Joint production, quality control and maintenance policies subject to quality-dependant demand

This thesis is a strive to find a proper solution, using the stochastic optimal control means for an unreliable production system with product quality control and quality-dependent demand. The system consists of a single machine producing a single product type (M1P1) subject to breakdowns and random repairs and must satisfy a non-constant rate of customer demand, which response to the quality of parts received. Since the machine produces with a rate of noncompliant products, an inspection of the products is made to reduce the number of bad parts that would deliver to the customer. It is done continuously and consists of controlling a fraction of the production. Approved products are put back on the production line, while bad products are discarded. The intended objective of this study is to provide optimal quality control and production policy, which maximize the net revenue consisting of the gross revenue, the cost of inventory, the cost of shortage, the cost of the inspection, the cost of maintenance and the cost of no-quality parts. Main decision variables are the sampling rate of the quality control system as well as the threshold of finished product inventory. The demand function reacts to the average outgoing quality level (AOQ) of finished products. In the third chapter of this study, preventive maintenance and dynamic pricing policies are added up to the optimal policy, cited above. To achieve the optimal points of the policy, which maximize our net production revenue, a simulation approach is implemented as an experimental design and its results were used in response surface methodology. To implement the experiment design (simulation approach) which thoroughly reflects model considerations such as its continuous nature and the variety, first, a continuous variable for the probability of defectiveness was introduced, functioning with the age of machine up until its next breakdown maintenance. Second, so as to reflect the effect of quality control process that results in Average Outgoing Quality rather than simple defectiveness possibility, this function (AOQ) was built based on instant behavior of mentioned function above as its independent variable. Third, due to the use of prospect theory assumptions in building a demand function that responds to the level of client delivered defectiveness (AOQ), a responsive continuous function was created for the demand, reacting to the level of product quality by determining it's needed per time amount. Finally. To illustrate the machine’s manufacturing policy based on Hedging Point, finished product inventory variable was introduced in the experiment design. In a nutshell, we have a production system that has been designed in a way that by raising its age (At), leads to more possibility of defectiveness and less demand in time units. This manner continuous up until the next maintenance action of the system, which restores all factors to their initial conditions. By use of the simulation approach of optimization an experiment is designed and implemented to control decision variables of the policy and maximize the objective function of average net revenue (ANR). Decision variables are statistically and practically in the matter of consideration such as finished product inventory threshold (Z), the proportion of inspection (F) and PM thresholds (Mk or Pk)

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

Towards an Integrated Inventory Management Process

The most serious inventory management problems are related to poor inventory management – stock shortages or surpluses. Too much inventory reduces competitiveness, increases goods depreciation and ultimately natural losses. A very important aspect is the loss of sales due to a shortage of goods, although this is much less felt and therefore much less analyzed. The aim of the study is to identify the main problems of inventory management in distribution companies and provide possible solutions to these problems. The paper analyzes problematic aspects of inventory management processes, highlighting the advantages and disadvantages of the major applicable inventory management models used. A qualitative study (experts survey) was also conducted and an integrated inventory management model called “Min-Max” was developed to address problem questions of inventory management. The essence of this model is to integrate and present principles and methodologies for managing inventories to maintain optimal levels of goods in distribution companies

Inventory models for production systems with constant/linear demand, time value of money, and perishable/non-perishable items

This research considers inventory systems for economic production models where the objective is to find the optimal cycle time, which minimizes the total cost, and optimal amount of shortage if it is allowed. Several aspects such as time value of money, inflation, constant and linear demand rates, shortages, and deterioration are considered in developing different models. Closed formulas are obtained for the optimal policy in one model. For others, more complex models where closed formulas cannot be obtained, search techniques are used to find the optimal solution.;First, a deterministic inventory control problem is considered for determination of optimal production quantities for an item with constant demand rate, while considering the effect of time value of money. Closed formulas are obtained to calculate the optimal cycle time and corresponding production quantity for the model without shortage. However, search procedures are used to find the optimal cycle time and maximum amount of shortage allowed for the models where shortage is allowed.;In the next inventory control problem, a deterministic model for items with linear demand rate over time, for a finite planning horizon, while considering the effect of time value of money, is considered. Search techniques are developed to find the optimal cycle time for the models without shortage, and the optimal cycle time and maximum amount of shortage for the models where shortage is allowed. A proof of the existence of a unique optimal point for the cost function is presented for the model without shortage.;A deterministic inventory control problem is also considered for items with constant rate of demand and exponentially decaying inventory over an infinite planning horizon, while considering the effect of time value of money. Two different search techniques are developed to find the optimal cycle time for the models without shortage, and the optimal cycle time and maximum amount of shortage allowed for the models where shortage is allowed. A proof of the existence of a unique optimal point for the cost function is presented for the model without shortage

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

Integrated Systems Health Management as an Enabler for Condition Based Maintenance and Autonomic Logistics

Health monitoring systems have demonstrated the ability to detect potential failures in components and predict how long until a critical failure is likely to occur. Implementing these systems on fielded structures, aircraft, or other vehicles is often a struggle to prove cost savings or operational improvements beyond improved safety. A system architecture to identify how the health monitoring systems are integrated into fielded aircraft is developed to assess cost, operations, maintenance, and logistics trade-spaces. The efficiency of a health monitoring system is examined for impacts to the operation of a squadron of cargo aircraft revealing sensitivity to and tolerance for false alarms as a key factor in total system performance. The research focuses on the impacts of system-wide changes to several key metrics: materiel availability, materiel reliability, ownership cost, and mean downtime. Changes to theses system-wide variables include: diagnostic and prognostic error, false alarm sensitivity, supply methods and timing, maintenance manning, and maintenance repair window. Potential cost savings in maintenance and logistics processes are identified as well as increases in operational availability. The result of this research is the development of a tool to conduct trade-space analyses on the effects of health monitoring techniques on system performance and operations and maintenance costs

Optimising replenishment policy in an integrated supply chain with controllable lead time and backorders-lost sales mixture

This paper aims to optimize the inventory replenishment policy in an integrated supply chain consisting of a single supplier and a single buyer. The system under consideration has the features such as backorders-lost sales mixture, controllable lead time, stochastic demand, and stockout costs. The underlying problem has not been studied in the literature. We present a novel approach to formulate the optimization problem, which is able to satisfy the constraint on the number of admissible stockouts per time unit. To solve the optimization problem, we propose two algorithms: an exact algorithm and a heuristic algorithm. These two algorithms are developed based on some analytical properties that we established by analysing the cost function in relation to the decision variables. The heuristic algorithm employs an approximation technique based on an ad-hoc Taylor series expansion. Extensive numerical experiments are provided to demonstrate the effectiveness of the proposed algorithms

Stochastic Optimization Models for Perishable Products

For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation. This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products. This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the standard LS method. Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks