Overview on: sequencing in mixed model flowshop production line with static and dynamic context

In the present work a literature overview was given on solution techniques considering basic as well as more advanced and consequently more complex arrangements of mixed model flowshops. We first analyzed the occurrence of setup time/cost; existing solution techniques are mainly focused on permutation sequences. Thereafter we discussed objectives resulting in the introduction of variety of methods allowing resequencing of jobs within the line. The possibility of resequencing within the line ranges from 1) offline or intermittent buffers, 2) parallel stations, namely flexible, hybrid or compound flowshops, 3) merging and splitting of parallel lines, 4) re-entrant flowshops, to 5) change job attributes without physically interchanging the position. In continuation the differences in the consideration of static and dynamic demand was studied. Also intermittent setups are possible, depending on the horizon and including the possibility of resequencing, four problem cases were highlighted: static, semi dynamic, nearly dynamic and dynamic case. Finally a general overview was given on existing solution methods, including exact and approximation methods. The approximation methods are furthermore divided in two cases, know as heuristics and methaheuristic

Extended classification for flowshops with resequencing

Este trabajo presenta una clasificación extendida de líneas de flujo no-permutación. Se consideran las múltiples opciones que se presentan al incluir la posibilidad de resecuenciar piezas en la línea. Se ha visto que en la literatura actual no se ha clasificado con profundidad este tipo de producción. Abstract This paper presents an extended cassification for non-permutation flowshops. The versatile options which occur with the possibility of resequencing jobs within the line are considered. The literature review shows that no classification exists which considers extensively this type of flowshop

AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

Customers in the manufacturing sector, especially in the automotive industry, have a high demand for individualized products at price levels comparable to traditional mass production. The contrary objectives of providing a variety of products and operating at minimum costs have introduced a high degree of production planning and control mechanisms based on a stable order sequence for mixed-model assembly lines. A major threat to this development is sequence scrambling, triggered by both operational and product-related root causes. Despite the introduction of just-in-time and fixed production times, the problem of sequence scrambling remains partially unresolved in the automotive industry. Negative downstream effects range from disruptions in the just-in-sequence supply chain to a stop of the production process. A precise prediction of sequence deviations at an early stage allows the introduction of counteractions to stabilize the sequence before disorder emerges. While procedural causes are widely addressed in research, the work at hand requires a different perspective involving a product-related view. Built on unique data from a real-world global automotive manufacturer, a supervised classification model is trained and evaluated. This includes all the necessary steps to design, implement, and assess an AI artifact, as well as data gathering, preprocessing, algorithm selection, and evaluation. To ensure long-term prediction stability, we include a continuous learning module to counter data drifts. We show that up to 50% of the major deviations can be predicted in advance. However, we do not consider any process-related information, such as machine conditions and shift plans, but solely focus on the exploitation of product features like body type, powertrain, color, and special equipment

Mixed-model Sequencing with Reinsertion of Failed Vehicles: A Case Study for Automobile Industry

In the automotive industry, some vehicles, failed vehicles, cannot be produced according to the planned schedule due to some reasons such as material shortage, paint failure, etc. These vehicles are pulled out of the sequence, potentially resulting in an increased work overload. On the other hand, the reinsertion of failed vehicles is executed dynamically as suitable positions occur. In case such positions do not occur enough, either the vehicles waiting for reinsertion accumulate or reinsertions are made to worse positions by sacrificing production efficiency. This study proposes a bi-objective two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. Moreover, an evolutionary optimization algorithm, a two-stage local search algorithm, and a hybrid approach are developed. Numerical experiments over a case study show that while the hybrid algorithm better explores the Pareto front representation, the local search algorithm provides more reliable solutions regarding work overload objective. Finally, the results of the dynamic reinsertion simulations show that we can decrease the work overload by ~20\% while significantly decreasing the waiting time of the failed vehicles by considering vehicle failures and integrating the reinsertion process into the mixed-model sequencing problem.Comment: 26 pages, 6 figures, 5 table

