Journal articles: 'Simulation based preventive maintenance optimization'

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Ge, En Shun, Qing Min Li, and Ao Lin Huang. "Optimization of Condition-Based Maintenance Policy for Deteriorating System Based on Monte-Carlo Simulation." Advanced Materials Research 544 (June 2012): 44–48. http://dx.doi.org/10.4028/www.scientific.net/amr.544.44.

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Condition-based Maintenance (CBM) can not only efficiently improve the performance of deteriorating system but also guarantee the system operation safety. This paper assumes that the system state is periodically inspected, and a preventive maintenance is performed if the degradation level exceeds a threshold. The effect of maintenance is imperfect, which means that maintenance can restore the system state to somewhere between as good as new and as bad as old. The algorithm is presented to get the solution of long run cost based on Monte-Carlo simulation, and the joint optimization of inspection rate, the threshold value and the number of preventive maintenance activities is investigated for the minimization of long run cost rate. A case study is given to show the procedure of the maintenance model and simulation. Therefore, the correctness and rationality of the model are proved.

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Wang, Keren, and Dragan Djurdjanovic. "Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets." Machines 6, no. 4 (November 1, 2018): 55. http://dx.doi.org/10.3390/machines6040055.

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Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics of maintenance resources. The joint decision-making problem becomes particularly challenging if one considers multiple options for preventive maintenance operations and multiple delivery methods for the necessary spare parts. In this paper, we propose an integrated decision-making policy that jointly considers scheduling of preventive maintenance for geographically dispersed multi-part assets, managing inventories for spare parts being stocked in maintenance facilities, and choosing the proper delivery options for the spare part inventory flows. A discrete-event, simulation-based meta-heuristic was used to optimize the expected operating costs, which reward the availability of assets and penalizes the consumption of maintenance/logistic resources. The benefits of joint decision-making and the incorporation of multiple options for maintenance and logistic operations into the decision-making framework are illustrated through a series of simulations. Additionally, sensitivity studies were conducted through a design-of-experiment (DOE)-based analysis of simulation results. In summary, considerations of concurrent optimization of maintenance schedules and spare part logistic operations in an environment in which multiple maintenance and transpiration options are available are a major contribution of this paper. This large optimization problem was solved through a novel simulation-based meta-heuristic optimization, and the benefits of such a joint optimization are studied via a unique and novel DOE-based sensitivity analysis.

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Xie, Lubing, Xiaoming Rui, Shuai Li, and Xin Hu. "Maintenance Optimization of Offshore Wind Turbines Based on an Opportunistic Maintenance Strategy." Energies 12, no. 14 (July 10, 2019): 2650. http://dx.doi.org/10.3390/en12142650.

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Owing to the late development of offshore wind power in China, operational data and maintenance experience are relatively scarce. Due to the harsh environmental conditions, a reliability analysis based on limited sample fault data has been regarded as an effective way to investigate maintenance optimization for offshore wind farms. The chief aim of the present work is to develop an effective strategy to reduce the maintenance costs of offshore wind turbines in consideration of their accessibility. The three-parameter Weibull distribution method was applied to failure rate estimation based on limited data. Moreover, considering the impacts of weather conditions on the marine maintenance activities, the Markov method and dynamic time window were used to depict the weather conditions. The opportunistic maintenance strategy was introduced to cut down on the maintenance costs through optimization of the preventive maintenance age and opportunistic maintenance age. The simulation analysis we have performed showed that the maintenance costs of the opportunistic maintenance strategy were 10% lower than those of the preventive maintenance strategy, verifying the effectiveness of the proposed maintenance strategy.

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Goti, Aitor, Aitor Oyarbide-Zubillaga, Ana Sanchez, Tugce Akyazi, and Elisabete Alberdi. "Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms." Applied Sciences 9, no. 22 (November 13, 2019): 4849. http://dx.doi.org/10.3390/app9224849.

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Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair.

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DING, FANGFANG, and ZHIGANG TIAN. "OPPORTUNISTIC MAINTENANCE OPTIMIZATION FOR WIND TURBINE SYSTEMS CONSIDERING IMPERFECT MAINTENANCE ACTIONS." International Journal of Reliability, Quality and Safety Engineering 18, no. 05 (October 2011): 463–81. http://dx.doi.org/10.1142/s0218539311004196.

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Currently corrective maintenance and time-based preventive maintenance strategies are widely used in wind power industry. However, few methods are applied to optimize these strategies. This paper aims to develop opportunistic maintenance approaches for an entire wind farm rather than individual components that most of the existing studies deal with. Furthermore, we consider imperfect actions in the preventive maintenance tasks, which address the issue that preventive maintenance do not always return components to the as-good-as-new status in practice. In this paper we propose three opportunistic maintenance optimization models, where the preventive maintenance is considered as perfect, imperfect and two-level action, respectively. Simulation methods are developed to evaluate the costs of the proposed opportunistic maintenance policies. Numerical examples are provided to demonstrate the advantage of the proposed opportunistic maintenance methods in reducing the maintenance cost. The two-level action method demonstrates to be the most cost-effective in different cost situations, while the imperfect maintenance policy, which is a simpler method, is a close second. The developed methods are expected to bring immediate benefits to wind power industry.

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Bányai, Ágota. "Energy Consumption-Based Maintenance Policy Optimization." Energies 14, no. 18 (September 9, 2021): 5674. http://dx.doi.org/10.3390/en14185674.

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The optimal predictive, preventive, corrective and opportunistic maintenance policies play an important role in the success of sustainable maintenance operations. This study discusses a new energy efficiency-related maintenance policy optimization method, which is based on failure data and status information from both the physical system and the digital twin-based discrete event simulation. The study presents the functional model, the mathematical model and the solution algorithm. The maintenance optimization method proposed in this paper is made up of four main phases: computation of energy consumption based on the levelized cost of energy, computation of GHG emission, computation of value determination equations and application of the Howard’s policy iteration techniques. The approach was tested with a scenario analysis, where different electricity generation sources were taken into consideration. The computational results validated the optimization method and show that optimized maintenance policies can lead to an average of 38% cost reduction regarding energy consumption related costs. Practical implications of the proposed model and method regard the possibility of finding optimal maintenance policies that can affect the energy consumption and emissions from the operation and maintenance of manufacturing systems.

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Supsomboon, Srisawat, and Kanthapong Hongthanapach. "A Simulation Model for Machine Efficiency Improvement Using Reliability Centered Maintenance: Case Study of Semiconductor Factory." Modelling and Simulation in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/956182.

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The purpose of this study was to increase the quality of product by focusing on the machine efficiency improvement. The principle of the reliability centered maintenance (RCM) was applied to increase the machine reliability. The objective was to create preventive maintenance plan under reliability centered maintenance method and to reduce defects. The study target was set to reduce the Lead PPM for a test machine by simulating the proposed preventive maintenance plan. The simulation optimization approach based on evolutionary algorithms was employed for the preventive maintenance technique selection process to select the PM interval that gave the best total cost and Lead PPM values. The research methodology includes procedures such as following the priority of critical components in test machine, analyzing the damage and risk level by using Failure Mode and Effects Analysis (FMEA), calculating the suitable replacement period through reliability estimation, and optimizing the preventive maintenance plan. From the result of the study it is shown that the Lead PPM of test machine can be reduced. The cost of preventive maintenance, cost of good product, and cost of lost product were decreased.

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Li, Yan, Peng Han, Jinkuan Wang, and Xin Song. "Geometric Process-Based Maintenance and Optimization Strategy for the Energy Storage Batteries." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9798406.

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Renewable energy is critical for improving energy structure and reducing environment pollution. But its strong fluctuation and randomness have a serious effect on the stability of the microgrid without the coordination of the energy storage batteries. The main factors that influence the development of the energy storage system are the lack of valid operation and maintenance management as well as the cost control. By analyzing the typical characteristics of the energy storage batteries in their life cycle, the geometric process-based model including the deteriorating system and the improving system is firstly built for describing the operation process, the preventive maintenance process, and the corrective maintenance process. In addition, this paper proposes an optimized management strategy, which aims to minimize the long-run average cost of the energy storage batteries by defining the time interval of the detection and preventive maintenance process as well as the optimal corrective maintenance times, subjected to the state of health and the reliability conditions. The simulation is taken under the built model by applying the proposed energy storage batteries’ optimized management strategy, which verifies the effectiveness and applicability of the management strategy, denoting its obvious practicality on the current application.

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Wang, Rongcai, Zhonghua Cheng, Enzhi Dong, Chiming Guo, and Liqing Rong. "Reliability-Based Opportunistic Maintenance Modeling for Multi-Component Systems with Economic Dependence under Base Warranty." Discrete Dynamics in Nature and Society 2021 (May 7, 2021): 1–16. http://dx.doi.org/10.1155/2021/5541902.

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Maintenance usually plays a key role in controlling a multi-component production system within normal operations. Furthermore, the failure of components in the production system will also cause large economic losses for users due to the shutdown. Meanwhile, manufacturers of the production system will be confronted with the challenges of the warranty cost. Therefore, it is of great significance to optimize the maintenance strategy to reduce the downtime and warranty cost of the system. Opportunistic maintenance (OM) is a quite important solution to reduce the maintenance cost and improve the system performance. This paper studies the OM problem for multi-component systems with economic dependence under base warranty (BW). The irregular imperfect preventive maintenance (PM) is performed to reduce the failure rate of components at a certain PM reliability threshold. Moreover, the OM optimization model is developed to minimize the maintenance cost under the optimal OM reliability threshold of each component. A simulated annealing (SA) algorithm is proposed to determine the optimal maintenance cost of the system and the optimal OM threshold under BW. Finally, a numerical example of a belt conveyor drive device in a port is introduced to demonstrate the feasibility and advantages of the proposed model in maintenance cost optimization.

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Aldubaisi, Ali, and Jorge Valenzuela. "Maintenance Optimization of Wind Turbines Using Weather-Dependent Equivalent Age Model." Journal of Energy and Power Technology 03, no. 03 (April 26, 2021): 1. http://dx.doi.org/10.21926/jept.2103036.

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Aging models are important input into wind farm maintenance and financial viability models. Aging of wind turbines depends on many factors, including both ambient and usage conditions. This paper presents a virtual age based maintenance model for wind turbines considering the effect of wind speed and ambient air temperature on turbine aging. Two maintenance thresholds (i.e., corrective threshold and preventive threshold) and three repair actions (i.e., unscheduled corrective, scheduled corrective and preventive actions) are integrated into the maintenance model. The objective is to determine the optimal thresholds values that minimize the expected total maintenance costs. A discreet time simulation model is developed to produce 20 years of weather and usage scenarios for a single onshore wind turbine. The optimization model is formulated as a mixed-integer nonlinear problem and solved using the Nelder–Mead method. A numerical example is presented to highlight the benefits of the proposed approach. Compared with traditional age-based maintenance, the proposed approach can achieve improvement in both availability and costs. The results show up to 50% reduction in maintenance cost as well as the significance of the effects of wind speed and ambient air temperature in maintenance planning.

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Karima, Halim Qista, and Fauzan Romadlon. "Optimizing the Preventive Maintenance Scheduling Based on Dynamic Deterministic Demand in The Cement Manufacturing." Jurnal Ilmiah Teknik Industri 20, no. 1 (July 2, 2021): 109–18. http://dx.doi.org/10.23917/jiti.v20i1.13894.

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Preventive maintenance is a planned and scheduled maintenance method that is carried out before a machine failure occurs. The maintenance schedule can be determined based on experience, historical data, or recommendations. Selecting the maintenance schedule greatly affects the production system. The Clin machine in cement manufacturing has an important role in the cement production process. During treatment, the client machine cannot produce clinker, so it is necessary to plan a production system to meet the demand. This paper aims to design an optimization model for determining the preventive maintenance schedule for cement manufacturing by considering the production process and inventory control. Mathematical models with binary options are used to model that system. The model supports showing the optimal preventive maintenance schedule for the ciln machines with a binary decision each period. This mathematical model describes the interaction of production planning, inventory control, and scheduling of total maintenance on a kiln machine. The goal of this system is to determine the optimal preventive maintenance schedule with minimum costs. In addition, the system's output is the optimal production and inventory decision rule for each period. Based on the analysis and simulation of the model with the deterministic and dynamic demand, the optimal preventive maintenance schedule is in the 9th and 21st periods. The kiln machines are maintained every July with minimal costs. The model scenario shows the interaction of the variables and the sensitivity of the production capacity and demand to the decision rule of the variable.

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Ye, Jianfei, and Huimin Ma. "Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/725460.

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In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

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Solomentsev, O. V., M. Yu Zaliskyi, O. A. Shcherbyna, and M. M. Asanov. "OPTIMIZATION OF PREVENTIVE THRESHOLD FOR CONDITIONBASED MAINTENANCE OF RADIO ELECTRONIC EQUIPMENT." Radio Electronics, Computer Science, Control, no. 2 (July 15, 2021): 19–27. http://dx.doi.org/10.15588/1607-3274-2021-2-2.

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Context. Operation costs throughout the life cycle of radio electronic equipment are very significant, which value far exceeds the initial cost of the equipment. Therefore, the up-to-date scientific and technical problem is to minimize operation costs. One of the ways to solve this problem is the introduction of statistical data processing technologies in the operation systems of radio electronic equipment. Objective. The goal of the paper is to improve the efficiency of thecondition-based maintenance with the determining parameters monitoring, which is widely used in civil aviation. Method. The solution of this problem is based on finding the functional dependence of the efficiency indicator in the form of specific operation costs on the basic parameters of radio electronic equipment and its operation system. To determine this dependence, the probability-event model is used,as well as methods of probability theory and mathematical statistics, in particular methods of statistical classification of sample sets and functional transformations of random variables. To determine the optimal level of the preventive threshold by the criterion of minimizing operation costs, the method of statistical simulation of Monte-Carlo is used. Results. Maintenance strategy with the determining parameters monitoring based on additional statistical data processing and technology of the optimal preventive threshold calculation are improved. Conclusions. The obtained results can be used during the development and modernization of operation systems of radio electronic equipment in terms of application of statistical data processing procedures. A comparative analysis of the two maintenance strategies showed that the use of additional statistical data processing might reduce specific operation costs. The proposed technology for determining the optimal preventive threshold can be extended to use during the operation of complex technical systems, in particular for those whose technical condition is associated with the values of the determining parameters.

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Sangroudi, Hadi Abdollahzadeh. "Wind Power Performance Optimization Considering Redundancy and Opportunistic Maintenance." MENDEL 24, no. 1 (June 1, 2018): 55–62. http://dx.doi.org/10.13164/mendel.2018.1.055.

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In This paper the redundancy and imperfect opportunistic maintenance optimization of a multi-state weighted k-out-of-n system is formulated. The objective is to determine the k-out-of-n system redundancy level and the maintenance strategy which will minimize the wind farm life cycle cost subject to an availability constraint. A new condition based opportunistic maintenance approach is developed. Different component health state thresholds are introduced for imperfect maintenance of failed subsystems and working subsystems and preventive dispatching of maintenance teams. In addition, a simulation method is developed to evaluate the performance measures of the system considering different types of subsystems, maintenance activation delays and durations, limited number of maintenance teams, and discrete inspection of the system. Also, a multi-seed tabu search heuristic algorithm is also proposed to solve the formulated problem. An application to the optimal design of a wind farm is provided to illustrate the proposed approach.

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Orlov, Sergey P., Sergey V. Susarev, and Roman A. Uchaikin. "Application of Hierarchical Colored Petri Nets for Technological Facilities’ Maintenance Process Evaluation." Applied Sciences 11, no. 11 (May 31, 2021): 5100. http://dx.doi.org/10.3390/app11115100.

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The high reliability of modern engineering systems is achieved by performing predictive maintenance. Mathematical models based on stochastic timed colored Petri nets are an effective tool for developing complex production processes for Industry 4.0. This article discusses the maintainability evaluation used in hierarchical Petri net models. The hierarchical simulation model was built using timed colored Petri nets, and was constructed with four levels of repair and maintenance modules. New module structures are proposed for simulating the schedule of production tasks and interaction with technological units. The emphasis is on the processes of predicting maintenance and repair, moving units to service, replacing units, and forming a reserve. The design of the simulation modules allows the setting of probabilistic parameters for the distributions of equipment failures, requests for unit maintenance, repair time, and recovery time after repair. The article proposes to use the hierarchical Petri model in conjunction with solving the problem of minimizing the cost of service. The iterative procedure consists of obtaining an approximate unit distribution by tasks, subsequent simulation of the technological process, and adjusting the optimization problem constraints. For example, the hierarchical Petri net is considered to assess the maintainability of autonomous agricultural vehicles. The results of the simulation experiments are presented. A simulation of the agrotechnical production process was performed, during which vehicles were maneuvered, taken out for repair or maintenance, and returned to the reserve fund. The interdependencies of preventive maintenance periods, service operations, failure rates, and predictive maintenance requests were obtained in order to comply with the task scheduling. The proposed model is a generalization, but it is especially effective in studying mobile equipment servicing.

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Bai, Cheng, and Guo. "Maintenance Optimization Model with Sequential Inspection Based on Real-Time Reliability Evaluation for Long-Term Storage Systems." Processes 7, no. 8 (July 25, 2019): 481. http://dx.doi.org/10.3390/pr7080481.

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For long-term storage systems such as rockets and missiles, most of the relevant models and algorithms for inspection and maintenance currently focus on analysis based on periodic inspection. However, considering factors such as the complexity of the degradation mechanisms of these systems, the constraints imposed by failure risk, and the uncertainty caused by environmental factors, it is preferable to dynamically determine the inspection intervals based on real-time status information. This paper investigates the issue of maintenance optimization modelling for long-term storage systems based on real-time reliability evaluation. First, the Wiener process is used to establish a performance degradation model for one critical unit of such a system, and a closed-form expression for the real-time reliability distribution is obtained by using the first-hitting-time theory. Second, sequential inspection intervals are dynamically determined by combining the real-time reliability function with a real-time reliability threshold for the system. Third, a maintenance optimization model is established for the critical unit based on update process theory. An analytical expression for the expected total cost rate is derived, and then, the real-time reliability threshold and the preventive maintenance threshold for the unit are jointly optimized by means of Monte Carlo simulation, with the lowest expected total cost rate as the optimization goal. Finally, two examples of a gyroscope and an alloy blade that are commonly used in the long-term storage systems are considered, and the validity of the proposed model is illustrated by means of a sensitivity analysis of the relevant parameters.

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Finkelstein, Maxim, Ji Hwan Cha, and Gregory Levitin. "A hybrid preventive maintenance model for systems with partially observable degradation." IMA Journal of Management Mathematics 31, no. 3 (February 6, 2020): 345–65. http://dx.doi.org/10.1093/imaman/dpz018.

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Abstract A new model of hybrid preventive maintenance of systems with partially observable degradation is developed. This model combines condition-based maintenance with age replacement maintenance in the proposed, specific way. A system, subject to a shock process, is replaced on failure or at some time ${T}_S$ if the number of shocks experienced by this time is greater than or equal to m or at time $T>{T}_S$ otherwise, whichever occurs first. Each shock increases the failure rate of the system at the random time of its occurrence, thus forming a corresponding shot-noise process. The real deterioration of the system is partially observed via observation of the shock process at time ${T}_S$. The corresponding optimization problem is solved and a detailed numerical example demonstrates that the long-run cost rate for the proposed optimal hybrid strategy is smaller than that for the standard optimal age replacement policy.

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Zhou, Binghai, Jiadi Yu, Jianyi Shao, and Damien Trentesaux. "Bottleneck-based opportunistic maintenance model for series production systems." Journal of Quality in Maintenance Engineering 21, no. 1 (March 9, 2015): 70–88. http://dx.doi.org/10.1108/jqme-09-2013-0059.

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Purpose – The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect effect into maintenance activities. Design/methodology/approach – On the analysis of availability and maintenance cost, preventive maintenance (PM) models subjected to imperfect maintenance for different equipment types are built. And then, a cost-saving function of OM is established to find out an optimal OM strategy, depending on whether the front-bottleneck machines adopt OM strategy or not. A numerical example is given to show how the proposed bottleneck-based OM model proceeded. Findings – The simulation results indicate that the proposed model is better than the methods to maintain the machines separately and the policy to maintain all machines when bottleneck machine reaches its reliability threshold. Furthermore, the relationship between OM strategy and corresponding parameters is identified through sensitivity analysis. Practical implications – In practical situations, the bottleneck machine always determines the throughput of the whole series production system. Whenever a PM activity is carried out on the bottleneck machine, there will be an opportunity to maintenance other machines. Under such circumstances, findings of this paper can be utilized for the determination of optimal OM policy with the objective of minimizing total maintenance cost of the system. Originality/value – This paper presents a bottleneck-based OM optimization model with the integration of the imperfect effect as a new method to schedule maintenance activities for a series production system with buffers. Furthermore, to the best of the knowledge, this paper presents the first attempt to considering the bottleneck constraint on system capacity and diverse types of machines as a means to minimize the maintenance cost and ensure the system throughput.

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Qi, Faqun, and Binghai Zhou. "Maintenance policy optimization for a cold standby system considering multiple failure types." Journal of Quality in Maintenance Engineering 25, no. 2 (May 7, 2019): 182–98. http://dx.doi.org/10.1108/jqme-09-2017-0065.

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Purpose The purpose of this paper is to develop novel preventive maintenance (PM) modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure. Design/methodology/approach The system consists of two components and a single repair shop, assuming that the repair shop can only service for one component at a time. Based on semi-Markov theory, transition probabilities between all possible system states are discussed. With the transition probabilities, Markov renewal equations are established at regenerative points. By solving the Markov regenerative equations, the mean time from the initial state to system failure (MTSF) and the steady state availability (SSA) are formulated as two reliability measures for different reliability requirements of systems. The optimal PM policies are obtained when MTSF and SSA are maximized. Findings The result of simulation experiments verifies that the derived maintenance models are effective. Sensitivity analysis revealed the significant influencing factors for optimal PM policy for cold standby systems when different system reliability indexes (i.e. MTSF and SSA) are considered. Furthermore, the results show that the repair for random failure has a tremendous impact on prolonging the MTSF of cold standby system and PM plays a greater role in promoting the system availability of a cold standby system than it does in prolonging the MTSF of system. Practical implications In practical situations, system not only suffers normal deterioration caused by internal factors, but also undergoes random failures influenced by random shocks. Therefore, multiple failure types are needed to be considered in maintenance modeling. The result of the sensitivity analysis has an instructional role in making maintenance decisions by different system reliability indexes (i.e. MTSF and SSA). Originality/value This paper presents novel PM modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure. The sensitivity analysis identifies the significant influencing factors for optimal maintenance policy by different system reliability indexes which are useful for the managers for further decision making.

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Rychagova, Elena, and Vladimir M. Levin. "Improving the efficiency of electric network equipment operational service." E3S Web of Conferences 114 (2019): 03001. http://dx.doi.org/10.1051/e3sconf/201911403001.

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Maintenance and repair (M&R) of electric network equipment is one of the crucial methods for reliable power supplying. For implementing the condition based maintenance strategy is necessary to solve a number of problems, including prioritizing of preventive measures order and determining optimal time of their periodic. This paper points out two specific aspects: 1) development of an adequate reliability simulation model to optimize the operational factors of electric network equipment; 2) advantages of the proposed M&R optimization methodology in the operational process of real distribution system. Developed probabilistic mathematical model of M&R allows determining criteria for obtaining the optimal solution according to the results of numerical calculations procedures. Described mathematical model is based on the theory of controlled Markov processes. This mathematical method allows describing “ageing” systems. The main advantages of the model are the possibility of considering all random processes for described type of equipment and obtaining the analytical solutions for basic reliability and economical indexes. Based on this, the criterions and strategy for choosing an optimal time intervals of M&R procedures have been formed. Improving the efficiency of M&R of electrical equipment is considered on a real distribution network.

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Ciortea, Mihaela Elisabeta. "Analytical Interpretation to Optimize Transport Systems." Advanced Materials Research 1036 (October 2014): 881–84. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.881.

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This paper aims to analyze the classical transport system using only two processing centers, maintenance can ensure, without negatively impacting the transportation system optimization. To achieve transport system simulation packages have been used to simulate Petri and implementation specialist and preparing conclusion is made based on data specialist. The model is intended to be a source of help in the activities of companies seeking to optimize the transport system and not be disrupted by achieving maintenance. Benefits are those prototypes and analyze the whole system after the implementation of tracking and being able to control the entire system, leading to the prevention and elimination of queues later appearance possible accidents. Studying this system model can deduce new information without the high costs.

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Han, Soo-Bin, Hwanyeong Oh, Won-Yong Lee, Jinyeon Won, Suyong Chae, and Jongbok Baek. "On-Line EIS Measurement for High-Power Fuel Cell Systems Using Simulink Real-Time." Energies 14, no. 19 (September 26, 2021): 6133. http://dx.doi.org/10.3390/en14196133.

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Impedance measurements by EIS are used to build a physical circuit-based model that enables various fault diagnostics and lifetime predictions. These research areas are becoming increasingly crucial for the safety and preventive maintenance of fuel cell power systems. It is challenging to apply the impedance measurement up to commercial applications at the field level. Although EIS technology has been widely used to measure and analyze the characteristics of fuel cells, EIS is applicable mainly at the single-cell level. In the case of stacks constituting a power generation system in the field, it is difficult to apply EIS due to various limitations in the high-power condition with uncontrollable loads. In this paper, we present a technology that can measure EIS on-line by injecting the perturbation current to fuel cell systems operating in the field. The proposed EIS method is developed based on Simulink Real-Time so that it can be applied to embedded devices. Modeling and simulation of the proposed method are presented, and the procedures from the simulation in virtual space to the real-time application to physical systems are described in detail. Finally, actual usefulness is shown through experiments using two physical systems, an impedance hardware simulator and a fuel cell stack with practical considerations.

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Liu, Shiyang, Xuefu Zhang, Yuanfu Zhou, and Feng Gao. "Optimization Study of Fluffy Materials Flocking Drainage Pipes to Resist Blockage Based on MD Binding Energy." Coatings 11, no. 7 (July 15, 2021): 853. http://dx.doi.org/10.3390/coatings11070853.

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Drainage pipe blockage resulting from crystals is one of the causes for cracking and leakage of tunnel lining. Therefore, effective prevention from drainage pipe blockage caused by crystals is crucial to ensure the safety and stability of lining structures during the operation of tunnel drainage system. Based on a large number of indoor model tests and numerical simulation analyses, binding energy between four materials and the calcium carbonate aqueous solution (“solid + liquid” system) and that between the four materials and the two typical growth crystals of calcium carbonate (“solid + solid” system) were studied. The research results indicated that: (1) The four materials all had an adsorption effect on the calcium carbonate aqueous solution system, and the PA6 had the greatest adsorption effect while the PP had the smallest adsorption effect; (2) There was spontaneous adsorption between the PVC or PA6 and the two typical growth crystals of calcium carbonate and no adsorption between the PP or SiC and the two typical growth crystals of calcium carbonate unless external energy was in place; (3) The PP and SiC can be used as the materials for drainage pipe flocking, but it shall be ensured that the fluffy material has a good geometrical property. The prevention technology for crystallization that causes drainage pipe blockage fills the gap in the research of drainage pipe blockage caused by crystals, which can reduce the maintenance cost for the operation of the tunnel drainage system and ensure safe and normal operation of the tunnel.

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Roux, O., D. Duvivier, G. Quesnel, and E. Ramat. "Optimization of preventive maintenance through a combined maintenance-production simulation model." International Journal of Production Economics 143, no. 1 (May 2013): 3–12. http://dx.doi.org/10.1016/j.ijpe.2010.11.004.

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Baek, Seok Heum, Kyoung Young Lee, Seok Swoo Cho, and Won Sik Joo. "Reliability-Based Optimization for Fatigue Maintenance Planning of Freight Car." Key Engineering Materials 345-346 (August 2007): 1369–72. http://dx.doi.org/10.4028/www.scientific.net/kem.345-346.1369.

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The performance of bogie decreases according as traveling distance increases. Its causes were expected to be fatigue, wear, and deterioration. Especially, maintenance coat needs about triple than manufacture cost during required life in case of container and uncovered freight car. Freight car doesn’t adapt corrective maintenance which repairs a poor part after a trouble but use preventive maintenance which fixes a bad part before a trouble. There were a few researches on preventive maintenance such as inspect affairs and facilities management. They couldn’t estimate the operation reliability on railroad vehicle. Therefore, this study proposes the preventive maintenance procedure that predict repair period of end beam for uncovered freight car using reliability function and instantaneous failure rate on the basis of fatigue test and load history data.

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Lee, Juhyun, Byunghoon Kim, and Suneung Ahn. "Maintenance Optimization for Repairable Deteriorating Systems under Imperfect Preventive Maintenance." Mathematics 7, no. 8 (August 7, 2019): 716. http://dx.doi.org/10.3390/math7080716.

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This study deals with the preventive maintenance optimization problem based on a reliability threshold. The conditional reliability threshold is used instead of the system reliability threshold. Then, the difference between the two thresholds is discussed. The hybrid failure rate model is employed to represent the effect of imperfect preventive maintenance activities. Two maintenance strategies are proposed under two types of reliability constraints. These constraints are set to consider the cost-effective maintenance strategy and to evaluate the balancing point between the expected total maintenance cost rate and the system reliability. The objective of the proposed maintenance strategies is to determine the optimal conditional reliability threshold together with the optimal number of preventive maintenance activities that minimize the expected total maintenance cost per unit time. The optimality conditions of the proposed maintenance strategies are also investigated and shown via four propositions. A numerical example is provided to illustrate the proposed preventive maintenance strategies. Some sensitivity analyses are also conducted to investigate how the parameters of the proposed model affect the optimality of preventive maintenance strategies.

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Belyi, Dmitriy, Elmira Popova, David P. Morton, and Paul Damien. "Bayesian failure-rate modeling and preventive maintenance optimization." European Journal of Operational Research 262, no. 3 (November 2017): 1085–93. http://dx.doi.org/10.1016/j.ejor.2017.04.019.

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Oke, S. A., O. E. Charles-Owaba, and A. E. Oluleye. "Preventive Maintenance Scheduling." International Journal of Operations Research and Information Systems 4, no. 2 (April 2013): 69–94. http://dx.doi.org/10.4018/joris.2013040105.

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In this work, the effectiveness of preventive maintenance scheduling (PMS) decisions was reported based on a techno-economic model that reflects cost objective function for ship maintenance activities. With a potential to impact on both transportation businesses and users of transportation services, the model provides an alternative to the combined classical literature problems of spare-parts inventory management and control, failure prediction and reliability. The PMS model developed incorporates separate and combined functions of indirect, direct and factor maintenance costs. Idleness period for various formulated schedules are evaluated and compared. First, a general form of the preventive maintenance cost function incorporating unit cost of maintaining ships, a set of cost function parameters and variables was developed. The operations research framework for the problem is then applied to obtain test cases in which cost parameter(s) was/were used for scheduling decisions. Monte Carlo simulation is applied to generate additional test problems. Practical data were used to validate the model. For each problem, optimal schedules based on one to four cost parameters were determined. For each schedule, the total maintenance cost, cost of idleness, total ship idle period and total ship operation period were computed under inflation, opportunity and combined opportunity and inflation and compared with the values corresponding to maintenance cost parameter using t-test (p<0.05). Thus, the use of combined data from maintenance, opportunity and inflation for preventive maintenance scheduling of a fleet of ships is more effective than direct maintenance cost approach.

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Arueti, Shimshon, and David Okrent. "A knowledge-based prototype for optimization of preventive maintenance scheduling." Reliability Engineering & System Safety 30, no. 1-3 (January 1990): 93–114. http://dx.doi.org/10.1016/0951-8320(90)90091-z.

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Ramirez Restrepo, Laura M., Sophie Hennequin, and Aicha Aguezzoul. "Optimization of integrated preventive maintenance based on infinitesimal perturbation analysis." Computers & Industrial Engineering 98 (August 2016): 470–82. http://dx.doi.org/10.1016/j.cie.2016.06.019.

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Li, Ruiying, and Xufeng Zhang. "Preventive Maintenance Interval Optimization for Continuous Multistate Systems." Mathematical Problems in Engineering 2020 (January 20, 2020): 1–10. http://dx.doi.org/10.1155/2020/2942940.

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Preventive maintenance (PM), which is performed periodically on the system to lessen its failing probability, can effectively decrease the loss caused by the system breakdown or the performance degradation. The optimal PM interval has been well studied for both binary-state systems (BSSs) and discrete multistate systems (MSSs). However, in reality, the performance of many systems can change continuously, ranging from complete failure to perfect functioning. Considering such characteristics of systems, two types of performance-based measures, performance availability and probabilistic resilience, are addressed to quantify the system’s behaviour for continuous MSS. A Monte Carlo-based method is given to analyse the performance change process of the system, and an optimization framework is proposed to find the optimal PM interval with the considerations of per-unit-time cost, system breakdown rate, performance availability, and probabilistic resilience. A computer cluster is used as an example to illustrate the effectiveness of our proposed method.

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Lyonnet, Patrick, and Rosario Toscano. "Dynamic Preventive Maintenance, Optimization of Time between Overhaul." Advanced Materials Research 433-440 (January 2012): 3066–69. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3066.

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We present in this paper an method for evaluating the reliability in real time applied to the optimization of preventive maintenance and evaluation of the parameters of dependability. This approach is based on a function Z (t), which assesses the damage from the history of real operating conditions. This assessment is used to calculate the residual reliability, and can then be used to optimize the preventive maintenance and in particular optimize the time between overhaul (TBO). This approach can be used to take more realistic decisions about preventive change and thus led to a better risk management.

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Hani, Yasmina, Lionel Amodeo, Farouk Yalaoui, and Haoxun Chen. "Simulation based optimization of a train maintenance facility." Journal of Intelligent Manufacturing 19, no. 3 (January 15, 2008): 293–300. http://dx.doi.org/10.1007/s10845-008-0082-8.

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Lim, Jae‐Hak, Dae‐Kyung Kim, and Dong Ho Park. "Maintenance optimization for second‐hand products following periodic imperfect preventive maintenance warranty period." Applied Stochastic Models in Business and Industry 35, no. 4 (March 21, 2019): 1077–89. http://dx.doi.org/10.1002/asmb.2450.

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WANG, Lingzhi. "Preventive Maintenance Economic Optimization Model Based on Equipment Availability and Reliability." Journal of Mechanical Engineering 46, no. 04 (2010): 163. http://dx.doi.org/10.3901/jme.2010.04.163.

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., Marsetio, Supartono ., Amarulla Octavian, Ahmadi ., Rajab Ritonga, and Rudiyanto . "Optimization of Time Delay based Preventive Maintenance using Markov Decision Process." International Journal of Signal Processing, Image Processing and Pattern Recognition 10, no. 8 (August 31, 2017): 125–34. http://dx.doi.org/10.14257/ijsip.2017.10.8.11.

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37

Lua, Yang, Liping Suna, Jichuan Kanga, and Xinyue Zhang. "Maintenance Grouping Optimization for Offshore Wind Turbine Considering Opportunities Based on Rolling Horizon Approach." Polish Maritime Research 25, no. 2 (June 1, 2018): 123–31. http://dx.doi.org/10.2478/pomr-2018-0063.

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Abstract In future, offshore wind turbines may be consider a crucial part in the supply of energy. Maintenance processes are directed to attain a safe and reliable operation of offshore machines and wind turbines. In this paper, an opportunistic maintenance strategy for offshore wind turbine is proposed, considering imperfect maintenance and the preventive maintenance durations. Reliability Centric Maintenance serves as a proactive tactic to operations and maintenance by inhibiting the possible reasons of poor performance and controlling failures. Other components can implement the opportunistic preventive maintenances if one component has reached its reliability threshold. According to the rolling horizon approach, it is of great importance to update the maintenance planning for the sake of the short-term information. By figuring out the best combination, the maintenance schedule in the mission time has been finally determined. Failure information are obtained from previous studies to accomplish the calculations. The outcomes indicate that the maintenance cost has been dramatically reduced through the application of opportunistic maintenance.

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Wang, Qian, Zhonghua Cheng, Qintao Gan, Yongsheng Bai, and Jianqing Zhang. "Cost Optimization of Two-Dimensional Warranty Products under Preventive Maintenance." Mathematical Problems in Engineering 2021 (March 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/2538050.

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Aiming to reduce the warranty cost, we put forward a new warranty strategy of two-dimensional (2D) warranty products. In this strategy, the incomplete preventive maintenance and minimal repair are proposed where the preventive maintenance is further divided by the degree of maintenance, and all other failures are repaired minimally. Preventive maintenance of different degree is put forward by the manufacturer and the user, respectively, and the repair factor is used to distinguish the different degree of maintenance. We establish model of warranty cost based on reliability theory and propose a method to solve the model. Finally, the validity of this model is proved by a numerical example, and the sensitivity analysis is carried out.

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Mahfoud, H., A. El Barkany, and A. El Biyaali. "Preventive Maintenance Optimization in Healthcare Domain: Status of Research and Perspective." Journal of Quality and Reliability Engineering 2016 (July 27, 2016): 1–10. http://dx.doi.org/10.1155/2016/5314312.

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Although medical equipment maintenance has been carefully managed for years, very few in-depth studies have been conducted to evaluate the effectiveness and efficiency of these implemented preventive maintenance strategies, especially after the debate about the credibility of manufacturer’s recommendations has increased in the clinical engineering community. Facing the dilemma of merely following manufactures maintenance manual or establishing an evidence-based maintenance, medical equipment maintenance could have exploited an advanced area in operations research which is maintenance optimization research. In this paper, we review and examine carefully the status of application oriented research on preventive maintenance optimization of medical devices. This study addresses preventive healthcare maintenance with a focus on factors influencing the maintenance decision making. The analysis is structured by defining different aspects necessary to construct a maintenance optimization model. We conclusively propose directions to develop suitable tools for better healthcare maintenance management.

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Huang, Ao Lin, Qing Min Li, and Tie Bing Li. "Study on Information System with Condition-Based Maintenance Models for a Continuously Degrading System." Advanced Materials Research 703 (June 2013): 227–30. http://dx.doi.org/10.4028/www.scientific.net/amr.703.227.

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This paper considers on-condition maintenance of a continuously degrading information system, in which the inter-maintenance time and the maintenance time depend on the condition of the system at which maintenance is carried out. The durations of preventive maintenance activities are supposed to be exponentially distributed. Assuming deterioration of the system follows a gamma process, models are established to maximize the system average availability when the effectiveness of the preventive maintenance activity becomes weaker and weaker. Optimal solutions on the condition of the system at which maintenance should be performed and the number of times of maintenance action to be carried out are obtained based on Monte-Carlo simulation. A case study is given to show the procedure of the maintenance model and simulation.

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ZHANG, Xiaohong. "Joint Optimization of Condition-based Preventive Maintenance and Spare Parts Provisioning Policy for Equipment Maintenance." Journal of Mechanical Engineering 51, no. 11 (2015): 150. http://dx.doi.org/10.3901/jme.2015.11.150.

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42

Hussain, Zahid, and Hamid Jan. "Establishing simulation model for optimizing efficiency of CNC machine using reliability-centered maintenance approach." International Journal of Modeling, Simulation, and Scientific Computing 10, no. 06 (November 27, 2019): 1950034. http://dx.doi.org/10.1142/s179396231950034x.

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The objective of this work was to enhance the product’s quality by concentrating on the machine’s optimized efficiency. In order to increase the machine’s reliability, the basis of reliability-centered maintenance approach was utilized. The purpose was to establish a planned preventive maintenance strategy to identify the machine’s critical components having a noteworthy effect on the product’s quality. The critical components were prioritized using failure mode and effect analysis (FMEA). The goal of the study was to decrease the ppm time interval for a CNC machine by simulating the projected preventive maintenance time interval. For this purpose, the simulation software ProModel 7.5 was implemented for the current preventive maintenance procedure to choose the best ppm time interval which contributed better norms. Five dissimilar optimization approaches were applied, however, the first approach yielded the prominent total system cost and the shorter ppm interval. The results of the study revealed that there was an increase of USD 1878 as a result of an increase in total system cost from USD 78,365 to USD 80,243. Preventive maintenance costs were reduced from USD 4196 to USD 2248 (46%). The costs associated with good parts increased from USD 8259 to USD 8294 (0.4%) and the costs associated with defective parts reduced from USD 171 to USD 3 (98.25%), respectively.

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Zhao, Yongjun, Vitali Volovoi, Mark Waters, and Dimitri Mavris. "A Sequential Approach for Gas Turbine Power Plant Preventative Maintenance Scheduling." Journal of Engineering for Gas Turbines and Power 128, no. 4 (December 14, 2005): 796–805. http://dx.doi.org/10.1115/1.2179470.

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Traditionally, gas turbine power plant preventive maintenance schedules are set with constant intervals based on recommendations from the equipment suppliers. Preventive maintenance is based on fleet-wide experience as a guideline as long as individual unit experience is not available. In reality, the operating conditions for each gas turbine may vary from site to site and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests a gas turbine needs more frequent inspection and maintenance as it ages. A unit-specific sequential preventive maintenance approach is therefore needed for gas turbine power plant preventive maintenance scheduling. Traditionally, the optimization criteria for preventive maintenance scheduling is usually cost based. However, in the deregulated electric power market, a profit-based optimization approach is expected to be more effective than the cost-based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economic factors drive maintenance frequency and expense to more frequent repairs and greater expense as equipment ages is introduced, and a profit-based unit-specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, a conceptual level study is performed using a base load combined cycle power plant with a single gas turbine unit.

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Su, H. S., and J. D. Li. "Research and Simulation on Repairable Device Preventive Maintenance Control Policy." Applied Mechanics and Materials 743 (March 2015): 189–95. http://dx.doi.org/10.4028/www.scientific.net/amm.743.189.

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In consideration of the situation that the conventional preventive maintenance inspection cycle of the repairable device is not be changed as long as it is determined, based on continuous-time Markov processes, the repairable device preventive maintenance control strategy is studied. The preventive maintenance model is established, i.e., its state transition diagram, steady state value of the model is calculated, the control policy of device’s check rates based on device status is proposed, and the proposed control policy is simulated and confirmed using MATLAB. Simulation results show that under the proposed control policy rule, the actual operation curve of the system can well track the desired operation curve of the system, and finally can achieve the desired steady state value. Studies have shown that the method has some help to strengthen device maintenance management, improve device maintenance strategies, ensure that the device is repaired according to the state, and reduce maintenance costs of the device.

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Vale, Cecília, and Isabel M. Ribeiro. "RAILWAY CONDITION-BASED MAINTENANCE MODEL WITH STOCHASTIC DETERIORATION." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 20, no. 5 (October 10, 2014): 686–92. http://dx.doi.org/10.3846/13923730.2013.802711.

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The application of mathematical programming for scheduling preventive maintenance in railways is relatively new. This paper presents a stochastic mathematical model designed to optimize and to predict tamping operations in ballasted tracks as preventive condition-based maintenance. The model is formulated as a mixed 0–1 nonlinear program that considers real technical aspects as constraints: the reduction of the geometrical track quality over time is characterized by the deterioration rate of the standard deviation of the longitudinal level; the track layout; the dependency of the track recovery on its quality at the moment of the maintenance operation; the limits for preventive maintenance that depend on the maximum permissible train speed. In the model application, a railway stretch with 51.2 km of length is analysed for a time period of five years. The deterioration model is stochastic and represents the reduction of the standard deviation of the longitudinal level over time. The deterioration rate of the standard deviation of the longitudinal level is simulated by Monte Carlo techniques, considering the three parameters Dagum probabilistic distribution fitted with real data (Vale, Simões 2012). Two simulations are performed and compared: stochastic simulation in space; stochastic simulation in space and time. The proposed condition-based maintenance model is able to produce optimal schedules within appropriate computational times.

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Rezg, N., X. Xie, and Y. Mati. "Joint optimization of preventive maintenance and inventory control in a production line using simulation." International Journal of Production Research 42, no. 10 (May 15, 2004): 2029–46. http://dx.doi.org/10.1080/00207540310001638235.

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Mahayudin, Aisyah Razana, Mohamed Tarmizi Ahmad, Fairuz Izzuddin Romli, and Amzari Zhahir. "Investigating Imperfect Inspection of Avionics System and its Relationship with Availability Percentage." Applied Mechanics and Materials 225 (November 2012): 534–39. http://dx.doi.org/10.4028/www.scientific.net/amm.225.534.

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System maintenance is necessary to prolong the operational life of a system. There are four types of maintenance policies which are corrective, preventive, scheduled and predictive (conditioning based maintenance). However, this paper focuses on preventive maintenance and corrective maintenance policies. In general, the goal of preventive maintenance is to retain the system at its good operating conditions before the occurrence of any failure while corrective maintenance is done as a result of the system’s operating failure. This paper investigates the effects of maintenance activities for Engine Indication and Flight Instruments subsystems of the avionics system of a general aircraft. The simulation of the maintenance process is done using Monte Carlo and Discrete Event simulation methods for different preventive maintenance interval (PMI).

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Yang, Ruifeng, Jianshe Kang, and Zhenya Quan. "An Enhanced Preventive Maintenance Optimization Model Based on a Three-Stage Failure Process." Science and Technology of Nuclear Installations 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/193075.

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Nuclear power plants are highly complex systems and the issues related to their safety are of primary importance. Probabilistic safety assessment is regarded as the most widespread methodology for studying the safety of nuclear power plants. As maintenance is one of the most important factors for affecting the reliability and safety, an enhanced preventive maintenance optimization model based on a three-stage failure process is proposed. Preventive maintenance is still a dominant maintenance policy due to its easy implementation. In order to correspond to the three-color scheme commonly used in practice, the lifetime of system before failure is divided into three stages, namely, normal, minor defective, and severe defective stages. When the minor defective stage is identified, two measures are considered for comparison: one is that halving the inspection interval only when the minor defective stage is identified at the first time; the other one is that if only identifying the minor defective stage, the subsequent inspection interval is halved. Maintenance is implemented immediately once the severe defective stage is identified. Minimizing the expected cost per unit time is our objective function to optimize the inspection interval. Finally, a numerical example is presented to illustrate the effectiveness of the proposed models.

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Altehmazi, Manar Mohammed, Saad Mohamed Ahmed Suliman, and Yaser Alalawi. "An Optimization Approach to the Preventive Maintenance Planning Process." Modern Applied Science 11, no. 9 (August 5, 2017): 20. http://dx.doi.org/10.5539/mas.v11n9p20.

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Creating a good preventive maintenance schedule is essential to perform an efficient shutdown. This paper is presenting a mathematical non-linear model that is formulated for the turnaround maintenance scheduling problem, and proposing an algorithmic optimization approach that combines the scheduling and workforce allocation in one phase. The strategy used here mainly aims to filter the uncompleted tasks from the tasks set and then to filter again from the resulted uncompleted tasks the ones which are satisfying the precedence constraint. If a task is not completed because of its preceding task, then it is put under hold until the precedence is finished. Once these two conditions are satisfied, the allocation of processors (workers in departments) starts considering the available ones. The algorithmic optimization approach is based on customized objective function and a number of constraints. It is coded in MATLAB format and solved using a modified genetic solver. It provides an optimized or pseudo-optimized schedule and workforce allocation plan, saves time and effort, and as a consequence it improves the efficiency and effectiveness of the maintenance system. The efficiency of the proposed algorithm in terms of computation time is affected mostly by the number of assigned tasks and the branching density of the dependent tasks.

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Naga Srinivasa Rao, P., and V. N. Achutha Naikan. "An Algorithm for Simultaneous Optimization of Parameters of Condition-based Preventive Maintenance." Structural Health Monitoring: An International Journal 8, no. 1 (April 2008): 83–94. http://dx.doi.org/10.1177/1475921708094793.

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Journal articles on the topic 'Simulation based preventive maintenance optimization'

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