Takım çalışması esaslı çok-ürünlü demontaj hatlarının tasarımı ve planlanması: Optimizasyon modeli ve çözüm algoritmaları
Design and planning of multi-manned multi-product disassembly lines: A generi̇c optimization model and solution algorithms
Yazar: FATMA BETÜL YENİ
Danışman: PROF. DR. EMRE ÇEVİKCAN
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Endüstri Mühendisliği Bilim Dalı
Derece: Doktora, 2023
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering
Dizin: Karışık tam sayılı doğrusal programlama = Mixed  integer linear programming ; Metasezgiseller = Metaheuristics

Özet:
Günümüzde büyük bir hızla değişen müşteri ihtiyaçları ve tüketim alışkanlıkları beraberinde kullanılan ürünlerin erkenden demode hale gelmesine ve yenisiyle değiştirilmesine neden olmaktadır. Bu durum ise geride büyük bir atık bırakmaktadır. Atıkla yola çıkmanın en kolay yolu bertaraf yöntemidir. Ancak ekonomik ve çevresel koşullar dikkate alındığında, atık malzemelerin çevreye olan zararının minimize edilmesi oldukça önemli bir konu olarak karşımıza çıkmaktadır. Nitekim ülkemizde olduğu gibi dünya genelinde de bu konuya oldukça hassas yaklaşılmakta ve devletler bu hususta yasal düzenlemelere gitmektedir. Bu da beraberinde atık ürünlerin geri dönüşümüne verilen önemi arttırmaktadır.
Atık ürünlerin geri dönüşümü sürecinde en yaygın kullanılan yöntem demontaj faaliyetleridir. Kullanım ömrünü tamamlamış ürünler her ne kadar tekrardan kullanılamaz ve tamir edilemez kabul edilse de içerdikleri bileşenlerden bir kısmı ayrıştırılarak yeniden üretime dahil edilebilmektedir. Demontaj faaliyetleri tek bir iş istasyonunda, demontaj hattında veya demontaj hücresinde yürütülebilmektedir. Bunların arasında demontaj hatları, atık ürünlerin otomatik olarak sökülmesi için en iyi seçenek olarak kabul edilmektedir. Demontaj hatlarının etkin tasarımı ve dengelenmesi hem çevresel faktörler açısından hem de ürünün geri kazanım verimliliği açısından oldukça önemlidir.
Geleneksel demontaj hattı dengeleme problemlerinde sıklıkla tek bir ürün modeli ve tek işçinin çalıştığı istasyon konsepti dikkate alınmaktadır. Ancak gerçek hayatta geri dönüşüm tesisleri birden çok modelde ürünün demontaj faaliyetlerini yürütmektedir. Bununla birlikte demontaj sırasında yapılacak faaliyetler gruplanabilmekte ve eş zamanlı olarak yapılabilmektedir. Bu da her bir istasyonda birden çok işçinin çalışmasına olanak sağlamaktadır. Geleneksel çalışma mantığından farklı olan bu tip takım çalışması esasına dayanan iş istasyonlarında işler öncelik ilişkilerine göre belirli kısıtlar altında gruplandırılarak işçilere atanmakta ve bu sayede demontaj faaliyetleri daha az sayıda istasyonda, daha kısa sürede tamamlanmaktadır.
Yukarıdaki bilgiler ışığında hazırlanan bu tez çalışmasında takım çalışması esaslı çok- ürünlü çok-periyotlu demontaj dengeleme problemi ele alınmıştır. Problemin amacı dönemsel olarak her ürün modeli için demonte edilecek miktarı hesaplamak ve buna göre o dönem için efektif bir hat dengelemesi yapmaktır. Ele alınan problem literatürde bir ilk olduğundan, öncelikle problemi matematiksel olarak ifade etmek için genel bir optimizasyon modeli önerilmiştir. Toplam maliyet minimizasyonunu amaçlayan bu problemin karmaşıklık açısından NP-tam olması nedeniyle, çözümü için genetik algoritma (GA) tabanlı çözüm algoritmaları geliştirilmiştir. Üç farklı taktik seviye politika (Rassal-R, Tam zamanında – JIT, Ekonomik Demontaj Miktarı – EDQ) ile iki farklı çaprazlama operatörü (CR1, CR2) üzerinden geliştirilen bu altı farklı algoritmanın çözüm kalitesini arttırmak için tavlama benzetimi (TB) tabanlı yerel arama algoritmasından yararlanılmıştır. Problemi daha detaylı analiz etmek ve taktiksel açıdan dikkate değer içgörüler elde etmek için farklı boyuttaki test problemleri üzerinde karşılaştırmalı analizler yapılmıştır. Elde edilen sonuçlara göre, GACR1-JIT algoritması, EDQ ve R politikalarına dayalı olarak geliştirilen diğer algoritmalara kıyasla toplam maliyette önemli bir düşüş sağlamaktadır. Ayrıca, JIT tabanlı parti büyüklüğü politikasının uygulanmasının önemi, özellikle dönem sayısı arttıkça daha da belirgin hale gelmektedir.

Abstract:
The rapidly changing customer needs and consumption habits cause the products used to become outdated early and be replaced with new ones. This situation leaves a huge waste behind. That being the case, in today's economic and environmental conditions, minimising the environmental impact of waste materials is one of the most significant issues in manufacturing. States are approach to this situation very sensitive and making legal arrangements in this regard. Strict legislations enacted by governments to reduce the environmental impacts of end-of-life (EOL) products lead to the growing interest in the product recovery.
A general recovery process consists of disintegrating the disused or outdated products by selected disassembly activities. Although the EOL products are considered non- reusable and non-repairable, some of the components they contain can be separated and included in manufacturing process again. By definition, disassembly is a systematic extraction of parts from EOL products with respect to predetermined objectives. After the disassembly operations are performed, the obtained parts can be reused in the same manufacturing process for new products or delivered to recyclers according to their demands.
According to the disassembly operations, a cell, a line or a single workstation may be suitable for the disassembly process. Because enterprises have paid close attention to the disassembly line as one of the most suitable settings to recover an EOL product, the disassembly line balancing problem (DLBP) has been of great interest. As a critical step for disassembly line design, the DLBP is related with assigning tasks to workstations with respect to the pre-determined objective(s). Unlike an assembly process, the product is parted into a plurality of subcomponents whose qualities, quantities and reliabilities have to be maintained without deterioration in a disassembly process. The DLBP differs from the traditional assembly line balancing problem (ALBP) not only because of the disassembly operations but also the presence of AND, OR predecessor/successor precedence relations between disassembly tasks. With additional precedence relation types, DLBP is considered to be more complicated compared to ALBP.
The traditional DLBP often consider a single product model and a single worker station concept. However, in real life, disassembly activities of multiple products in different sizes are carried out in recycling facilities. Besides, the activities to be performed during disassembly can be grouped and performed simultaneously. This situation allows more than one worker to work at each station. In this type of workstations based on teamwork (known as multi-manned stations) which differs from the traditional working logic, the jobs are grouped under certain constraints according to the priority relations and assigned to the workers, and thus disassembly activities are completed in a smaller number of stations in a shorter time.
The most distinctive characteristic of the lines with multi-manned stations is that different tasks of the same workpiece are allowed to be performed synchronously within each station. However, parallel execution of tasks for the same workpiece cannot be observed in lines with single-worker stations.
Multi-manned stations have many advantages over classical stations in terms of quality, flexibility, line length and work satisfaction. In the multi-manned station concept; (i) the lead time is shortened due to the parallel exacution of tasks in stations, (ii) communication among workers is provided and monotonousness is prevented by handling task variety, (iii) line lenght is shortened due to the fewer stations, (iv) the changes or improvements in product design is responded quickly due to the worker expertise.
With that in mind, the disassembly line balancing decisions must be made by taking multi-manned stations into account, so-called multi-manned DLBP. Even though the DLBP has attracted attention over the last decade, the addressed problem remains scarcely explored in the literature. Being thus motivated, in this study, the multi- manned station concept is explored by extending the DLBP to include multi-products and multi-periods for a more realistic perspective. Moreover, the lot-sizing problem is integrated into the DLBP to further analyze the influence of the quantity of disassembled products on the system performance over the planning horizon. The integrated problem is composed of the lot-sizing, assignment, and scheduling decisions in which the quantity of EOL products, assignment of tasks and workers to stations, and scheduling of tasks to be performed must be determined simultaneously for product type over periods.
A generic optimization model is presented for the proposed problem. Since the problem is strongly NP-hard in terms of complexity, genetic algorithm (GA) based solution approaches are developed for the solution. Whilst the algorithms are developed, three different lot size methods which mainly focus on the tactical level policies are considered: (i) Random (R) based lot size policy, (ii) Just-intime (JIT) based lot size policy, and (iii) Economic Disassembly Quantity (EDQ) based lot size policy. The policies employed in algorithms during the initial populations are generated and continue to be implemented during the next iterations as well. By doing so, several important insights into the problem are revealed regarding the theoretical and practical implications. Further, two different crossover operators namely One- point-crossover (CR1) and Two-point-crossover (CR2) are developed to generate high-quality solutions for each implemented policy respectively. The impact of tactical disassembly policies along with the crossover operators are analyzed through computational experiments. While the initial solution generations procedures are applied at the first iteration, the crossover and mutation operators are used through the subsequent iterations. The crossover operator is dealt only with the task and worker assignments, i.e. the left side of the chromosome. On the other hand, the mutation operator is implemented for the disassembly quantities by employing randomness in the initial solution generation procedures. Furthermore, the Simulated Annealing (SA) based local search algorithm is used within the proposed approaches to improve the solutions.
A set of nine specific test problems are generated by considering the combinations of two, four, and six product types and two, four, and six periods respectively to compare the algorithms. For a detailed comparison Relative Percentage Deviation (RPD) ratio is also employed.
According to the computational results, the smallest fitness function values are achieved when the JIT-based initial solution generation procedure is applied. From this point of view, because the JIT-based policy demonstrates superiority, other lean manufacturing principles can be employed for recovering activities, such as production leveling and smoothing. It is also interesting to state that the GA crossover operators do not make a significant effect on the results in terms of the fitness function. Nonetheless, the one-point crossover operator is recommended to be used, as it performs slightly better for the JIT-based lot size policy. That being the case, applying the right lot size policy is enough to improve the system's performance. When the reliability of results is considered, it is verified that while the R-based lot size policy causes greater variability, the JIT-based policy provides reliable and robust results. As an overall conclusion, the GACR1-JIT algorithm is recommended to be used for the addressed problem and its variants. On top of that, the structure of problems is analyzed and it is revealed that while the largest RPD values are yielded when the number of periods is six, the smallest values are always obtained when the number of periods is two. Therefore, it can be concluded that as the number of periods increases, the importance of applying the JIT-based lot size policy becomes more prominent.