A Periodicity Metric for Assessing Maintenance Strategies

Organised by: Cranfield UniversityThe maintenance policy in manufacturing systems is devised to reset the machines functionality in an economical fashion in order to keep the products quality within acceptable levels. Therefore, there is a need for a metric to evaluate and quantify function resetting due to the adopted maintenance policy. A novel metric for measuring the functional periodicity has been developed using the complexity theory. It is based on the rate and extent of function resetting. It can be used as an important criterion for comparing the different maintenance policy alternatives. An industrial example is used to illustrate the application of the new metric.Mori Seiki – The Machine Tool Company; BAE Systems; S4T – Support Service Solutions: Strategy and Transitio

Firm productivity, profit and business goal satisfaction: an assessment of maintenance decision effects on small and medium scale enterprises (SME’s)

[EN] This study was carried out to identify which factors are most relevant to managers of SMEs in maintenance decision making, and to investigate how these factors influence the realization of business goals satisfactorily, using structural equation modelling, partial least square design (PLS-SEM) to establish significant relationships between manifest and latent variables. A study of maintenance cost vis a vis the number of maintenance works carried out and profits realized was conducted to ascertain correlations and identify which factors played key roles in profit maximization. Results showed that with increasing level of maintenance for SMEs, profit margins reduced significantly. Also, an R2 value of 0.83 showed that the latent variable, business goal satisfaction was explained to a high degree (83%) by the manifest variables. Rentals of equipment from third parties (0.27), halting production (0.11) and outsourcing (0.39) were less considered for business sustainability per correlation coefficients than funds (0.79), and the possibilities to carry out both corrective (0.64) and preventive (0.58) maintenance works.  F-square value greater than zero was realized (0.387) and this showed reliability of the both inner and outer models. These findings can be used in building a decision tool or framework that will best suit SMEs with high financial budget constraints.Owusu-Mensah, D.; Quaye, EK.; Brako, L. (2021). Firm productivity, profit and business goal satisfaction: an assessment of maintenance decision effects on small and medium scale enterprises (SME’s). Journal of Applied Research in Technology & Engineering. 2(1):23-31. https://doi.org/10.4995/jarte.2021.14615OJS233121Al-Tabbaa, O., Ankrah, S. (2016). 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Reliability Engineering & System Safety, 91(7), 839-848. https://doi.org/10.1016/j.ress.2005.08.006Hair, Jr, Joseph, F., Tomas, G., Hult, M., Ringle, C., Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.Jiang, R., Murthy, D.N.P. (2008). Maintenance: Decision Models for Management. Science press, Beijing, China.Joo, S-J. (2009). Scheduling preventive maintenance for modular designed components: A dynamic approach. European Journal of Operational Research, 192(2), 512-520. https://doi.org/10.1016/j.ejor.2007.09.033Lee, H. (2005). A