Gestion conjointe de production et qualité appliquée aux lignes de production non fiables

RÉSUMÉ Cette recherche s'intéresse aux lignes de production non fiables formées de plusieurs machines satisfaisant une demande fixe de produits finis de type unique et comprenant des stocks d'encours à capacité fixe. Deux types de machines sont considérés ici: un type de machine dont une partie de la production est non conforme aux normes de la qualité et un autre type de machine dont la production est 100 % conforme. La thèse est organisée selon trois contributions principales. L'objectif visé dans la première partie est de développer des modèles d'analyse de performance et des techniques d'optimisation efficaces pour le réglage des paramètres de conception suivant une approche de contrôle de type CONWIP (Constant Work-In-Process). Notre recherche s'inscrit dans le courant des approches de décomposition des ateliers de fabrication. L'analyse de la performance de ces systèmes aléatoires discrets/continus repose essentiellement sur les équations de Kolmogorov et le principe de la demande moyenne. De plus, nous introduisons des blocs de construction formés de paires de stock local-machine globale. La machine globale commune à toutes ces paires permet alors d'introduire une mesure de corrélation importante entre tous les blocs de construction quelle que soit la distance des stocks qui entrent dans leur composition. Ceci permet de créer des liens entre blocs de construction de la décomposition qui se situent au-delà de leurs voisinages respectifs, comme c'est le cas dans d'autres méthodes de décomposition. Cet aspect de corrélation des machines est caractéristique de la stratégie de production CONWIP. De plus, dans notre modélisation globale, la dynamique totale du stock dans la boucle CONWIP est considérée comme étant essentiellement affectée par les statistiques de fiabilité de la machine M1, et la probabilité de disponibilité des pièces dans le stock (n-1), reflétant ainsi l'opinion que le CONWIP est une forme de Kanban imposée à une collection de machines.----------ABSTRACT This research is concerned with unreliable production lines. Two types of machines are considered here: a machine for which part of the production is part substandard in quality and a machine whose production is 100% in conformity. The thesis is organized according to three principal contributions. In the first part of our research and for a given choice of the maximum allowable total storage parameter, the performance of constant work-in-process (CONWIP) disciplines in unreliable transfer lines subjected to a constant rate of demand for parts is characterized via a tractable approximate mathematical model. For a (n-1) machines CONWIP loop, the model consists of n multi-state machine single buffer building blocks, separately solvable once a total of (n-1)2 unknown constants shared by the building blocks are initialized. The multi-state machine is common to all building blocks, and its n discrete states approximate the joint operating state of the machines within the CONWIP loop; each of the first (n-1) blocks maps into a single internal buffer dynamics, while the nth building block characterizes total work-in-process (wip) dynamics. The blocks correspond to linear n component state equations with boundary conditions. The unknown (shared) constants in the block dynamics are initialized and calculated by means of successive iterations. The performance estimates of interest, mean total wip, and probability of parts availability at the end buffer in the loop are obtained from the model and validated against the results of Monte-Carlo simulations. In the second part of our research, we address the optimal production control problems for an unreliable manufacturing system that produces items that can be regarded as conforming or non conforming. A new stochastic hybrid state Markovian model with three discrete states, also called modes is introduced. The first two, operational sound and operational defective are not directly observable, while the third mode, failure, is observable. Production of defective parts is respectively initiated and stopped at the random entrance times to and departure times from the defective operational mode. The intricate piecewise-deterministic dynamics of the model are studied, and the associated Kolmogorov equations are developed under the suboptimal class of hedging policies

Production and maintenance planning of deteriorating manufacturing systems taking into account the quality of products

