Availability optimisation for stochastic degrading systems under imperfect preventive maintenance

This paper deals with imperfect preventive maintenance (PM) optimisation problem. The system to be maintained is typically a production system assumed to be continuously monitored and subject to stochastic degradation. To assess such degradation, the proposed maintenance model takes into account both corrective maintenance (CM) and PM. The system undergoes PM whenever its reliability reaches an appropriate value, while CM is performed at system failure. After a given number of maintenance actions, the system is preventively replaced by a new one. Both CM as well as PM are considered imperfect, i.e. they bring the system to an operating state which lies between two extreme states, namely the as bad as old state and as good as new state. The imperfect effect of CM and PM is modelled on the basis of the hybrid hazard rate model. The objective of the proposed PM optimisation model consists on finding the optimal reliability threshold together with the optimal number of PM actions to maximise the average availability of the system. A mathematical model is then proposed. To solve this problem an algorithm is provided. A numerical example is presented to illustrate the proposed maintenance optimisation model.     

Joint production,setup and preventive maintenance policies of unreliable two-product manufacturing systems

This article addresses the problem of joint optimisation of production, setup and maintenance activities of unreliable manufacturing system producing two products. Given the complexity of the problem in a dynamic and stochastic environment, the literature has treated the problem separately by considering each axis individually (setup, production and maintenance) or by combining two axes simultaneously (production-setup, production-maintenance). Following the trend of scientific research advances that supports the fact that an integrated control leads to best performances, the main objective of this paper is to provide a control policy that will simultaneously combine the production, the setup and the preventive maintenance activities. To tackle the problem, an experimental resolution approach using combined continuous/discrete event simulation models is considered. The aim is to accurately imitate the production system behaviour, and to optimise the control policy parameters which minimise the total cost incurred. An in-depth study of the effects of the system parameter variation on the performance of the studied policies is performed in order to draw meaningful conclusions and to illustrate the robustness of the proposed resolution approach.     

Joint optimisation of maintenance and production policies considering random demand and variable production rate

This paper deals with a randomly failing manufacturing system M1 which has to satisfy a random demand during a finite horizon given a required service level. To help meet this demand, subcontracting is used through another production system M2. M1 operates with a variable production rate and its failure rate depends on both time and the production rate. In these conditions, as a first step, we establish a preliminary production plan corresponding to a given service level. In a second stage, we integrate the effect of the machine degradation introducing a unitary degradation cost. The optimal production plan is then obtained by minimising the sum of the production, the inventory and the degradation costs. In the final stage, we propose another optimal plan combined with a preventive maintenance policy aiming at reducing the machine degradation while minimising the total cost including the production, inventory and maintenance costs.     

Joint optimisation of production,maintenance and quality for batch production system subject to varying operational conditions

This paper considers the integration problem of production, maintenance and quality for a capacitated lot-sizing production system subject to deterioration. The effects of varying operational conditions from batch to batch on system reliability and product quality are modelled by proportional hazards models, resulting in non-monotonic failure rate and defect rate. After each batch production, imperfect preventive maintenance (PM) is determined to mitigate the system deterioration, and inspection is taken to sort nonconforming items in the finished goods. Once the cumulative number of nonconforming items exceeds a predetermined threshold, an overhaul is performed to renew the system. An integrated model for optimising production plan, PM plan and overhaul strategy is developed to minimise the total cost while satisfying all product demands. A genetic algorithm is proposed to solve the integrated model efficiently. Numerical results validate the rationality of the model with varying operational conditions consideration and its applicability in economic benefits.     

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.     

A variable neighbourhood search for integrated production and preventive maintenance planning in multi-state systems

This paper presents a variable neighbourhood search (VNS) to the integrated production and maintenance planning problem in multi-state systems. VNS is one of the most recent meta-heuristics used for problem solving in which a systematic change of neighbourhood within a local search is carried out. In the studied problem, production and maintenance decisions are co-ordinated, so that the total expected cost is minimised. We are given a set of products that must be produced in lots on a multi-state production system during a specified finite planning horizon. Planned preventive maintenance and unplanned corrective maintenance can be performed on each component of the multi-state system. The maintenance policy suggests cyclical preventive replacements of components, and a minimal repair on failed components. The objective is to determine an integrated lot-sizing and preventive maintenance strategy of the system that will minimise the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs, while satisfying the demand for all products over the entire horizon. We model the production system as a multi-state system with binary-state components. The formulated problem can be solved by comparing the results of several multi-product capacitated lot-sizing problems. The proposed VNS deals with the preventive maintenance selection task. Results on test instances show that the VNS method provides a competitive solution quality at economically computational expense in comparison with genetic algorithms.     

Investigating reliability improvement of second-hand production equipment considering warranty and preventive maintenance strategies

A cost model for optimal reliability improvement of warranted second-hand production equipment is developed. The second-hand production equipment of age x is subjected to an upgrade action of a certain level u before it is sold with a Free Repair Warranty. We look at determining the optimal upgrade level when not performing and when performing periodic preventive maintenance (PM) during the warranty period. Two different PM strategies are considered: (a) periodic PM actions having the same efficiency level; (b) periodic multi-phase PM actions with a maintenance efficiency level which varies according to the phase. The proposed model aims at helping the dealer to find the optimal upgrade level to perform before selling the second-hand equipment, and to assess whether performing PM actions during the warranty period, according to a specific maintenance strategy, is worthwhile in terms of cost reduction. Numerical experimentations considering each PM scenario are performed in order to investigate how each PM strategy impacts the improvement level to be performed and the associated total expected cost. The obtained results showed that the expected total cost incurred by the dealer is governed by a sensitive trade-off between the warranty servicing cost and the costs associated with the reliability improvement, and with the PM performed during the warranty period. It is also found that the proposed new periodic multi-phase PM policy with an increasing maintenance efficiency level yields lower upgrade levels, inducing lower costs for the dealer.     

Multi-objective imperfect preventive maintenance optimisation with NSGA-II

Maintenance optimisation is a multi-objective problem in nature, and it usually needs to achieve a trade-off among the conflicting objectives. In this study, a multi-objective maintenance optimisation (MOMO) model is proposed for electromechanical products, where both the soft failure and hard failure are considered, and minimal repair is performed accordingly. Imperfect preventive maintenance (IPM) is carried out during the preplanned periods, and modelled with a hybrid failure rate model and quasi-renewal coefficient. The initial IPM period and the total number of IPM periods are set as the decision variables, and a MOMO model is developed to optimise the availability and cost rate concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the model. A case study of wind turbine’s gearbox is provided. The results show that there are 30 optimal solutions in the MOMO’s Pareto frontier that can maximise the availability and minimise the cost rate simultaneously. Compared with the single-objective maintenance optimisation, it can provide more choices for maintenance decision, and better satisfy the resource constraints and the customer’s preference. The results of the sensitivity analysis show that the effect of age reduction factor on optimisation results is greater than that of failure rate increase factor.     

A reliability/availability approach to joint production and maintenance scheduling with multiple preventive maintenance services

In classical scheduling problems, it is often assumed that the machines are available during the whole planning horizon, while in realistic environments, machines need to be maintained and therefore may become unavailable within production periods. Hence, in this paper we suggest a joint production and maintenance scheduling (JPMS) with multiple preventive maintenance services, in which the reliability/availability approach is employed to model the maintenance aspects of a problem. To cope with the suggested JPMS, a mixed integer nonlinear programming model is developed and then a population-based variable neighbourhood search (PVNS) algorithm is devised for a solution method. In order to enhance the search diversification of basic variable neighbourhood search (VNS), our PVNS uses an epitome-based mechanism in each iteration to transform a group of initial individuals into a new solution, and then multiple trial solutions are generated in the shaking stage for a given solution. At the end of the local search stage, the best obtained solution by all of the trial solutions is recorded and the worst solution in population is replaced with this new solution. The evolution procedure is continued until a predefined number of iterations is violated. To validate the effectiveness and robustness of PVNS, an extensive computational study is implemented and the simulation results reveal that our PVNS performs better than traditional algorithms, especially in large size problems.     

A graphical approach for confidence limits of optimal preventive maintenance cycles

Facilities management (FM) is the management of infrastructure resources and services to support and sustain the operational strategy of an organization over time. Maintenance is often the business process that has not been optimized and is considered as a liability of business operations. Therefore, extensive studies have been done to determine the optimal replacement interval for irreparable parts of repairable systems where typically the time between failures is characterized by lifetime distribution in which the parameters are estimated from failure data. As a result, the optimal preventive maintenance (PM) interval computed is exposed to sampling risk as the repair cost and failure data used for estimation are typically highly censored due to issues related to data collection and unobserved failures. In this paper, we present a graphical approach to obtain the confidence interval for the optimal PM interval that resulted from sampling variations parameter estimates. The proposed methodology is applied in the context of FM as a strategy for opportunistic replacement and for the purpose of validating the cost components in maintenance. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

Environmental issue in an integrated production and maintenance control of unreliable manufacturing/remanufacturing systems

This paper addresses a joint production and maintenance problem under environmental constraints and reliability issues in a manufacturing/remanufacturing context. The manufacturing system is composed of one machine producing one type of product. The remanufacturing system, also composed of one machine, retrieves returned products from the market in order to refurbish them. The manufacturing and remanufacturing systems aim to satisfy random demands under a given service level. Moreover, the entire system generates harmful emissions. Exceeding carbon emission limits defined by authorities may risk sanctions. We aim to propose a compromise between ecologic and economic production and maintenance plan by calling on green subcontracting in order to satisfy the demand and avoid emission excess. Three models are proposed in this paper. These models tackle mainly the basic production problems and propose alternative equivalent solution schemas for future extensions. The robustness and usefulness of the proposals are illustrated with various examples and sensitivity analyses.     

Integrated reconfiguration and age-based preventive maintenance decision making

The use of manufacturing system reconfiguration in conjunction with maintenance operations has not been previously reported in the literature. This research attempts to incorporate reconfiguration into Preventive Maintenance (PM) actions for improved system performance in terms of reduced total cost. This paper presents an Integrated Reconfiguration and Age-Based Maintenance (IRABM) policy and applies it to a parallel-serial manufacturing system. The expected total cost of implementing the IRABM policy is estimated and minimized through a simulation-based heuristic optimization procedure. Using this method, it is possible to systematically identify the conditions under which the integration of reconfiguration into maintenance is cost effective. In addition, numerical examples demonstrate that the manufacturing system could have a higher probability of fulfilling production requirements at a lower cost under the IRABM policy compared to the conventional age-based PM policy. The influences of the input parameters associated with reconfiguration, production, and reliability on the performance of IRABM policy also are studied.     

Optimization of imperfect preventive maintenance for multi-state systems

The paper generalizes a preventive maintenance optimization problem to multi-state systems, which have a range of performance levels. Multi-state system reliability is defined as the ability to satisfy given demand. The reliability of system elements is characterized by their hazard functions. The possible preventive maintenance actions are characterized by their ability to affect the effective age of equipment. An algorithm is developed which obtains the sequence of maintenance actions providing system functioning with the desired level of reliability during its lifetime by minimum maintenance cost.To evaluate multi-state system reliability, a universal generating function technique is applied. A genetic algorithm (GA) is used as an optimization technique. Basic GA procedures adapted to the given problem are presented. Examples of the determination of optimal preventive maintenance plans are demonstrated.     

An integrated preventive maintenance and production planning model with sequence‐dependent setup costs and times

This paper considers the integration of preventive maintenance and tactical production planning in a multiproduct production system, where setup costs and times are sequence dependent. A set of products needs to be produced in lots during a finite planning horizon, where preventive maintenance is conducted periodically at the end of some production periods and corrective maintenance is always performed when there is a failure. The system downtime—as caused by maintenance and setup—affects the system's available production capacity. We use a sequence‐oriented method to search for the optimal setup sequence and develop some steps to prune the searching tree. Our objective is to find the optimal preventive maintenance interval, production lot size, and production sequence in every period, by minimizing the sum of maintenance, production, inventory, and setup costs within the planning horizon. Numerical examples are presented to illustrate our model.     

A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.     

Decision support for design of reconfigurable rotary machining systems for family part production

To remain competitive in currently unpredictable markets, the enterprises must adapt their manufacturing systems to frequent market changes and high product variety. Reconfigurable manufacturing systems (RMSs) promise to offer a rapid and cost-effective response to production fluctuations under the condition that their configuration is attentively studied and optimised. This paper presents a decision support tool for designing reconfigurable machining systems to be used for family part production. The objective is to elaborate a cost-effective solution for production of several part families. This design issue is modelled as a combinatorial optimisation problem. An illustrative example and computational experiments are discussed to reveal the application of the proposed methodology. Insight gained would be useful to the decision-makers managing the configuration of manufacturing systems for diversified products.     

Generalized non‐stationary preventive maintenance model for deteriorating repairable systems

This paper deals with a practical approach for the analysis and modelling of preventive maintenance (PM) data for repairable systems which have an increasing failure frequency and/or increasing severity. The concept and testing for the trend of severity of corrective work (CO) and PM are discussed. A framework for statistical analysis of interarrival times and downtimes due to CO/PM is proposed. A generalized non‐stationary model for scheduling PM to maximize availability is suggested. The effect of severity on scheduling PM activities is shown through sensitivity analysis. Copyright © 2002 John Wiley & Sons, Ltd.     

Dependability and performance stochastic modelling of a two-unit repairable production system with preventive maintenance

In this paper, a two-unit multistate repairable production system is considered in which preventive maintenance (PM) is implemented in order to improve its dependability and performance. A general model is provided for the production system using a semi-Markov process, for examining system’s limiting behaviour. Apart from combining redundancy with PM, we introduce scenarios like imperfect and failed maintenance which are usually met in real life production systems. For the proposed model, we calculate the availability, the mean time to failure and the total operational cost and we formulate optimisation problems settled with respect to the system’s inspection times. The main aim of our work is to determine the optimal inspection times and consequently the optimal PM policies to be adopted in order to optimise system’s dependability and performance.     

An event-based analysis of condition-based maintenance decision-making in multistage production systems

Condition-based maintenance (CBM) is becoming increasingly prevalent because of its capability to continuously track equipment health degradation and accurately predict unscheduled equipment failure. CBM helps to improve the business bottom line by preventing costly station failure. However, it is not uncommon that CBM needs to stop stations for maintenance during operation, which can severely impede the normal production. The objective of this paper is to develop a systematic method to predict the negative impact of CBM stoppage events on production in a multistage manufacturing system. The research helps to predict the real expense of applying CBM, which is the foundation to establish a comprehensive real-time CBM decision-making model. We start from the event-based analysis of system dynamics and develop a stochastic estimation method to predict the permanent production loss caused by a CBM stoppage event. The monotonicity property of permanent production loss is investigated. Simulation case studies are performed to illustrate the theoretical results and demonstrate their potential in facilitating CBM decision-making.