cost/benefit model for investments in inventory and preventive maintenance in an imperfect production system. Computers and Industrial Engineering, 48(1), 55-68. https://doi.org/10.1016/j.cie.2004.07.008Liu, X., Wang, W., Peng, R. (2015). An integrated production: inventory and preventive maintenance model for a multiproduct production system. Reliab Eng Syst Safety, 137(2), 76-86. https://doi.org/10.1016/j.ress.2015.01.002Liu, X., Zheng, J., Fu, J., Ji, J., Chen, G. (2017). Multi-level optimization of maintenance plan for natural gas pipeline systems subject to external corrosion. Journal of Natural Gas Science and Engineering, 50, 64-73. https://doi.org/10.1016/j.jngse.2017.11.021Ma, J., Cheng, L., Li, D. (2018). Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network. Journal of Advanced Transportation. Article ID 4539324, 11 pages. https://doi.org/10.1155/2018/4539324Marquez, A.C., Gupta, J.N.D. (2006). Contemporary maintenance management: process, framework and supporting pillars. Omega, 34(3), 313-326. https://doi.org/10.1016/j.omega.2004.11.003Nourelfath, M., Nahas, N. & Ben-Daya, M. (2015). Integrated preventive maintenance and production decisions for imperfect processes. Reliab Eng Syst Safety, 148, 21-31. https://doi.org/10.1016/j.ress.2015.11.015Olivotti D., Passlick J., Dreyer S., Lebek B., Breitner M.H. (2018) Maintenance Planning Using Condition Monitoring Data. In: Kliewer N., Ehmke J., Borndörfer R.(eds) Operations Research Proceedings 2017. https://doi.org/10.1007/978-3-319-89920-6_72Pallant, J. (2007). SPSS survival manual, 3rd. Edition. McGrath Hill.Parida, A., Kumar, U. (2016). Applications and Case Studies. Maintenance performance measurement (MPM): issues and challenges. Journal of Quality in Maintenance Engineering, 12(3), 239-251. https://doi.org/10.1108/13552510610685084Qiu, Q., Cui, L., Shen, J., Yang, L. (2017). Optimal maintenance policy considering maintenance errors for systems operating under performance-based contracts. Comput Industr Eng., 112, 147-155. https://doi.org/10.1016/j.cie.2017.08.025Ruschel, E., Santos, E.A.P. & Loures, E.D.F.R. (2017). Industrial maintenance decision-making: a systematic literature review. J Manuf Syst., 45, 180-194. https://doi.org/10.1016/j.jmsy.2017.09.003Shayesteh, E., Yu, J., Hilber, P. (2018). Maintenance optimization of power systems with renewable energy sources integrated. Energy, 149, 577-586. https://doi.org/10.1016/j.energy.2018.02.066Shen, J., Zhu, K. (2017). An uncertain single machine scheduling problem with periodic maintenance. Knowledge-Based Systems, 144, 32-41. https://doi.org/10.1016/j.knosys.2017.12.021Stebbins, R. A. (2001). Exploratory research in the social sciences (Vol. 48). Sage.Van, P.D., Bérenguer, C. (2012). Condition-based maintenance with imperfect preventive repairs for a deteriorating production system. Qual Reliab Eng., 28(6), 624-633. https://doi.org/10.1002/qre.1431Verbert, K., Schutter, B.D., Babuska, R. (2017). Timely condition-based maintenance planning for multi-component systems. Reliab Eng Syst Safety, 159, 310-321. https://doi.org/10.1016/j.ress.2016.10.032Yang, L., Ma, X., Zhao, Y. (2017). A condition-based maintenance model for a three-state system subject to degradation and environmental shocks. Comput Industr Eng., 105, 210-222. https://doi.org/10.1016/j.cie.2017.01.01

