Optimisation de la politique de maintenance pour un système à dégradation graduelle stressé

International audienceThis paper investigates a maintenance policy allowing the maintenance cost optimization per unit of time combining statistical process control (SPC) and condition-based maintenance (CBM) policy. We consider a single-unit system with two failure modes which can be partially explained by several covariates. Failure modes are a continuous-state deterioration and a stress. A CBM policy is used for inspecting and replacing the system in order to balance the impacts of an excessive deterioration level whereas a control a classical control chart is used to monitor the stress covariate. Sensitivity analysis based on numerical results is proposed

Optimisation de la politique de maintenance pour un système à dégradation graduelle stressé

International audienceThis paper investigates a maintenance policy allowing the maintenance cost optimization per unit of time combining statistical process control (SPC) and condition-based maintenance (CBM) policy. We consider a single-unit system with two failure modes which can be partially explained by several covariates. Failure modes are a continuous-state deterioration and a stress. A CBM policy is used for inspecting and replacing the system in order to balance the impacts of an excessive deterioration level whereas a control a classical control chart is used to monitor the stress covariate. Sensitivity analysis based on numerical results is proposed

Optimizing the Cost of Integrated Model for Fuzzy Failure Weibull Distribution Using Genetic Algorithm

This research applies the fuzzy concept in development of an integrated model (Statistical process control and Maintenance management) with Weibull distribution for Exponentially Weighted Moving Average (EWMA) control chart. Since sample data may contain uncertainties coming from measurement systems and environment conditions, fuzzy number is used to inspect these suspicions. Moreover, the Weibull distribution with fuzzy scale parameters is considers, and the genetic algorithm approach is used to determine the optimal values of six variables that minimize the fuzzy hourly cost. Finally, the fuzzy hourly cost is transformed to crisp number by the centroid defuzzification.This research applies the fuzzy concept in development of an integrated model (Statistical process control and Maintenance management) with Weibull distribution for Exponentially Weighted Moving Average (EWMA) control chart. Since sample data may contain uncertainties coming from measurement systems and environment conditions, fuzzy number is used to inspect these suspicions. Moreover, the Weibull distribution with fuzzy scale parameters is considers, and the genetic algorithm approach is used to determine the optimal values of six variables that minimize the fuzzy hourly cost. Finally, the fuzzy hourly cost is transformed to crisp number by the centroid defuzzification

From process logic to business logic - A cognitive approach to business process management

The unpredictability of business activities means that business process management should provide a way to adapt to change. The traditional workflow approach, based on predefined process logic, offers little support for today's complex and dynamic business environment. Therefore, a cognitive approach is proposed to help manage complex business activities, based on continuous awareness of situations and real-time decisions on activities. In this approach, the business environment is seen as capturing events that occurred and the state of tasks and resources; business logic involving process routing, operational constraints, exception handling and business strategy is used to determine which actions are appropriate for the current situation. By extending process management from process logic to business logic, the methodology offers flexibility, agility and adaptability in complex business process management. © 2005 Elsevier B.V. All rights reserved.postprin

A Model for Maintenance Planning and Process Quality Control Optimization Based on EWMA and CUSUM Control Charts

The performance of a production system is highly dependent on the smooth operation of various equipment and processes. Thus, reducing failures of the equipment and processes in a cost-effective manner improves overall performance; this is often achieved by carrying out maintenance and quality control policies. In this study, an integrated optimization method that addresses both maintenance strategies and quality control practices is proposed using an exponentially weighted moving average (EWMA) chart, in which both corrective and preventive maintenance policies are considered. The integrated model has been proposed to find optimal decision variables of both the process quality decision parameters and the optimal interval of preventive maintenance (i.e., Ns, Hs, L, λ, and t_PM) to result in overall optimal expected hourly total system costs. A case study is then utilized to investigate the impact of cost criteria on the proposed integrated model and to compare the proposed model with a model using the cumulative sum (CUSUM) control chart. The improved model outputs indicate that there is a reduction of 34.6% in the total expected costs compared with those of the other model using the CUSUM chart. Finally, an analysis of sensitivity to present the effectiveness of the model parameters and the main variables in the overall costs of the system is provided

Estimation of the Optimal Statistical Quality Control Sampling Time Intervals Using a Residual Risk Measure

Background: An open problem in clinical chemistry is the estimation of the optimal sampling time intervals for the application of statistical quality control (QC) procedures that are based on the measurement of control materials. This is a probabilistic risk assessment problem that requires reliability analysis of the analytical system, and the estimation of the risk caused by the measurement error. Methodology/Principal Findings: Assuming that the states of the analytical system are the reliability state, the maintenance state, the critical-failure modes and their combinations, we can define risk functions based on the mean time of the states, their measurement error and the medically acceptable measurement error. Consequently, a residual risk measure rr can be defined for each sampling time interval. The rr depends on the state probability vectors of the analytical system, the state transition probability matrices before and after each application of the QC procedure and the state mean time matrices. As optimal sampling time intervals can be defined those minimizing a QC related cost measure while the rr is acceptable. I developed an algorithm that estimates the rr for any QC sampling time interval of a QC procedure applied to analytical systems with an arbitrary number of critical-failure modes, assuming any failure time and measurement error probability density function for each mode. Furthermore, given the acceptable rr, it can estimate the optimal QC sampling time intervals

Integration of production, maintenance and quality : Modelling and solution approaches

Dans cette thèse, nous analysons le problème de l'intégration de la planification de production et de la maintenance préventive, ainsi que l'élaboration du système de contrôle de la qualité. Premièrement, on considère un système de production composé d'une machine et de plusieurs produits dans un contexte incertain, dont les prix et le coût changent d'une période à l'autre. La machine se détériore avec le temps et sa probabilité de défaillance, ainsi que le risque de passage à un état hors contrôle augmentent. Le taux de défaillance dans un état dégradé est plus élevé et donc, des coûts liés à la qualité s’imposent. Lorsque la machine tombe en panne, une maintenance corrective ou une réparation minimale seront initiées pour la remettre en marche sans influer ses conditions ou le processus de détérioration. L'augmentation du nombre de défaillances de la machine se traduit par un temps d'arrêt supérieur et un taux de disponibilité inférieur. D'autre part, la réalisation des plans de production est fortement influencée par la disponibilité et la fiabilité de la machine. Les interactions entre la planification de la maintenance et celle de la production sont incorporées dans notre modèle mathématique. Dans la première étape, l'effet de maintenance sur la qualité est pris en compte. La maintenance préventive est considérée comme imparfaite. La condition de la machine est définie par l’âge actuel, et la machine dispose de plusieurs niveaux de maintenance avec des caractéristiques différentes (coûts, délais d'exécution et impacts sur les conditions du système). La détermination des niveaux de maintenance préventive optimaux conduit à un problème d’optimisation difficile. Un modèle de maximisation du profit est développé, dans lequel la vente des produits conformes et non conformes, les coûts de la production, les stocks tenus, la rupture de stock, la configuration de la machine, la maintenance préventive et corrective, le remplacement de la machine et le coût de la qualité sont considérés dans la fonction de l’objectif. De plus, un système composé de plusieurs machines est étudié. Dans cette extension, les nombres optimaux d’inspections est également considéré. La fonction de l’objectif consiste à minimiser le coût total qui est la somme des coûts liés à la maintenance, la production et la qualité. Ensuite, en tenant compte de la complexité des modèles préposés, nous développons des méthodes de résolution efficaces qui sont fondées sur la combinaison d'algorithmes génétiques avec des méthodes de recherches locales. On présente un algorithme mimétique qui emploi l’algorithme Nelder-Mead, avec un logiciel d'optimisation pour déterminer les valeurs exactes de plusieurs variables de décisions à chaque évaluation. La méthode de résolution proposée est comparée, en termes de temps d’exécution et de qualités des solutions, avec plusieurs méthodes Métaheuristiques. Mots-clés : Planification de la production, Maintenance préventive imparfaite, Inspection, Qualité, Modèles intégrés, MétaheuristiquesIn this thesis, we study the integrated planning of production, maintenance, and quality in multi-product, multi-period imperfect systems. First, we consider a production system composed of one machine and several products in a time-varying context. The machine deteriorates with time and so, the probability of machine failure, or the risk of a shift to an out-of-control state, increases. The defective rate in the shifted state is higher and so, quality related costs will be imposed. When the machine fails, a corrective maintenance or a minimal repair will be initiated to bring the machine in operation without influencing on its conditions or on the deterioration process. Increasing the expected number of machine failures results in a higher downtime and a lower availability rate. On the other hand, realization of the production plans is significantly influenced by the machine availability and reliability. The interactions between maintenance scheduling and production planning are incorporated in the mathematical model. In the first step, the impact of maintenance on the expected quality level is addressed. The maintenance is also imperfect and the machine conditions after maintenance can be anywhere between as-good-as-new and as-bad-as-old situations. Machine conditions are stated by its effective age, and the machine has several maintenance levels with different costs, execution times, and impacts on the system conditions. High level maintenances on the one hand have greater influences on the improvement of the system state and on the other hand, they occupy more the available production time. The optimal determination of such preventive maintenance levels to be performed at each maintenance intrusion is a challenging problem. A profit maximization model is developed, where the sale of conforming and non-conforming products, costs of production, inventory holding, backorder, setup, preventive and corrective maintenance, machine replacement, and the quality cost are addressed in the objective function. Then, a system with multiple machines is taken into account. In this extension, the number of quality inspections is involved in the joint model. The objective function minimizes the total cost which is the sum of maintenance, production and quality costs. In order to reduce the gap between the theory and the application of joint models, and taking into account the complexity of the integrated problems, we have developed an efficient solution method that is based on the combination of genetic algorithms with local search and problem specific methods. The proposed memetic algorithm employs Nelder-Mead algorithm along with an optimization package for exact determination of the values of several decision variables in each chromosome evolution. The method extracts not only the positive knowledge in good solutions, but also the negative knowledge in poor individuals to determine the algorithm transitions. The method is compared in terms of the solution time and quality to several heuristic methods. Keywords : Multi-period production planning, Imperfect preventive maintenance, Inspection, Quality, Integrated model, Metaheuristic