The research work presented in this thesis addresses the integration of quality aspects in the development of stochastic dynamic programming models. The goal is to determine the joint optimal production planning, and several maintenance strategies for an unreliable and deteriorating manufacturing system. In particular, we conjecture that deterioration has a severe influence on various aspects of the machine, thus this leads to divide our research work in three (3) phases. In the first one, we analyze the simultaneous production planning and quality control problem for an unreliable manufacturing system. The machine is subject to deterioration whose effect is observed mainly on the quality throughput. The quality related decisions involves a major overhaul strategy that counters the effect of deterioration. A simulation optimization approach is applied to determine the optimal control policy, providing a better understanding about the influence of quality deterioration on such system. The second phase of the research analyzes the fact where the deterioration of the production system is originated by a combination of several factors. We consider that the system deteriorates by the combined effect of the wear of the machine and imperfect repairs. Multiple operational states are implemented to model variations on the rate of defectives. Furthermore at failure, either a repair or a major overhaul can be conducted; however the machine deteriorates even more following repairs. We use a Semi-arkov decision model, since the rate of defectives is depended of the machine’s history denoted by the number of repairs and the set of multiple operational states. Then the simultaneous production plan, and repair/overhaul switching strategy are determined through numerical methods. The third phase complements the previous models by considering that the deterioration of the production systems has a twofold effect that decreases the quality of the parts produced and also increases the failure intensity. We employ the age of the machine to denote the progressive deterioration. At failure it is conducted a minimal repair that leaves the machine at the same level of deterioration before failure. To counter completely the effect of deterioration it can be performed a major overhaul. Moreover, this phase introduces preventive maintenance strategies to reduce partially the level of deterioration. This set of characteristics yields to formulate a Semi-Markov model that thorough numerical methods, we determine the joint optimal production plan and the overhaul and preventive maintenance strategies. This model clarifies the role of quality aspects on the optimal control policy. In this way our research deepens the effects of quality aspects and deterioration on the optimal control policy, and provides interesting contributions to the domain of stochastic control of manufacturing systems. Additionally, a number of numerical examples are conducted as illustration, and extensive sensitivity analyses are presented with the purpose to confirm the structure and validity of the obtained control policies. The models developed in this thesis provide further insights into the relations between the production policy and quality aspects in the context of deterioration, and also contribute to a better understanding about the behavior of stochastic manufacturing systems

Smart Sustainable Manufacturing Systems

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable, and accessible, as well as for fostering their economic and social impact. The manufacturing industry also serves as an innovator for sustainability since automation coupled with advanced manufacturing technologies have helped manufacturing practices transition into the circular economy. To that end, this Special Issue of the journal Applied Sciences, devoted to the broad field of Smart Sustainable Manufacturing Systems, explores recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing, in order to advance and promote the development of modern and intelligent manufacturing systems. In light of the above, this Special Issue is a collection of the latest research on relevant topics and addresses the current challenging issues associated with the introduction of smart sustainable manufacturing systems. Various topics have been addressed in this Special Issue, which focuses on the design of sustainable production systems and factories; industrial big data analytics and cyberphysical systems; intelligent maintenance approaches and technologies for increased operating life of production systems; zero-defect manufacturing strategies, tools and methods towards online production management; and connected smart factories

Modeling and Optimization of Disassembly Systems with a High Variety of End of Life States.

Remanufacturing is a promising product recovery method that brings new life to cores that otherwise would be discarded thus losing all value. Disassembly is a sub-process of remanufacturing where components and modules are removed from the core, sorted and graded, and directly reused, refurbished, recycled, or disposed of. Disassembly is the backbone of the remanufacturing process because this is where the reuse value of components and modules is realized. Disassembly is a process that is also very difficult in most instances because it is a mostly manual process creating stochastic removal times of components. There is a high variety of EOL states a core can be in when disassembled and an economic downside due to not all components having reuse potential. This thesis focuses on addressing these difficulties of disassembly in the areas of sequence generation, line balancing, and throughput modeling. In Chapter 2, we develop a series of sequence generation models that considers the material properties, partial disassembly, and sequence dependent task times to determine the optimal order of disassembly in the presence of a high variety of EOL states. In Chapter 3, we develop a joint precedence graph method for disassembly that models all possible EOL states a core can be in that can be used with a wide variety of line balancing algorithms. We also develop a stochastic joint precedence graph method in the situation where some removal times of components are normal random variables. In Chapter 4, we further advance the analytical modeling framework to analyze transfer lines that perform routing logics that result from a high variety of EOL states, such as a restrictive split routing logic and the possibility that disassembly and split operations can be performed at the same workstation.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111570/1/robriggs_1.pd

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Closing Loops in Supply Chain Management: Designing Reverse Supply Chains for End-of-Life Vehicles.

In this thesis, the focus is on the design of reverse supply chains for end-of-life products, in particular end-of-life vehicles. For long-term success of end-of-life management, more economic stimuli are needed than is currently the case. Legislation as a single driving force is insufficient for companies to achieve closed loop supply chains. The key issue is to find eco-efficient solutions, i.e. design and operate an economically low cost network without violating applicable targets imposed by environmental legislation. In this thesis, a case study research methodology is adopted to develop design principles for network design and assess the consequences on the operations research models. Three case studies, which stem from the network of Auto Recycling Nederland, are described in detail.

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications