Designing a manufacturing cell system by assigning workforce

Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce. Design/methodology/approach: Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms. Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation. Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells.Peer Reviewe

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

Artificial Intelligence as an Enabler of Quick and Effective Production Repurposing Manufactur-ing: An Exploratory Review and Future Research Propositions

The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development

Optimal Configuration of Inspection and Rework Stations in a Multistage Flexible Flowline

Inspection and rework are two important issues of quality control. In this research, an N-stage flowline is considered to make decisions on these two issues. When defective items are detected at the inspection station the items are either scrapped or reworked. A reworkable item may be repaired at the regular defect-creating workstation or at a dedicated off-line rework station. Two problems (end-of-line and multistage inspections) are considered here to deal with this situation. The end-of-line inspection (ELI) problem considers an inspection station located at the end of the line while the multistage inspection (MSI) problem deals with multiple in-line inspection stations that partition the flowline into multiple flexible lines. Models for unit cost of production are developed for both problems. The ELI problem is formulated for determining the best decision among alternative policies for dealing with defective items. For an MSI problem a unit cost function is developed for determining the number and locations of in-line inspection stations along with the alternative decisions on each type of defects. Both of the problems are formulated as fractional mixed-integer nonlinear programming (f-MINLP) to minimize the unit cost of production. After several transformations the f-MINLP becomes a mixed-integer linear programming (MILP) problem. A construction heuristic, coined as Inspection Station Assignment (ISA) heuristic is developed to determine a sub-optimal location of inspection and rework stations in order to achieve minimum unit cost of production. A hybrid of Ant-Colony Optimization-based metaheuristic (ACOR) and ISA is devised to efficiently solve large instances of MSI problems. Numerical examples are presented to show the solution procedure of ELI problems with branch and bound (B&B) method. Empirical studies on a production line with large number of workstations are presented to show the quality and efficiency of the solution processes involved in both ELI and MSI problems. Computational results present that the hybrid heuristic ISA+ACOR shows better performance in terms of solution quality and efficiency. These approaches are applicable to many discrete product manufacturing systems including garments industry

Un problema de programación de la producción en células de fabricación que incluye almacenes

En este trabajo se presenta un problema específico de programación de la producción flow-shop de interés práctico. El sistema de fabricación está configurado como una célula de fabricación y en el planteamiento del problema se consideran los almacenes de materias primas y de productos terminados. El desempeño de la programación se evalúa de una manera multiobjetivo, considerando el tiempo total de producción (makespan) y la tardanza total (tardiness). Se propone una formulación matemática para el problema. Además, se presenta una estrategia meta-heurística para resolver eficientemente dicho problema y obtener soluciones de buena calidad en un tiempo computacional razonable. El procedimiento aplicado se basa en una adaptación de la metaheurística de recocido simulado. Se generaron conjuntos de problemas para evaluar el método propuesto, obteniendo soluciones óptimas o casi óptimas en tiempos significativamente menores que los requeridos por el enfoque de optimización resuelto mediante CPLEX. Además, el algoritmo fue probado con problemas de mayor tamaño, para evaluar su comportamiento en espacios de búsqueda más extensos.Sociedad Argentina de Informática e Investigación Operativ

Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura

Introduction: Cellular Manufacturing (CM) is an application of group technology that consists of grouping components in part families and machines into cells, via the decomposition of a complex manufacturing system into small systems, which attend the operations of entire part families. In this work, we developed a linear programming model that integrates production costs with costs for transfers between cells. Besides, using an approach algorithm method called Harmony Search is solved the mathematical model. Objective: Evaluate the performance of the alternative Harmony Search and its machine allocation strategies in a cellular manufacturing problem. Method: The mathematical model consists in a linear programming structure in which there are binary variables to determine the assignment of the operations of products to different machines in diverse cells, and integer variables to count the requirements of machines and the number of transfers between cells. In order to validate the model, we use modified instances based on the literature review and set in GAMS software using the CPLEX solver, also, is developed a metaheuristic algorithm in MATLAB in order to give an approximate solution. Results:  The proposed Harmony Search and its variants can provide highlighted results taking advantage of the exploitation approach of the search space. Conclusions: The Harmony Search and its variants can provide outstanding solutions in considerably short times; nevertheless, it is necessary to implement strategies to explore the search space in order to avoid falling into local optima.Introducción: La manufactura celular (MC) es una aplicación de la tecnología de grupos que consiste en la agrupación de familias de productos y la formación de familias de máquinas, mediante la descomposición de un sistema de manufactura complejo en subsistemas que atienden las operaciones de familias enteras de productos. Durante el presente trabajo se desarrolla un modelo de programación lineal entera que integra costos de producción con costos por transferencias entre celdas, además, se propone como método de solución un algoritmo denominado Búsqueda Armónica Objetivo: Determinar el desempeño de la Búsqueda Armónica modificada y las variantes de asignación de máquinas al problema de formación de celdas de manufactura. Metodología: Se desarrolla un modelo matemático de programación entera, el cual utiliza variables binarias para determinar la asignación de las operaciones de diversos productos a distintas máquinas en diferentes celdas, y variables enteras para cuantificar los requerimientos de máquinas y la cantidad de transferencias entre celdas. La validación del modelo se hace utilizando instancias modificadas de la literatura en el software GAMS usando el solver CPLEX y se desarrolla en MATLAB el algoritmo metaheurístico para dar solución aproximada. Resultados: Se encuentra que las variantes propuestas integradas en la Búsqueda Armónica logran buenos resultados aprovechando el enfoque de explotación del espacio de búsqueda. Conclusiones: A partir de las variantes aplicadas se logró encontrar muy buenas soluciones en tiempos considerablemente cortos; no obstante, es necesario implementar estrategias de exploración del espacio de búsqueda con el fin de evitar caer en óptimos locales

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