PengaruhKompetensi dan Jumlah Kru Perawatan Terhadap Biaya Perawatan Kapal Penangkap Ikan dengan Pemodelan Dinamika Sistem

Pelaksanaan kegiatan perawatan, baik perawatan preventif maupun reaktif, tidak terlepas dari peran kru. Kompetensi dari seorang kru dapat diketahui dari pendidikan dan pelatihan yang pernah diterima, keahlian dan pengalaman. Metode dinamika sistem merupakan metode pemodelan yang menggunakan hubungan sebab-akibat dalam menyusun model suatu sistem yang kompleks. Penggunaan metode ini lebih ditekankan tentang bagaimana tingkah laku sistem. Kaitannya dengan crew skill dan jumlah crew terhadap perawatan pada kapal latih karena faktor human memiliki sifat yang dinamis, dengan pengertian kondisinya selalu berubah terhadap waktu. Tujuan dari penelitian ini adalah untuk memodelkan kompetensi dan jumlah kru perawatan terhadap terhadap biaya perawatan kapal penangkap ikan dengan menggunakan pendekatan dinamika system serta merekomendasikan pilihan antara kompetensi kru dan jumlah kru perawatan yang paling efektif untuk operasional kapal penangkap ikan sehingga menghasilkan biaya perawatan yang minimum. Metode yang digunakan dalam penelitian ini menggunakan pemodelan dinamika sistem. Hasil simulasi menunjukkan pada tingkat keandalan dipertahankan pada nilai minimum 0.6, biaya yang optimum untuk perawatan adalah meningkatkan nilai kompetensi kru hingga level 120% tanpa meningkatkan jumlah kru. Pada nilai minimum reliability 0.7 untuk mencapai total biaya minimum dengan meningkatkan kompetensi kru hingga level 130% dan jumlah kru hingga 2 orang. Selanjutnya untuk nilai minimum reliability 0.8 untuk mencapai total biaya minimum dengan meningkatkan kompetensi kru hingga level 130% dan meningkatkan jumlah kru hingga 2 orang. ============================================================================================================ Implementation of maintenance activities, both preventive and corrective maintenance, is inseparable from the role of the crew. Crew competence can be observed from the level of education and training, expertise and experience. This research utilized system dynamics modeling using causal relationships in developing the model of a complex system to understand the behavior of the system reliability when there is a change in either crew competence or crew size for fishing vessel. The objective of this study is to model the competence and crew size on the fishing vessel and observe its impact on maintenance cost by using system dynamics approach. By using such a model, we could recommend the most effective design of crew skills and crew size for the operation of fishing vessels which minimizes the maintenance cost. This research uses a system dynamics modeling to solve the problem. The simulation results show when the level of reliability is set to minimum value 0.6, the optimum cost for maintenance is obtained by increasing the level of crew skills to 120% without increasing crew size. When the minimum value of reliability is set to 0.7, the optimum cost for maintenance is obtained when we improve the competence of crew level to 130% with change crew size to 2 crews. And for the minimum level of reliability is set to 0.8, the optimum cost for maintenance is gained when we increasing crew skill level to 130% and increase the crew size up to 2 crews