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles

QUALITY ANALYSIS IN FLEXIBLE MANUFACTURING SYSTEMS WITH BATCH PRODUCTIONS

To improve product quality and reduce cost, batch production is often implemented in many exible manufacturing systems. However, the current literature does not provide any method to analyze the quality performance in a flexible manufacturing system with batch production. In this research, we present an analytical method with closed-form formula to evaluate the quality performance in such systems. Based on the model, we discover and investigate monotonic and non-monotonic properties in quality to provide practical guidance for operation management. To improve product quality, we introduce the notions of quality improvability with respect to product sequencing. In addition, we develop the indicators for quality improvability based on the data available on the factory floor rather than complicated calculations. We define the bottleneck sequence and bottleneck transition as the ones that impede quality in the strongest manner, investigate the sensitivity of quality performance with respect to sequences and transitions, and propose quality bottleneck sequence and transition indicators based on the measured data. Finally, we provide a case study at an automotive paint shop to show how this method is applied to improve paint quality. Moreover, we explore a potential application to reduce energy consumption and atmospheric emissions at automotive paint shops. By selecting appropriate batch and sequence policies, the paint quality can be improved and repaints can be reduced so that less material and energy will be consumed, and less atmospheric emissions will be generated. It is shown that such scheduling and control method can lead to significant energy savings and emission reduction with no extra investment nor changes to existing painting processes. The successful development of such method would open up a new area in manufacturing systems research and contribute to establish a solid foundation for an integrated study on productivity, quality and exibility. In addition, it will provide production engineers and operation managers a quantitative tool for continuous improvement on product quality in flexible manufacturing environmen

Sequencing in Mixed Model Non-Permutation Flowshop Production Lines using Constrained Buffers

En una línea de producción clásica, solamente se producían productos con las mismas opciones. Para la fabricación de variaciones del mismo producto básico se utilizaba una línea diferente o eran necesarias modificaciones importantes de la maquinaria. En los últimos años se ha visto acrecentada la necesidad de considerar métodos que permitan más flexibilidad ofreciendo una mayor variedad de productos al cliente. En general estos métodos consisten en producir diferentes tipos de productos en una misma línea de producción. Además, con la filosofía de Just-In-Time, los stocks y sus costes derivados, especialmente el stock de productos acabados, se reducen considerablemente y consecuentemente una producción con lotes ya no es favorable. Con este panorama la producción de distintos productos o modelos en la misma línea de forma simultánea, sin lotes, adquiere un gran auge y con ello la complejidad de gestión de la línea aumenta. La toma de decisiones en las fases de secuenciación y programación se convierte en esencial.Existen varios diseños de líneas que pueden permitir la resecuenciación, como son:utilizar grandes almacenes (Automatic-Storage-and-Retrieval-System), desacoplar una parte del proceso del resto de la línea; disponer de almacenes con plazas limitadas fuera de la línea; existencia de líneas híbridas o flexibles; posibilitar la división y unión de líneas;o cambiar los atributos de las piezas en vez de cambiar la posición en la secuencia. La resecuenciación de piezas dentro de la línea llega ser más efectiva cuando se presenta un tiempo o coste adicional, conocido como setup-time y setup-cost, necesario en muchos casos, cuando en una estación, la siguiente pieza es de otro modelo.Esta tesis considera el caso de una línea de flujo con la posibilidad de resecuenciar piezas entre estaciones consecutivas. Los almacenes están ubicados fuera de la línea y en un primer paso accesible desde una sola estación (caso del almacén intermedio). A continuación se utilizará un solo almacén, centralizado, accesible desde varias estaciones. En ambos casos se considera que una pieza, debido a su tamaño, quizás no pueda ocupar ciertas plazas del almacén ya sea intermedio o centralizado. Como resultado del estudio y análisis del Estado del Arte, que permitió delimitar el caso a estudiar, se propone una Novedosa Clasificación de líneas de flujo no permutación. Esta clasificación era indispensable, debido a que en la literatura actual no se ha clasificado con profundidad este tipo de producción, hasta hoy las clasificaciones existentes no consideran las múltiples opciones que se presentan al incluir la posibilidad de resecuenciar piezas en la línea. La presente tesis presenta distintas formulaciones: un método exacto, utilizando un modelo de programación por restricciones (CLP), varios métodos híbridos, basados en CLP, y un método heurístico, utilizando un Algoritmo Genético (GA).Durante el curso de este trabajo, los estudios que se han realizado muestran la efectividad de resecuenciar. Los resultados de los experimentos simulados muestran los beneficios que sumergen con un almacén centralizado, comparado con los almacenes intermedios.El problema considerado es relevante para una variedad de aplicaciones de líneas de flujo como es el caso de la industria química, donde los pedidos de los clientes tienen diferentes volúmenes y en la misma línea existen tanques de diferentes volúmenes para resecuenciar. También, en líneas en las cuales se utilizan lotes divididos (split-lot) con el fin de investigar variaciones en los procesos, así como en la industria de semiconductores, o en la producción de casas prefabricadas, donde fabrican paredesgrandes y pequeñas que pasan por estaciones consecutivas y en las que se instalan circuitos eléctricos, tuberías, puertas, ventanas y aislamientos.In the classical production line, only products with the same options were processed at once. Products of different models, providing distinct options, were either processed on a different line or major equipment modifications were necessary. For today's production lines approaches, considering more flexibility, are required which result more and more in the necessity of manufacturing a variety of different models on the same line, motivated by offering a larger variety of products to the client. Furthermore, with the Just-In-Time philosophy, the stock and with that the expenses derived from it, especially for finished products, are considerably reduced and lead to the case in which a production with batches is no longer favourable.Taking into account this panorama, the simultaneous production of distinct products ormodels in the same line, without batches, lead to an increased importance and at the same time the logistic complexity enlarges. The decision-making in sequencing and scheduling become essential.Various designs of production lines exist which permit resequencing of jobs within the production line: using large buffers (Automatic-Storage-and-Retrieval-System) which decouple one part of the line from the rest of the line; buffers which are located offline; hybrid or flexible lines; and more seldom, the interchange of job attributes instead of physically changing the position of a job within the sequence. Resequencing of jobs within the line is even more relevant with the existence of an additional cost or time, occurring when at a station the succeeding job is of another model, known as setup cost and setup time.The present thesis considers a flowshop with the possibility to resequence jobs between consecutive stations. The buffers are located offline either accessible from a single station (intermediate case) or from various stations (centralized case). In both cases, it is considered that a job may not be able to be stored in a buffer place, due to its extended physical size.Following the extensive State-of-the-Art, which led to the problem under study, a Novel Classification of Non-permutation Flowshops is proposed. This classification was indispensable, due to the lack of an adequate classification for flowshop production lines that would consider the diversity of arrangements which permit resequencing of jobs within the production line. Furthermore, distinct formulations are presented: an exact approach, utilizing Constrained Logic Programming (CLP), various hybrid approaches, based on CLP, and a heuristic approach, utilizing a Genetic Algorithm (GA).During the course of this work, the realized studies of performance demonstrate the effectiveness of resequencing. The results of the simulation experiments reveal the benefits that come with a centralized buffer location, compared to the intermediate buffer location.The considered problem is relevant to various flowshop applications such as chemical productions dealing with client orders of different volumes and different sized resequencing tanks. Also in productions where split-lots are used for engineering purpose, such as the semiconductor industry. Even in the production of prefabricated houses with, e.g., large and small walls passing through consecutive stations where electrical circuits, sewerage, doors, windows and isolation are applied

Otomotiv montaj hatlarında montaj öncesi ara stok içeriğinin belirlenmesi

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.Karma modelli montaj hatlarında hattın düzgün ilerlemesi, üretim kısıtları ve müşteri talepleri doğrultusunda belirlenmiş olan çizelgelenmiş sıraya uyuma bağlıdır. Fakat çizelgelenmiş sıra kasıtlı ve kasıtsız araç sıra değişiklikleri nedeniyle bozulabilmektedir. Bozulan sırayı onarmak için boyahane ile montaj departmanları arasında araç yeniden sıralanma ara stoğu bulunmaktadır. Bu ara stok bozulan sırayı üç farklı şekilde onarmaktadır: Bunlar (i) araç sıralarının değiştirilmesi, araçların yeniden sıralanması, (ii) hatalı araçların ara stokta tutulan araçlarla değiştirilmesi, (iii) son olarak da ara stok kapasitesi ve araç hatalarına bağlı olarak yeniden sıralamayla onarmanın mümkün olmadığı durumda ara stoktan montaj hattına araç beslenmesidir. Tezde, boyahanede rastgele oluşan hataları göz önünde bulunduran iki-aşamalı stokastik programlama modeli geliştirilmiştir. Modelin ilk aşamasında, bozulan sıranın onarılması için yeniden sıralama ara stoğunda tutulması gereken optimal model-renk kombinasyonlarına ait miktarlar belirlenmektedir. İkinci aşamada ise boyahanede oluşan hatalar gözlendikten sonra, araçların montaj giriş sıralarına karar verilmektedir. Problemin çözümü ara stok depolama sistemine bağlı olduğundan, otomatik depolama ve çekme sistemleri (AS/RS) ve yeniden sıralama hatları (mix-bank) için iki ayrı model kurulmuştur. Geliştirilen iki-aşamalı stokastik modelin çözümü örneklem ortalaması yaklaşımı (SAA) ile yapılmış, ara stokta tutulması gereken optimal model-renk kombinasyonlarının miktarları belirlenmiştir. Ayrıca hata oranı, ara stok kapasitesi, araçların boya giriş sıralarının çizelgelenmiş sıraya uyumu gibi problem parametrelerinin çözüme etkisini incelemek için bir sayısal çalışma yapılmıştır. Boya hatalarına bağlı araç sıra değişiklikleri anlık karar vermeye dayandığı için, araç üreticilerinin kolaylıkla uygulayabileceği kural tabanlı, sezgisel alternatif bir model kurulmuştur. Geliştirilmiş olan kural tabanlı model matematiksel modele yakın sonuçlar vermiştir. Son olarak, AS/RS ve yeniden sıralama hatlarında karşılaşılan büyük ölçekli problemleri de çözebilmek için, sunulan model genetik algoritma kullanılarak geliştirilmiştir.In mixed model assembly lines, smooth operation of the line depends on adherence to the scheduled sequence which is determined according to production constraints and customer demand. However, the scheduled sequence is scrambled due to intentional and unintentional sequence alterations. A resequencing buffer between paint and final assembly is located to restore the altered sequence. Restoring the altered sequence requires three distinct operations of this buffer: (i) Changing the positions of vehicles (i.e., resequencing), (ii) replacing spare vehicles with paint defective vehicles, (iii) lastly inserting spare vehicles to final assembly from the buffer when restoring the altered sequence is not possible by resequencing due to paint defects and limited buffer capacity. In this thesis, a two-stage stochastic programming model which considers the stochastic nature of paint defect occurrences is developed. In the first stage of the model, optimal number of model-color types placed into resequencing buffer to restore the scheduled sequence is determined. In the second stage after defect occurrences, the assembly entrance sequences of the vehicles are decided. Since the solution of the problem depends on the resequencing buffer type, two different models for automated storage and retrieval system (AS/RS) and mix-bank are built. The developed two-stage stochastic program is solved by sample average approximation (SAA) algorithm to and the optimal number of model-color types to be placed in the buffer is found. Also a numerical study is performed to investigate the problem parameters to the solution such as paint defect rate, capacity of buffer, adherence ratio of vehicles entering paint shop to scheduled sequence. Since vehicle resequencing due to the paint defects requires instant decision making, another heuristic rule based model to resequence vehicles easily by car manufacturers is developed. The proposed heuristic model performs as good as mathematical model. Lastly, to solve the large scale problems for both AS/RS and mix-bank resequencing buffers the purposed model is enhanced with genetic algorithm

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202