alındığında amaç fonksiyonu değerinde iyileşmeler görülmüştür.

Keywords

Demontaj hattı dengeleme, logaritmik işlem süreleri toplamı tabanlı öğrenme etkisi, iş bozulması etkisi, hibrit PSO-GA yaklaşımı

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