Imperfect Maintenance Models, from Theory to Practice

The role of maintenance in the industrial environment changed a lot in recent years, and today, it is a key function for long-term profitability in an organization. Many contributions were recently written by researchers on this topic. A lot of models were proposed to optimize maintenance activities while ensuring availability and high-quality requirements. In addition to the well-known classification of maintenance activities—preventive and corrective—in the last decades, a new classification emerged in the literature regarding the degree of system restoration after maintenance actions. Among them, the imperfect maintenance is one of the most studied maintenance types: it is defined as an action after which the system lies in a state somewhere between an “as good as new” state and its pre-maintenance condition “as bad as old.” Most of the industrial companies usually operate with imperfect maintenance actions, even if the awareness in actual industrial context is limited. On the practical definition side, in particular, there are some real situations of imperfect maintenance: three main specific cases were identified, both from literature analysis and from experience. Considering these three implementations of imperfect maintenance actions and the main models proposed in the literature, we illustrate how to identify the most suitable model for each real case

A unified methodology of maintenance management for repairable systems based on optimal stopping theory

This dissertation focuses on the study of maintenance management for repairable systems based on optimal stopping theory. From reliability engineering’s point of view, all systems are subject to deterioration with age and usage. System deterioration can take various forms, including wear, fatigue, fracture, cracking, breaking, corrosion, erosion and instability, any of which may ultimately cause the system to fail to perform its required function. Consequently, controlling system deterioration through maintenance and thus controlling the risk of system failure becomes beneficial or even necessary. Decision makers constantly face two fundamental problems with respect to system maintenance. One is whether or when preventive maintenance should be performed in order to avoid costly failures. The other problem is how to make the choice among different maintenance actions in response to a system failure. The whole purpose of maintenance management is to keep the system in good working condition at a reasonably low cost, thus the tradeoff between cost and condition plays a central role in the study of maintenance management, which demands rigorous optimization. The agenda of this research is to develop a unified methodology for modeling and optimization of maintenance systems. A general modeling framework with six classifying criteria is to be developed to formulate and analyze a wide range of maintenance systems which include many existing models in the literature. A unified optimization procedure is developed based on optimal stopping, semi-martingale, and lambda-maximization techniques to solve these models contained in the framework. A comprehensive model is proposed and solved in this general framework using the developed procedure which incorporates many other models as special cases. Policy comparison and policy optimality are studied to offer further insights. Along the theoretical development, numerical examples are provided to illustrate the applicability of the methodology. The main contribution of this research is that the unified modeling framework and systematic optimization procedure structurize the pool of models and policies, weed out non-optimal policies, and establish a theoretical foundation for further development

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

Joint Determination of Preventive Maintenance and Buffer Stock for a Production Unit under Lease

Purpose: The purpose of this work is to develop a mathematical model for simultaneously determining the optimal period of preventive maintenance actions and the optimal size of buffer stock for a production unit that is owned by a lessor and leased to a lessee under a lease contract. Design/methodology/approach: A mathematical model is formulated and a numerical procedure is developed for finding the optimal period of preventive maintenance actions and the optimal size of buffer stock to minimize the total expected costs considering both a lessor and a lessee over a lease period. Findings: The proposed model gives better solutions than those where the maintenance cost to the lessor and the production inventory cost to the lessee are minimized separately. Originality/value: The joint determination of preventive maintenance and safety stock is a topic that has been extensively studied for decades. The majority of the models reported in the literature implicitly assume that the firm owns the production unit and maintenance actions are done in-house. However, equipment acquisition through leasing is a common practice nowadays. Normally, under a lease contract, the lessor who owns the equipment is responsible for maintenance services. This may lead to a conflict between the lessor and the lessee concerning the optimal choice of maintenance actions. To solve this conflict, we propose a joint determination of preventive maintenance and safety stock model for a production unit under a lease. The objective of our model is to simultaneously determine the optimal period of preventive maintenance actions that the lessor needs to perform and the optimal size of buffer stock the lessee needs to produce so that the total combined expected costs to both parties over the lease period are minimizedPeer Reviewe

An study of cost effective maintenance policies: Age replacement versus replacement after N minimal repairs

In this paper we consider the inspection and maintenance of a system under two types of age-dependent failures, revealed minor failures (R) and unrevealed catastrophic failures (U). Periodic inspections every T units of time are carried out to detect U failures, leading to the system replacement when one is discovered. R failures are followed by a minor repair. In addition the system is preventively replaced at MT or after the Nth R failure whichever comes first. The costs of minimal repair and replacement after N minor failures depend on age and history of failures. Non-perfect inspections are assumed, providing false positives when no U failure has happened or false negatives when a U failure is present. The long-run cost per unit of time along with the optimum policy (T*, M*, N*) are obtained. We explore conditions under which both strategies of preventive maintenance are profitable, comparing with suboptimal policies when only one of them is performed. Maintenance of infrastructures illustrates the model conditions

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

Optimal maintenance of multi-component systems: a review

In this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al. (1996), which focusses on economic dependence, and the survey of maintenance policies by Wang (2002), in which some group maintenance and some opportunistic maintenance policies are considered. Our classification scheme is primarily based on the dependence between components (stochastic, structural or economic). Next, we also classify the papers on the basis of the planning aspect (short-term vs long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy classes or exact algorithms). Finally, we pay attention to the applications of the models.literature review;economic dependence;failure interaction;maintenance policies;grouping maintenance;multi-component systems;opportunistic maintenance;maintencance optimization;structural dependence