Integrated production quality and condition-based maintenance optimisation for a stochastically deteriorating manufacturing system

This paper investigates the problem of optimally integrating production quality and condition-based maintenance in a stochastically deteriorating single- product, single-machine production system. Inspections are periodically performed on the system to assess its actual degradation status. The system is considered to be in ‘fail mode’ whenever its degradation level exceeds a predetermined threshold. The proportion of non-conforming items, those that are produced during the time interval where the degradation is beyond the specification threshold, are replaced either via overtime production or spot market purchases. To optimise preventive maintenance costs and at the same time reduce production of non-conforming items, the degradation of the system must be optimally monitored so that preventive maintenance is carried out at appropriate time intervals. In this paper, an integrated optimisation model is developed to determine the optimal inspection cycle and the degradation threshold level, beyond which preventive maintenance should be carried out, while minimising the sum of inspection and maintenance costs, in addition to the production of non-conforming items and inventory costs. An expression for the total expected cost rate over an infinite time horizon is developed and solution method for the resulting model is discussed. Numerical experiments are provided to illustrate the proposed approach

Conception conjointe des politiques de contrôle de production, de qualité et de maintenance des systèmes manufacturiers en dégradation

RÉSUMÉ : La gestion de la production, le contrôle de la qualité et la planification de la maintenance sont les trois principales fonctions de la gestion des opérations dans les usines manufacturières. Dans la pratique, ces trois fonctions sont souvent gérées séparément, bien qu’elles soient, en réalité, étroitement inter-reliées. Plusieurs recherches ont été menées depuis des décennies afin de concevoir et d’optimiser conjointement les politiques de contrôle de la production, de la qualité et de la maintenance. Cette tendance est motivée par le fait que les politiques d’intégration des trois fonctions permettent d’améliorer la productivité et de réduire considérablement les coûts. Cependant, dans la quasi-totalité des modèles d’intégration dans la littérature, seulement deux fonctions sont intégrées à la fois. De plus, pour des raisons de simplification, ces modèles sont basés sur certaines hypothèses simplificatrices et irréalistes pour modéliser la dégradation de la qualité des produits et de la fiabilité des machines. Par exemple, ces modèles négligent souvent l’impact des opérations de production sur l’intensité de dégradation, mais aussi la corrélation entre les dégradations de la qualité et de la fiabilité. Par ailleurs, les politiques de contrôle de la qualité utilisées dans ces modèles sont soit les cartes de contrôle, soit le contrôle à 100%. Toutefois, l’intégration des plans d’échantillonnage, qui représentent une branche importante de Contrôle Statistique de la Qualité, avec les politiques de production et de maintenance n’a pas été étudiée encore dans la littérature. Ces plans sont largement utilisés dans l’industrie depuis longtemps afin d’éviter le coût excessif du contrôle à 100% et d’assurer en même un contrôle statistique de la qualité des produits livrés. Cette thèse s’intéresse au problème de conception conjointe des politiques de contrôle de la production, de la qualité et de la maintenance. L’objectif principal de la recherche est d’intégrer les plans d’échantillonnage avec les politiques de production et de maintenance des systèmes où la qualité et la fiabilité sont les deux sujettes à la dégradation. Nous proposons une approche pratique de modélisation et d’optimisation de ces politiques qui permet de prendre en considération la dynamique complexe de la dégradation telle que dans la réalité des systèmes manufacturiers. En outre, nous étudions les propriétés statistiques des plans d’échantillonnage afin de montrer comment des informations pertinentes fournies par ces plans peuvent être intégrées dans la planification des activités de maintenance préventive afin d’améliorer les performances globales des systèmes manufacturiers. Les contributions scientifiques réalisées dans le cadre de cette thèse sont présentées sous forme de quatre articles de revue. Le premier article introduit un modèle d’intégration du plan d’échantillonnage simple avec la commande de la production pour un système de fabrication par lots. Ce modèle vise essentiellement à étudier les interactions entre les paramètres du plan d’échantillonnage et les paramètres de gestion de la production tels que la taille du lot de production et le stock de sécurité. Ensuite, une extension de ce modèle est proposée dans le deuxième article afin de considérer l’aspect dynamique de la dégradation de la qualité et de la fiabilité en fonction des opérations de production et d’intégrer une politique de maintenance préventive. L’objectif est d’optimiser conjointement les paramètres de contrôle de la production, de la qualité et de la maintenance de façon à minimiser le coût total des opérations, tout en respectant une contrainte sur la qualité après-contrôle. De plus, cet article vise à montrer l’utilité des informations issues du plan d’échantillonnage simple pour la surveillance de la qualité de la production et pour l’organisation des actions de maintenance préventive. Une analyse comparative de l’utilisation de plan d’échantillonnage par rapport au contrôle à 100% est aussi fournie afin de quantifier les gains économiques qui en découleraient. Le troisième article propose une approche d’intégration du plan d’échantillonnage continu de type-1 (CSP-1) avec les politiques de production et de maintenance préventive pour les systèmes de production continue. L’objectif est d’étendre l’applicabilité du plan CSP-1 aux processus de production en dégradation, puisqu’il est actuellement applicable seulement aux processus stables. Un autre objectif de cet article est de quantifier les bénéfices de l’utilisation de CSP-1 par rapport au contrôle à 100%, et de montrer aussi comment le couplage de CSP-1 avec la maintenance préventive permet d’améliorer les performances des systèmes en dégradation. Finalement, le quatrième article introduit un modèle de contrôle conjoint de la production, de la qualité et de la maintenance d’une ligne de production dont les machines sont sujettes à la dégradation. En plus, les machines peuvent tomber en panne à cause des pièces non-conformes fabriquées dans les processus en amont. L’objectif est de montrer l’importance de la corrélation entre les dégradations de la qualité et de la fiabilité dans la modélisation de la dynamique des systèmes manufacturiers, et d’étudier l’effet de cette corrélation sur les paramètres optimaux du contrôle de la production, de la qualité et de la maintenance. Le second objectif de cet article est de montrer que les activités de maintenance et de contrôle de la qualité à un certain niveau de la ligne de production contribuent aussi à l’amélioration de la fiabilité des machines en aval.----------ABSTRACT : Production, quality and maintenance control are the three main functions of operations management in manufacturing plants. Traditionally, they have been treated by scientists and practitioners as separate problems even though they are strongly interrelated. In the past three decades, the integration of production, maintenance and quality control has attracted much attention in the literature. This trend is motivated by the fact that integrated control policies generally result in better manufacturing performance and significant cost savings. Nevertheless, most of the existing integrated models in the literature integrate only two functions at a time. Moreover, for simplicity, almost all of the integrated models are based on several simplifying assumptions that may make them unrealistic. For example, the complex dynamics of quality and reliability degradations such as the impact of operations speed on degradation intensity and the correlation between quality and reliability degradations have been always overlooked in the literature of integrated models. On the other hand, the quality control policies used in the existing integrated models are either 100% inspection of all parts produced or statistical process control tools such as the control charts. However, acceptance sampling which constitutes an important branch of the Statistical Quality Control has never been integrated with production and maintenance policies. Acceptance sampling plans and procedures have been widely used in industry for a long time to reduce the cost and time of quality inspection and to statistically control the outgoing quality. This research considers the problem of the joint design of production, quality and maintenance control policies of stochastic manufacturing systems. Specifically, the main objective of this thesis is to integrate sampling inspection techniques with production and maintenance control policies for systems subject to both quality and reliability degradations. We provide a practical modeling framework to adequately pattern the complex dynamics of degradation processes as in the real-life in order to develop new effective integrated control policies. Moreover, we investigate the intrinsic statistical properties of acceptance sampling plans in order to demonstrate how they can be coupled with condition-based maintenance to improve the overall performance of degrading manufacturing systems. This thesis is comprised of four journal articles. The first article investigates the joint production and quality control of a batch-processing production system which is unreliable and imperfect. A single acceptance sampling plan by attributes is used for quality control. The objective of this article is to introduce an integrated model for the joint optimization of the production lot size, the safety stock and the sampling plan parameters which minimize the total cost incurred. This aims to provide a better understanding of the interactions between the optimal production-inventory settings and the optimal sampling plan parameters. As an extension of this model, the second article considers that quality and reliability degradations are operation-dependent. Moreover, a preventive maintenance strategy is incorporated into the integrated control policy. Thus, the objective is to jointly optimize the production, quality and maintenance control parameters. This article investigates the statistical characteristics of the single sampling plan to show the relevance of quality information resulting from such a quality control to the maintenance decision-making. Also, a comparative study is conducted to quantify the economic savings that can be realized by using the sampling plans for degrading systems rather than 100% inspection. The third article addresses the joint economic design of production control, type-1 continuous sampling plan (CSP-1) and preventive maintenance of continuous-flow manufacturing systems. The objective is to show how integrated control policies can extend the application of continuous sampling plans to degrading production systems, as they are presently limited only to stable processes. In this article, three quality control policies are considered and compared: 100% inspection, the classical CSP-1 as in the standard procedures and a CSP-1 plan with a stopping rule that is coupled with condition-based maintenance. This aims to quantify the economic savings that can be achieved by using the CSP-1 compared to 100% inspection and to demonstrate how CSP-1 with an inspection stopping rule for degrading processes is more cost-effective than the classical CSP-1. Finally, the fourth article investigates the joint design of production, quality and maintenance control policies for manufacturing lines. We consider a small production line composed of two machines subject to quality and reliability degradations. The second machine is also subject to failures caused by defective products manufactured in upstream processes. The main objective of this article is to study the interactions between the optimal production, quality and maintenance control settings and the effect of failures correlation on those settings. Also, we show how maintenance and quality control activities in preceding stages can play an important role in the reliability improvement of the subsequent machines

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Sviluppo di modelli decisionali per la supply chain di prodotti deperibili mediante l’applicazione di tecnologie innovative

The supply chain of perishable products, as fruits and vegetables is affected by environmental abuses from harvest to the final destination which are responsible the uncontrolled deterioration of food. In order to reduce such phenomena the supply chain members should control and monitor the conditions of goods in order to ensure their quality for consumers and to comply with all legal requirements (Garcia Ruiz, 2008). The most important factor influencing the food quality is the temperature able to prolonging the shelf life of the products. Since the temperature can inhibit or promote the maturation and deterioration process, this parameter is involved both in the growing process of fruits and vegetables and in the transport and storage stages. Given this the aim of the present thesis is to show that the monitoring of such parameter during the pre and post harvest stages allows to improve the decision making process. In the context of temperature monitoring the introduction of emerging information technologies such as the Wireless Sensors Networks and the Radio Frequency Identification can now provide real-time status knowing of product managed. The real time monitoring can be of great help in the definition of the actual maturation level of products both in the field and during the cold chain. The suitability of such an approach is evaluated by means of case studies. The first case study concerns the monitoring of grapes growth directly in the vineyard. The suitability of Wireless Sensors Networks in the monitoring of the grapes growth process is evaluated in terms of the possibility to determine the date of starting or ending of phenological phases. This information allows to make faster decisions about the vineyard operations which must be performed during the grape growth and finally allows to predict the maturation date in order to optimize the harvest operations. In the next case study the possibility to apply the Radio Frequency Identification technology to the monitoring of the fresh fruits along the cold chain has been faced and the quality of the products at any stage of the supply chain has been determined through a mathematical model. The knowing of the current quality level allows to make decisions about the destination of products. In this case those products having a shorter shelf life can be distributed to a local market while those with longer shelf life can be distributed to more distant location. In the next case study the information about the current deterioration state of perishable products has been translated into a warehouse management system in order to determine the operational parameters able to optimize the quality of products stored. Even in this case the goal of the study was to provide a decision making tool for the proper management of the perishable products stored. However besides the advantages achievable by the real time evaluation of environmental conditions the costs involved with the implementation of innovative technologies must be determined in order to establish the suitability of the investment in such innovative technologies. The present thesis also faces this question by determining the optimal number of devices to apply to the stock keeping unit in order to minimize the total cost associated to the transferring batch from the producer to the distributor. In this case the methodology employed is that of a mathematical model including all costs associated to the product management. Finally the study conducted through the present thesis shows that in all of the cases treated the use of the innovative technologies allows to support the decision making process in the pre and post harvest phases thus improving the perishables management

Some contributions to maintenance and accelerated degradation test under complex failure process

Ph.DDOCTOR OF PHILOSOPH

Optimal control of production and distribution in a supply chain system operating under a JIT delivery policy

This research deals with a supply chain system where the production or manufacturing facility operates under a just-in-time (JIT) environment, and the facility consists of raw material suppliers, manufacturers, and retailers where inventory of raw materials, work-in-process, and finished goods are involved, respectively. This work considers that the production of finished goods in one cycle starts just after the production or uptime in preceding cycle to minimize the idle time of the facility. Considering this scenario, inventory models are developed for different delivery situations: (a) perfect matching condition where no finished good remains after the shipments and (b) imperfect matching condition where some finished goods remain after the shipments. In this research, the problems are categorized as integer and mixed integer non-linear programming problems which are solved to find optimum number of orders and shipments, optimum production quantity, and minimum system cost. Moreover, multi-supplier and multi-buyer operations, where raw materials are ordered from different suppliers and finished goods are delivered to different customers, are considered. In addition to these problems, a single facility lot-sizing model is applied in perfect and imperfect matching cases, and, multi-supplier and multi-buyer case, to concentrate on more practical supply chain environments. All the problems described in this research are non-convex functions for which the closed form solutions are cumbersome. Therefore, the heuristic solutions are developed to find the optimal lot-sizing techniques. Additionally, the multi-supplier and multi-buyer problem is solved with the help of integer approximation and the divide and conquer rule. The solutions are tested through numerical examples. Furthermore, the sensitivity analyses are performed to observe the variations of the different cost functions. Also, this research proposes an alternate delivery schedule of finished product supply, for which both manufacturers and buyers will be benefited economically. The production and supply chain management play a significant role for the necessary amounts of materials and parts arrive at the proper time and place. With the models obtained in this research, managers can quickly respond to consumers\u27 demand by determining the right policies to order raw materials, to manage their production schedule efficiently and to deliver finished goods just-in-time