Evolutionary algorithms for multi-objective flexible job shop cell scheduling

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is a challenging real-world problem. This recently introduced scheduling problem variant considers exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation requiring minimization of makespan and total tardiness, simultaneously. A previous study shows that the exact solver based on mixed-integer nonlinear programming model fails to find an optimal solution to each of the ‘medium’ to ‘large’ size instances considering even the simplified version of the problem. In this study, we present evolutionary algorithms for solving that bi-objective problem and apply genetic and memetic algorithms that use three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all evolutionary algorithms with various configurations is investigated across forty-three benchmark instances from ‘small’ to ‘large’ size including a large real-world problem instance. The experimental results show that the transgenerational memetic algorithm using weighted sum outperforms the rest producing the best-known results for almost all bi-objective flexible job shop cell scheduling instances, in overall

A parameter-free discrete particle swarm algorithm and its application to multi-objective pavement maintenance schemes

Regular maintenance is paramount for a healthy road network, the arteries of any economy. As the resources for maintenance are limited, optimization is necessary. A number of conflicting objectives exist with many influencing variables. Although many methods have been proposed, the related research is very active, due to difficulties in adoption to the actual practice owing to reasons such high-dimensional problems even for small road networks. Literature survey tells that particle swarms have not been exploited much, mainly due to unavailability of many techniques in this domain for multi-objective discrete problems like this. In this work, a novel particle swarm algorithm is proposed for a general, discrete, multi-objective problem. In contrast to the standard particle swarm, the bare-bones technique has a clear advantage in that it is a parameter-free technique, hence the end users need not be optimization experts. However, the existing barebones algorithm is available only for continuous domains, sans any particle velocity terms. For discrete domains, the proposed method introduces a parameter-free velocity term to the standard bare-bones algorithm. Based on the peak velocities observed by the different dimensions of a particle, its new position is calculated. A number of benchmark test functions are also solved. The results show that the proposed algorithm is highly competitive and able to obtain much better spread of solutions compared to three other existing PSO and genetic algorithms. The method is benchmarked against a number of other algorithms on an actual pavement maintenance problem. When compared against another particle swarm algorithm, it not only shows better performance, but also significant reduction in run-time compared to other POS algorithm. Hence, for large road network maintenance, the proposed method shows a lot of promise in terms of analysis time, while improving on the quality of solutions

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

Esnek atölye tipi hücre çizelgeleme problemleri için çok amaçlı matematiksel model ve genetik algoritma ile çözüm önerisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Günümüz rekabetçi iş ortamında, müşteriler daha düşük maliyetle daha yüksek kalitede çeşitli ürünleri satın almak istemektedir. İmalat firmaları, talep çeşitliliğini karşılamak için yüksek derecede ürün çeşitliliğine ve küçük imalat parti büyüklüğüne ihtiyaç duymaktadır. Üretimdeki ürün çeşitlilikleri uzun hazırlık ve taşıma süreleri, karmaşık çizelgeleme problemleri gibi birçok probleme neden olmaktadır. Geleneksel imalat sistemleri, bu tip değişikliklere cevap vermede yeterince esnek değilken Hücresel Üretim Sistemleri üreticilerin bu ihtiyaçlarına cevap verebilecek özelliklere sahiptir. Ayrıca gerçek hayat problemlerinin çoğunda, bir parçanın bazı ya da bütün operasyonları birden fazla makinede işlem görebilmekte ve bazen de bu operasyonlar bir makineyi ya da iş merkezini birden fazla kez ziyaret etmektedir. Bu seçenek sisteme esneklik kazandırırken bu kadar karmaşık bir üretim sisteminin başarılı ve doğru bir şekilde işletilebilmesi kaynakların etkin kullanılmasını da gerektirmektedir. Bu çalışma, istisnai parçaları, hücrelerarası hareketleri, hücrelerarası taşıma sürelerini, sıra bağımlı parça ailesi hazırlık sürelerini ve yeniden işlem gören parçaları dikkate alarak hücresel imalat ortamında esnek atölye tipi çizelgeleme probleminin çözümüne dair bir matematiksel model ve çözüm yöntemi sunmaktadır. Mevcut bilgilerimiz ışığında yapılan bu çalışma Esnek Atölye Tipi Hücre Çizelgeleme Probleminde (EATHÇP) çok amaçlı matematiksel model ve meta-sezgiselinin kullanımı için ilk girişimdir. Bununla birlikte gerçek hayat uygulamaları için EATHÇP süreci, birçok çelişen amacı dikkate almayı gerektirdiği için ele alınan skalerleştirme metodu pratik uygulama ve teorik araştırma açısından oldukça önemlidir. Önerilen karma tamsayılı doğrusal olmayan matematiksel modelle küçük ve orta boyutlu problemler çözülebilmektedir. Büyük boyutlu problemlerin çözümü, doğrusal olmayan modellerle makul zamanlarda olamayacağı ya da çok uzun süreceği için konik skalerleştirmeli çok amaçlı matematiksel modeli kullanan bir Genetik Algoritma (GA) meta-sezgisel çözüm yöntemi önerilmiştir. GA yaklaşımının en iyi veya en iyiye yakın çözüme ulaşmasına etki eden parametrelerin en iyi kombinasyonu belirlemek amacı ile bir deney tasarımı gerçekleştirilmiştir. Bu tez çalışması için Eskişehir Tülomsaş Motor Fabrikası'nda bir uygulama çalışması yürütülmüştür. Yürütülen bu çalışma, altı farklı amaç ağırlık değerleri kullanılarak hem konik skalerleştirmeli GA yaklaşımı ile hem de ağırlıklı toplam skalerleştirmeli GA yaklaşımı ile çözülmüştür. Amaç ağırlıklarının beşinde çok amaçlı konik skalerleştirme GA yaklaşımının daha baskın sonuçlara ulaşabildiği vurgulanmıştır. Ayrıca, önerilen çok amaçlı modelin gerçek hayat problemleri için de makul zamanda uygun çözümler üretebildiği gösterilmiştir.In today's highly competitive business environment, customers desire to buy various products with higher quality at lower costs. Manufacturing firms require a high degree of product variety and small manufacturing lot sizes to meet the demand variability. The product variations in manufacturing cause many problems such as lengthy setup and transportation times, complex scheduling. Cellular Manufacturing Systems contain the characteristics, which will respond to the needs of manufacturers, even though Conventional Manufacturing Systems are not flexible enough to respond to changes. In addition, in most real life manufacturing problems, some or all operations of a part can be processed on more than one machine, and sometimes operations may visit a machine or work center more than once. It is necessary to use resources effectively in order to run such a complex production system successfully. In this study, a mathematical model and a solution approach that deals with a flexible job shop scheduling problem in cellular manufacturing environment is proposed by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation. To the best of our knowledge, this is the first attempt to use multi-objective mathematical model and meta-heuristic approach for a Flexible Job Shop Cell Scheduling Problem (FJCSP). However, in the real-life applications, the scalarization method considered is highly important in terms of theoretical research and practical application because the FJCSP process is not easy because of many conflicting objectives. The proposed mixed integer non-linear model can be used for solving small and middle scaled problems. Solution of large scaled problems is not possible in reasonable time or takes too long time, so a Genetic Algorithm (GA) meta-heuristic approach that uses a multi-objective mathematical model with conic scalarization has been presented. An experimental design was used to determine the best combination of parameters which are affected performance of genetic algorithm to achieve optimum or sub-optimum solution. In this thesis study, a case study was conducted in Tülomsaş Locomotive and Engine Factory in Eskişehir. This study was solved by using both conic scalarization GA approach and weighted sum scalarization GA approach with six different weights of objective. It is emphasized that the multi-objective conic scalarization GA approach has better quality than other approach for five different weights of objective. In addition, it has been shown that the multi-objective model could also obtain optimum results in reasonable time for the real-world problems

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies