Scheduling Hybrid Flow Lines of Aerospace Composite Manufacturing Systems

Composite manufacturing is a vital part of aerospace manufacturing systems. Applying effective scheduling within these systems can cut the costs in aerospace companies significantly. These systems can be characterized as two-stage Hybrid Flow Shops (HFS) with identical, non-identical and unrelated parallel discrete-processing machines in the first stage and non-identical parallel batch-processing machines in the second stage. The first stage is normally the lay-up process in which the carbon fiber sheets are stacked on the molds (tools). Then, the parts are batched based on the compatibility of their cure recipe before going to the second stage into the autoclave for curing. Autoclaves require enormous capital investment and maximizing their utilization is of utmost importance. In this thesis, a Mixed Integer Linear Programming (MILP) model is developed to maximize the utilization of the resources in the second stage of this HFS. CPLEX, with an underlying branch and bound algorithm, is used to solve the model. The results show the high level of flexibility and computational efficiency of the proposed model when applied to small and medium-size problems. However, due to the NP-hardness of the problem, the MILP model fails to solve large problems (i.e. problems with more than 120 jobs as input) in reasonable CPU times. To solve the larger instances of the problem, a novel heuristic method along with a Genetic Algorithm (GA) are developed. The heuristic algorithm is designed based on a careful observation of the behavior of the MILP model for different problem sets. Moreover, it is enhanced by adding a number of proper dispatching rules. As its output, this heuristic algorithm generates eight initial feasible solutions which are then used as the initial population of the proposed GA. The GA improves the initial solutions obtained from the aforementioned heuristic through its stochastic iterations until it reaches the satisfactory near-optimal solutions. A novel crossover operator is introduced in this GA which is unique to the HFS of aerospace composite manufacturing systems. The proposed GA is proven to be very efficient when applied to large-size problems with up to 300 jobs. The results show the high quality of the solutions achieved by the GA when compared to the optimal solutions which are obtained from the MILP model. A real case study undertaken at one of the leading companies in the Canadian aerospace industry is used for the purpose of data experiments and analysis

Intelligent demand response for industrial energy management considering thermostatically controlled loads and EVs

In this paper, an intelligent energy management framework with demand response capability was proposed for industrial facilities. The framework consists of multiple components, including industrial processes modeled by the state task network (STN) method, thermostatically controlled loads (TCLs) like the heating, ventilation and air conditioning (HVAC) system with chilled water storage (CWS), renewable generation like photovoltaic (PV) arrays and electric vehicles (EVs). These components were firstly modeled and the operation of them is then optimized in time-of-use (TOU) pricing schemes. Factors that affect several components at the same time, e.g. the number of workers, are considered. The optimization is formulated as a mixed integer linear programming (MILP) problem. A general tire manufacturing facility was investigated as the case study. Simulation results show that the proposed intelligent industrial energy management (IIEM) with DR is able to effectively utilize the flexibility contained in all parts of the facility and reduce the electricity costs as well as the peak demand of the facility, while satisfying all the operating constraints

Mejoramiento de programación de producción en planta de inyección de plásticos usando un algoritmo genético

70 páginasEste proyecto de grado buscó solucionar el problema de secuenciar un conjunto de trabajos y moldes de inyección en máquinas inyectoras en una planta de inyección, con el fin de reducir el makespan y tardanza. La planta de inyección es el primer eslabón de la cadena de producción en una fábrica de productos de consumo masivo. El proyecto se caracterizó como un problema de secuenciación de trabajos en máquinas no relacionadas paralelas. El proyecto aborda el problema con dos métodos, el primero usando programación lineal entera mixta (MILP) y el segundo usando un algoritmo genético. El método exacto funciona bien con instancias pequeñas de máximo 10 trabajos y cinco máquinas. Respecto al segundo método se diseñó un algoritmo genético con una función fitness completamente original, el algoritmo genético permite encontrar soluciones de calidad en corto tiempo para instancias más grandes y complejas si se compara con el método exacto. El algoritmo genético requirió un ajuste de sus parámetros usando diseño factorial multinivel. Con el objetivo de probar el método exacto de solución y lograr una comparación estricta entre los dos métodos de solución se desarrollaron instancias de dos tipos: random y reales con información de la planta de inyección. Luego se desarrollaron experimentos computacionales solucionando las instancias con los dos métodos. Los resultados de los experimentos permitieron establecer que el algoritmo genético propuesto genera soluciones iguales o mejores en instancias random comparado con el método exacto.This thesis developed a method to find solutions to a scheduling problem in an injection mold factory, intending to reduce the makespan and tardiness. The injection mould factory is the first stage in a massive consumer product factory location. The project was characterized as a nonrelated parallel machine scheduling problem. The project approached the problem with two methods: the 1st one using mixed-integer linear programming (MILP) and the 2nd one using a genetic algorithm. The exact solution method works fine with small instances, with a maximal size of ten jobs and five machines. About the second method, a genetic algorithm was designed with a completely original fitness function, the genetic algorithm was able to find several quality solutions in a shorter time for larger and complex instances if it is compared with the exact solution method. The genetic algorithm required several parameter adjustments, the multilevel factorial design was used to do so. With the objective of testing the exact method and to can achieve a strict comparison between both solution methods, two kinds of instances were developed: 1 kind with random data and the other one with real production data. After it, several computational experiments were developed, solving the whole instances with both solution methods. Experiments results allowed the researchers to conclude that the proposed genetic algorithm creates equal or better solutions if it is compared with the exact solution method with random instances. One study case was developed, this case represents the production schedule for a month for injected components labeled as type A, the study case was used to compare the current scheduling method used in the injection factory versus the proposed genetic algorithm. The genetic algorithm provides a scheduling program with Cmax and Tardiness values 30% lower than the current scheduling method.Maestría en Gerencia de IngenieríaMagíster en Gerencia de Ingenierí

Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

Articles publicats per investigadors de l'ETSEIB indexats al Journal Citation Reports: 2013

Informe que recull els 297 treballs publicats per 203 investigadors de l'Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB) en revistes indexades al Journal Citation Reports durant l’any 2013.Postprint (published version

13th International Conference on Modeling, Optimization and Simulation - MOSIM 2020

Comité d’organisation: Université Internationale d’Agadir – Agadir (Maroc) Laboratoire Conception Fabrication Commande – Metz (France)Session RS-1 “Simulation et Optimisation” / “Simulation and Optimization” Session RS-2 “Planification des Besoins Matières Pilotée par la Demande” / ”Demand-Driven Material Requirements Planning” Session RS-3 “Ingénierie de Systèmes Basées sur les Modèles” / “Model-Based System Engineering” Session RS-4 “Recherche Opérationnelle en Gestion de Production” / "Operations Research in Production Management" Session RS-5 "Planification des Matières et des Ressources / Planification de la Production” / “Material and Resource Planning / Production Planning" Session RS-6 “Maintenance Industrielle” / “Industrial Maintenance” Session RS-7 "Etudes de Cas Industriels” / “Industrial Case Studies" Session RS-8 "Données de Masse / Analyse de Données” / “Big Data / Data Analytics" Session RS-9 "Gestion des Systèmes de Transport” / “Transportation System Management" Session RS-10 "Economie Circulaire / Développement Durable" / "Circular Economie / Sustainable Development" Session RS-11 "Conception et Gestion des Chaînes Logistiques” / “Supply Chain Design and Management" Session SP-1 “Intelligence Artificielle & Analyse de Données pour la Production 4.0” / “Artificial Intelligence & Data Analytics in Manufacturing 4.0” Session SP-2 “Gestion des Risques en Logistique” / “Risk Management in Logistics” Session SP-3 “Gestion des Risques et Evaluation de Performance” / “Risk Management and Performance Assessment” Session SP-4 "Indicateurs Clés de Performance 4.0 et Dynamique de Prise de Décision” / ”4.0 Key Performance Indicators and Decision-Making Dynamics" Session SP-5 "Logistique Maritime” / “Marine Logistics" Session SP-6 “Territoire et Logistique : Un Système Complexe” / “Territory and Logistics: A Complex System” Session SP-7 "Nouvelles Avancées et Applications de la Logique Floue en Production Durable et en Logistique” / “Recent Advances and Fuzzy-Logic Applications in Sustainable Manufacturing and Logistics" Session SP-8 “Gestion des Soins de Santé” / ”Health Care Management” Session SP-9 “Ingénierie Organisationnelle et Gestion de la Continuité de Service des Systèmes de Santé dans l’Ere de la Transformation Numérique de la Société” / “Organizational Engineering and Management of Business Continuity of Healthcare Systems in the Era of Numerical Society Transformation” Session SP-10 “Planification et Commande de la Production pour l’Industrie 4.0” / “Production Planning and Control for Industry 4.0” Session SP-11 “Optimisation des Systèmes de Production dans le Contexte 4.0 Utilisant l’Amélioration Continue” / “Production System Optimization in 4.0 Context Using Continuous Improvement” Session SP-12 “Défis pour la Conception des Systèmes de Production Cyber-Physiques” / “Challenges for the Design of Cyber Physical Production Systems” Session SP-13 “Production Avisée et Développement Durable” / “Smart Manufacturing and Sustainable Development” Session SP-14 “L’Humain dans l’Usine du Futur” / “Human in the Factory of the Future” Session SP-15 “Ordonnancement et Prévision de Chaînes Logistiques Résilientes” / “Scheduling and Forecasting for Resilient Supply Chains

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions