Optimal Maintenance Schedule Research Articles

An integrated framework for dynamic capacity withholding assessment considering commitment strategies of generation companies

This paper addresses an integrated framework for the dynamic capacity withholding assessment of an independent system operator that determines the mid-term maintenance scheduling of generation companies and day-ahead scheduling of wholesale market participants. The main contribution of this research is that two dynamic capacity-withholding indices are proposed for mid-term and day-ahead scheduling of generation companies that estimate the dynamic capacity withholding opportunities of generation units in an ex-ante manner. The proposed framework is another contribution of this research that uses a four-stage optimization process that the system operator can detect and prevent the formation of withholding groups. The optimal maintenance scheduling from the generation companies viewpoint is assessed in the first-stage problem that considers different mid-term withholding opportunities. The optimal mid-term maintenance scheduling is carried out in the second-stage problem that recognizes and rejects the dynamic capacity withholding of generation companies. The optimal scheduling of day-ahead generation companies considering their dynamic capacity withholding is the third contribution of this paper that optimizes the scheduling of generation units for day-ahead horizon considering responsive loads. The proposed method is applied to 30-bus, 57-bus and 118-bus IEEE test systems. A full competition algorithm is also carried out to evaluate the competition states of generation companies. The proposed algorithm detected that the dynamic capacity withholding might lead to increase of nodal price by about 279.22%, 764.43%, and 851.2% for 30-bus, 57-bus, and 118-bus IEEE test systems with respect to the non-capacity withholding conditions, respectively.

Synchronized optimization of EMU train assignment and second-level preventive maintenance scheduling

This paper addresses the synchronized electric multiple unit (EMU) train assignment and maintenance scheduling problem for a weekly planning period. To guarantee the safe operation of railways, the focus of this study is to coordinate the maintenance plans of all the EMU trains with a given fleet size, so as to make trains available to meet operational requirements. A 0 and 1 programming model is proposed to mathematically describe the problem. The objective is to minimize the mileage losses of all EMU trains by considering the train operational requirements and depot capacity restrictions. An important feature distinguishing this model from existing literature is that it is able to handle multi-day maintenance packet scenarios. To make the model computationally manageable, we apply various linearization techniques to refine the model into a linear one. A real-world case is considered to demonstrate the proposed solution approach, which indicates that the model can be solved to the optimum within a very short computational time using a state-of-the-art optimization solver.

Optimizing the Preventive Maintenance Scheduling Based on Dynamic Deterministic Demand in The Cement Manufacturing

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.

Optimal State Risk Scheduling Based on Selective Maintenance Strategy

Because stochastic fault cases (i.e., opportunistic maintenance strategy) of the equipment are not considered in the condition-based maintenance decision of the system, which will deviate from the actual situation, a system condition-based maintenance scheduling model considering opportunistic maintenance strategy is proposed in this paper. To implement the system maintenance strategies, the correlation set is formulated by considering the relationship among different equipment. According to renewal process theory, the availability of the correlation set considering planned maintenance and opportunity maintenance is deduced and the maintenance strategy of the system is realized. Then, to reflect the relationship between equipment maintenance and system operation, a system state scheduling model aiming at minimizing the sum of maintenance risk and failure risk as well as considering system resource constraints is proposed, thus obtaining the optimal maintenance schedule based on system state. Finally, a simple test system and IEEE-RTS79 node system are employed to demonstrate the feasibility and effectiveness of the proposed maintenance model, and it also verified that the proposed model can be integrated into the power system condition-based maintenance theory.

Optimal multiple replacement and maintenance scheduling in two-unit systems

This paper models and optimizes the replacement and maintenance schedule (RMS) for a heterogeneous two-unit standby system. The system must accomplish a specified amount of work for a successful mission. When one unit executes the specified mission task, the other unit undergoes a certain type of maintenance based on the RMS, followed by staying in a warm standby mode if its next activation time is not up. The mission fails if both units fail before the task completion, or if one unit fails while the other one undergoes maintenance and thus is not ready to be activated and take over the task timely. A probabilistic model-based method is proposed to evaluate the mission success probability (MSP) of the considered system that may undergo multiple preventive replacements and different types of maintenance actions during the mission. The optimal RMS problem is then formulated and solved, which determines durations of operation periods of the activated unit and types of maintenance actions performed by the inactivated unit during its idle time for maximizing the MSP. A detailed case study on an oil transfer system with two heterogeneous pumps is provided to illustrate the proposed methodology and influences of multiple model parameters on the optimal RMS and corresponding MSP.

Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning

Preventive maintenance and production scheduling are two important and interactive activities in production systems. In this work, the integrated optimization problem of production scheduling for multi-state single-machine production systems experiencing degradation processes is investigated. Preventive maintenance tasks and jobs scheduling are jointly considered to find the optimal production policy by considering the processing costs, the maintenance costs, and the completion rewards, simultaneously. We formulate the integrated optimization problem as Markov decision process framework. R-learning algorithm is introduced to maximize the long-run expected average rewards per time unit over infinite horizon. On the basis of the analysis of the optimal stationary policy, the appropriate condition to perform preventive maintenance following optimal stationary policy is presented. This provides the basis for the improvement in R-learning algorithm. Furthermore, a novel heuristic reinforcement learning method is proposed to deal with the integrated model more efficiently. Finally, we present the simulation results and analysis of the proposed algorithm's performance in terms of the number of job types and machine states. The simulation results and analysis show the effectiveness of the proposed approach for solving the integrated problems.

Optimal production and maintenance scheduling for a degrading multi-failure modes single-machine production environment

Two important aspects of manufacturing systems are production scheduling and maintenance planning. These aspects are interdependent, but in most research work, this dependency is ignored. This paper proposed an integrated mathematical model for joint production scheduling and maintenance planning for a degrading multi-failure single-machine manufacturing system, in which the machine has discrete deterioration states. The machine is subject to different failure modes. The first failure mode is the machine’s full deterioration, which is detected at the end of a job’s processing, and the other failure mode is random breakdowns, which are detected at the time of failure. Two machine deterioration state-based thresholds are considered, and five different maintenance actions may carry out a replacement, preventive, and corrective perfect or imperfect maintenance. Since the machine’s states’ transitions follow an exponential distribution, a closed-form matrix-based mathematical model with probabilistic input parameters is presented. This paper aims to optimize the total system’s cost, including the maintenance cost, machine energy consumption cost as well as the makespan penalty for exceeding a pre-determined threshold. The proposed model determines the optimal jobs’ sequence as well as the machine’s deterioration state-based thresholds. Due to the complexity of the developed model, a genetic algorithm (GA), simulated annealing (SA) algorithm, and a teaching–learning-based optimization (TLBO) algorithm have been used to solve the presented model. The algorithms are validated using a full enumeration technique, and the model is validated by applying different maintenance strategies. Our results demonstrate the superiority of the GA compared to the other algorithms.

Numerical analysis of temporal effect of ballast shoulder cleaning

The paper presents a numerical analysis to investigate the temporal effect of shoulder cleaning on ballast drainage performance at various fouling conditions. Two parameters, the fouling index (FI) and the fouling profile (FP) are proposed to quantitatively represent the fouling conditions. A computational fluid dynamics (CFD) model including the Darcy-Forchheimer’s law to take into account the effect of porous medium is developed. Prior experimental results are used to extract model configuration parameters for improved simulation accuracy. Parametric simulations are carried out to evaluate flow characteristics through the ballast layer before and after shoulder cleaning for different FIs and FPs. Service life-dependent mathematical models of FI and FP are also introduced to capture the temporal effect of shoulder cleaning on drainage performance. Our analysis indicates that the optimal maintenance schedule needs to be decided with a balanced consideration of both the drainage health and the potential of drain restoration during the service life.

Optimal operation and maintenance scheduling in m-out-of-n standby systems with reusable elements

Intensive research efforts have been dedicated to reliability analysis and optimization of standby systems. However, only few of the existing models allow a system element to be used/activated more than once during the mission time, and the existing work assumed a 1-out-of-n structure and fully reliable element activation procedures. This paper advances the state of the art by modeling and optimizing a m-out-of-n standby system subject to preventive replacements and imperfect element activations. Each element may be activated to execute the mission task and subsequently be preventively replaced multiple times during the mission; depending on the length of idle time, the element stays on standby or undergoes protection or maintenance actions prior to its next activation. Probabilistic approaches are suggested to evaluate mission success probability (MSP) and expected mission cost (EMC) for the considered standby system. Two optimization problems are solved, which determine the optimal operation and maintenance schedule (OMS) maximizing MSP and minimizing EMC, respectively. Effects of different model parameters on the MSP and optimization results are examined through examples. The proposed methodology is also extended to a generalized m-out-of-n standby system with heterogenous element performances, where the number of operating elements m may vary during the mission to meet a certain system demand.

Reliability and Safety Improvement of Emission-Free Ships: Systemic Reliability-Centered Maintenance

The power system of an all-electric ship (AES) establishes an independent microgrid using the distributed energy resources, energy storage devices, and power electronic converters. As a hybrid energy system (HES), the power system of an AES works as a unified system where each part can affect the reliability of the other parts. The systemic reliability centered maintenance (SRCM), which efficiently enhances the reliability and safety of the AES by identifying optimal maintenance tasks of the AES, is considered in this article to apply to the entire system. In order to calculate the reliability and optimal maintenance schedule, the Markov process and Enhanced JAYA (EJAYA) are utilized. A layer of protection analysis (LOPA), which is a risk management technique, is adopted to assess the safety of the system. A hybrid molten carbonate fuel cell, photovoltaic (PV), and lithium-ion battery are considered as energy sources of the AES. Based on two common standards, DNVGL-ST-0033 and DNVGL-ST-0373, the suggested maintenance planning method can be used in industrial applications. Eventually, in order to validate the proposed method, a model-in-the-loop real-time simulation using dSPACE is carried out. The obtained results show the applicability and efficiency of the proposed method for improving reliability and safety.

Optimal generation maintenance scheduling considering financial return and unexpected failure of distributed generation

Generation maintenance scheduling (GMS) is an important factor that can improve the reliability of power systems and decrease revenue achieved by generation companies (GenCos). Several GMS models, therefore, are based on these two crucial values. Nonetheless, there is no GMS problem that simultaneously considers unexpected failure of distributed generator (DG), financial return of GenCo, and reserve of system. This paper proposes the GMS model based on a global criterion approach to compromise functions that maximise the GenCo’s annual return and probability that no DG fails unexpectedly. The system reserve (SR) is considered as a reliability constraint, while surplus reserve is exchanged with the main grid. To support alternative energy sources that have uncertain outputs and ensure continuous operation of DG, short-term GMS model including power from wind farm, photovoltaic system, energy system storage, and demand response (DR) is also run by adding the SR and inconstant cost of DR. Effectiveness of the proposed model is examined using the IEEE 6 and IEEE 18-bus test systems. Results show that not only the proposed model provides a better GMS solution for the GenCo, resulting in appropriate values of the two objectives, but also alternative energy sources are useful for the short-term GMS.

Optimal Maintenance Scheduling of Transmission Assets in the Brazilian Electric System

An electric utility has as an objective to guarantee the continuity and quality of the service provided. In order to enforce an increase in the quality of service, the ANEEL (Portuguese for The Brazilian National Electric Energy Agency) introduced penalties in contracts with public electricity transmission utilities in case transmission facilities are taken out of service due to accidents, failures or scheduled maintenance. Traditional maintenance approaches to electric power transmission systems are based on actions taken periodically. Although these approaches aim to improve the performance of these systems, usually they do not accurately assess the impact of maintenance actions on their reliability related to the resources employed, as well as the resulting legal penalties. This paper proposes an approach for addressing this problem based on reliability/cost to find the best strategies for performing maintenance on electric power transmission equipment (assets), with a mathematical model that provides a feasible maintenance calendar while respecting the constraints of the electrical system operation. The proposed method consists of a combination of a constructive heuristic with dynamic programming, to find the best solutions for a given planning horizon.

Assessment of the effect of biofilm on the ship hydrodynamic performance by performance prediction method

Biofouling represents an important problem in the shipping industry since it causes the increase in surface roughness. The most of ships in the current world fleet do not have good coating condition which represents an important problem due to strict rules regarding ship energy efficiency. Therefore, the importance of the control and management of the hull and propeller fouling is highlighted by the International Maritime Organization and the maintenance schedule optimization became valuable energy saving measure. For adequate implementation of this measure, the accurate prediction of the effects of biofouling on the hydrodynamic characteristics is required. Although computational fluid dynamics approach, based on the modified wall function approach, has imposed itself as one of the most promising tools for this prediction, it requires significant computational time. However, during the maintenance schedule optimization, it is important to rapidly predict the effect of biofouling on the ship hydrodynamic performance. In this paper, the effect of biofilm on the ship hydrodynamic performance is studied using the proposed performance prediction method for three merchant ships. The applicability of this method in the assessment of the effect of biofilm on the ship hydrodynamic performance is demonstrated by comparison of the obtained results using the proposed performance prediction method and computational fluid dynamics approach. The comparison has shown that the highest relative deviation is lower than 4.2% for all propulsion characteristics, lower than 1.5% for propeller rotation rate and lower than 5.2% for delivered power. Thus, a practical tool for the estimation of the effect of biofouling with lower fouling severity on the ship hydrodynamic performance is developed.

A maintenance scheduling optimization model for a multi-component machine in a digitalized manufacturing context

Abstract Manufacturing systems are facing a digital transformation that supports data-driven decision making and the improvement of the efficiency of condition-based or predictive maintenance systems. In this context, international standards and previous works related to the architecture of condition-based maintenance (CBM) systems are available, and the interest in maintenance scheduling optimization, triggered by failure predictions, are increasing. This work is framed within the scope of the PICOePRO project and considers a critical multi-component welding machine. This paper first provides a predictive maintenance architecture, coherent with the OS A-CBM standard and proposed within the project, and then develops one of the modules of this system, in which the main component is a maintenance scheduling optimization model. The defined architecture allows real-time decision-making, and the proposed model minimizes the machine maintenance cost, exploring the potential for cost-saving by grouping maintenance and using machine downtime to perform maintenance activities. The functioning of the module and the potential gains from the adoption of the developed model are illustrated using a numerical example. The architecture and the model provide a viable approach to guide maintenance decision-making in the era of digitalized manufacturing.

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

The contribution deals with the optimization of a sequential preventive maintenance schedule of a technical device. We are given an initial time-to-failure probability distribution, model of changes of this distribution after maintenance actions, as well as the costs of maintenance, of a device acquisition, and of the impact of failure. The maintenance timing and, eventually, its extent, are the matter of optimization. The objective of the contribution is two-fold: first, to formulate a proper (random) objective function evaluating the lifetime of the maintained device relatively to maintenance costs; second, to propose a numerical method searching for a maintenance policy optimizing selected characteristics of this objective function. The method is based on the MCMC random search combined with simulated annealing. It is also shown that such a method is rather universal for different problem specifications. The approach will be illustrated on an artificial example dealing with accelerated lifetime after each maintenance action.

A Tri-Objective Model for Generator Maintenance Scheduling

The complexity of power systems is increasing as new generating units are added to power systems in order to supply power to the growing economies. This has resulted in further research into the generator maintenance scheduling (GMS) problem which seeks to ensure optimal preventive maintenance scheduling that is effective and reliable. This research is focused on developing a generator maintenance schedule using a tri-objective model. The GMS tri-objective model is solved using two solution methodologies. The first is an exact solution method using mathematical modelling software, Advanced Interactive Multidimensional Modelling System (AIMMS). The second solution method is a recently developed metaheuristic algorithm called Exchange Market Algorithm (EMA). Results show that the tri-objective model finds a trade-off solution of the individual solution methods. The metaheuristic algorithm gives a better solution for larger optimization problems.

Optimising preventive maintenance schedule for a distillery plant

In today's era of automation, the maintenance of complex systems is necessary for obtaining high payback ratios. Time has changed the thinking of plant/maintenance managers from the fix-it-broken approach to the preventive maintenance approach for bringing back deteriorated components/systems to predetermined operational conditions. But since resources are limited therefore it is required to achieve an effective maintenance approach to minimise the total maintenance cost and equipment downtime. This paper presents the framework for deciding the optimal schedules for preventive maintenance under constraints of the maintenance cost, availability and revenue generation. The programming package Mathematica is used to solve the complex equations of the framework and finding the optimal preventive maintenance schedule. The practical case of a distillery plant is considered to gauge the effectiveness of this approach.

Developing an Exhaustive Optimal Maintenance Schedule for Offshore Wind Turbines Based on Risk-Assessment, Technical Factors and Cost-Effective Evaluation

Developing an Exhaustive Optimal Maintenance Schedule for Offshore Wind Turbines Based on Risk-Assessment, Technical Factors and Cost-Effective Evaluation

Maintenance Schedule Optimization Applied to Large Hydroelectric Plants: Towards a Methodology Encompassing Regulatory Aspects

The maintenance schedule planning of hydro plant generating units is a subject of great interest to several agents in the energy industry. A correct approach for this problem can prevent the degradation of physical assets and minimize the probability of forced shutdowns of their equipment. In addition to these factors, due particularities of the Brazilian system, the operational strategies of its agents are also affected by the maintenance schedule of the generating units. This occurs due to the Availability Factor (AFA), which is directly influenced by the hours of maintenance performed at the plant and, in case of a performance below the stipulated in the concession contracts, it can lead to financial losses or administrative sanctions applied by the regulatory agent. With this motivation in mind, the present work proposes a methodology for Generator Maintenance Scheduling (GMS) of a hydroelectric plant, developing a mathematical model to determine the ideal moment to perform maintenance, considering operational restrictions and regulatory aspects of hydroelectric plants. The optimization methodology proposed for this problem is done through mixed-integer linear programming, where the integer variables consist of the operating state and start date of maintenance of each generating unit. In the end, to validate the proposed modeling, a case study is carried out for a real large plant in the Brazilian system.

Optimization of Gas Turbine Maintenance Scheduling in PLN Tanjung Datuk Pekanbaru

This study discusses determining the optimal scheduling for maintenance of gas turbine engines in PLN Tanjung Datuk Pekanbaru. The optimal maintenance scheduling is done on critical components, namely turbine blade and AVR (Automatic Voltage Regulator) using Monte Carlo simulation. The optimal scheduling maintenance scenario is done by generating random numbers from MTTF (Mean Time To Failure) and MTTR (Mean Time To Repair) values and data validity testing. The results of research for optimal checking of turbine engines are once every 10 days with the reliability of turbine engines 43%. The optimal time for repairing a gas turbine in case of damage is 1.49 hours. The checking time for critical components of the turbine blade is 9 days and AVR of 12 days. The scenario of preventive maintenance is likely need special repair or replacement periodically that is 117 days for turbine blade components and 173 days for AVR.