Power Plant Maintenance Scheduling Using Ant Colony Optimization

Maintenance cost contributes a large portion in total life-cycle cost of offshore wind systems and improves the efficiency of maintenance activities that has emerged as an effective solution in enhancing cost-effectiveness of offshore wind energy. This study proposes a mathematical model to determine the optimal maintenance schedules, by highlighting the deterioration process of offshore wind systems and accounting the optimal vessel routing of maintenance activities, maintenance cost therefore could be shifted to an optimal level. Firstly, reliability of each component is precisely analyzed with respect to the effect of previous maintenance activities. Then, an optimal individual maintenance schedule and a grouping maintenance schedule in daily manner are established based on reliability analysis and opportunity cost consideration. Finally, an optimal maintenance schedule approach accounting vessel routing is apparently presented to optimize the transportation cost of maintenance activities. Numerous influential factors such as system reliability, weather condition, maintenance resource, maintenance duration, production loss cost, and vessel related issues are deliberately examined to enhance the robustness and efficiency of the proposed approach. The experimental results investigating four different scenarios demonstrate that by applying the proposed approach, number of maintenance activity is lowered down, maintenance duration and vessel travelling distance are also significantly reduced in the range of 43.86% - 52.77%, and 52.90% - 58.35%, respectively. Therefore, the economic benefit on maintenance cost could be achieved in an interval of 15.15% - 23.25% by selecting the optimal maintenance schedule.

The importance of machine maintenance has been gradually recognized especially with the great attention in industrial sector. A company was named M is a manufacturing company which engaged in the industrial manufacturer of body pail cans. Previously, the process of machine maintenance at company M is to repair the machine when a problem occurs. This causes several machines to break down frequently and disrupt the production process. Furthermore, the purpose of this research is to determine the optimum and well-planned maintenance scheduling that can reduce the risk of-or prevent machine failures that may ruin the production process by doing the preventive maintenance in right time. Ant Colony Optimization (ACO) method was used in this research as maximizing the interval time between preventive maintenance periods before the trouble occurs based on previous breakdown data period as minimizing frequency of the task. In the principle of ACO, the required parameters are α, β, m, e, el. As a result of using ACO with the combination of parameters above, the optimal well-planned maintenance scheduling was obtained by using α=2, β=5, e=0.3, e1=0.96, and a number of ants needed. Finally, the optimizing of schedule maintenance has proposed in daily for next year period.

Maintaining a large fleet of aircraft can be very challenging due to the variety of aircraft models, ages, and operating environments. Usually, the maintenance activities are implemented in a few centered facilities with different levels of maintenance capabilities. For instance, the Air Force's Air Logistics Complexes (ALC) are responsible for maintaining the Air Force's aging aircraft fleets. Current static methods for maintenance planning are based on the long-established Programmed Depot Maintenance (PDM) interval method, which can result in schedule delays, low throughput, and aircraft availability shortfalls. The situations are further complicated due to the increasing age of aircraft, high operational demands, and overall lack of fiscal resources. This paper is to propose innovative condition-based maintenance scheduling methodologies by integrating complex data processing, feature extraction, prognostic algorithm, and maintenance scheduling optimization. The proposed framework of prognostic-based maintenance scheduling is able to provide tradeoff analysis in terms of key performance metrics such as command possession rate, cost, and capacity expansion. The optimized maintenance schedule based on fleet health status will lead to higher aircraft availability, lower unscheduled maintenance cost, and meeting the continuous improvement initiatives such as the transitioning from PDM-based maintenance to High Velocity Maintenance (HVM) paradigm. The research outcomes will lead to more predictable and efficient maintenance scheduling capability. A numerical example shows how the aircraft reliability and health information can be integrated into the maintenance scheduling and planning optimization.

According to the features of power system, a new mathematical model for distribution maintenance scheduling is proposed, which combines load path transfer and maintenance schedule time optimization problem. Network reconfiguration algorithm and genetic algorithm are used to solve load path transfer problem and maintenance schedule time optimization problem respectively. Due to the large number variables of the network reconfiguration, encoding with round line and feasible solution operator are adopted to decrease the searching space in the paper. The searching space is significantly reduced and the solution speed is raised. The proposed model and method are applied to IEEE33 test system, the result show that proposed method can obtain the most optimal maintenance schedule and the load transfer line with minimal network loss. The method observably reduces the power off lose which maintenance brings.

Finding an optimal maintenance schedule for generating units and securing the adequate amount of generation availability as a preventive measure to keep the power system reliable is becoming of great importance under the tight operating reserve margin in electricity markets. Therefore most recent research on maintenance scheduling concentrates on how to economically and reliably determine the optimal set of maintenance period for each generating unit while satisfying a variety of constraints given by a system operator. This study presents a methodology for optimal maintenance scheduling of generating units using a hybrid algorithm that combines a scatter search and a genetic algorithm. To verify the effectiveness of the proposed algorithm, a sample test system of 21 units is selected and the results are compared with those of the recent meta‐heuristic algorithms, including genetic algorithms, discrete particle swarm optimisation and modified discrete particle swarm optimisation. This study does a comparison between the proposed method and other conventional ones to determine the maintenance schedule for each generating unit. The proposed method shows a higher performance in the objective function evaluation than the conventional ones, and consequently yields a more optimal solution of the maintenance scheduling problem. The proposed method has also been explored on the IEEE reliability test system, and the results show that the proposed algorithm can provide a robust and consistent performance.

Organizations related to infrastructure, such as utilities and railway companies, manage a large number of facilities, the failure of which can have a huge impact on society. The cost of maintaining these facilities is a combination of regular maintenance costs and urgent recovery costs. Generally, the urgent costs are much higher than regular costs. Regular maintenance work should result in fewer sudden failures, and thus reduce these urgent costs. However, if the regular maintenance is too frequent, its cost becomes too high. Therefore, it is important to balance the regular and urgent costs to minimize the overall maintenance cost. We propose a maintenance schedule optimization method based on the failure probability distribution of the facilities. The total cost is mathematically modeled, with the regular maintenance schedule included via decision variables and the occurrence of failures modeled as stochastic variables. The stochastic total maintenance costs are evaluated using a Monte Carlo method, and a genetic algorithm is employed to optimize the maintenance schedule. The proposed method is evaluated using data provided by a Japanese railway company, and our results confirm that the method produces an excellent maintenance schedule. A statistical test shows there is a significant difference between the proposed and conventional methods.

Jet Dyeing Machine is a machine used in the textile industry to dye fabric materials with efficiency and large-scale production capacity. PT XXX faces a high frequency of damage and total lost time on several components of production machines, so proper maintenance and maintenance in accordance with standards needs to be carried out to reduce the frequency of damage and total lost time and extend the service life of components. The CPM approach can help in creating an effective maintenance and maintenance schedule so that production can run smoothly and efficiently. This study aims to plan maintenance schedules and preventive maintenance on production machines at PT XXX using the CPM approach. The results of the analysis show that routine maintenance and maintenance of machine components is very important to reduce the frequency of damage and total lost time that can have an impact on production losses. The maintenance schedule must be considered carefully so as not to interfere with production and not to interfere with the performance of the machine and maintenance personnel must have adequate knowledge and skills to carry out maintenance and maintenance properly in accordance with the established SOP. Total losses due to wasted lost time can be reduced by 24% from the original. Repair and replacement of components that often experience damage need to be done to reduce the frequency of damage and total lost time on PT XXX production machines.

An analytical method for determining the optimum maintenance of substations based on stochastic modeling is presented in this paper. The analytical method includes reliability modeling of operation states, and different types of maintenance for individual components, which provides more detail information comparing with traditional modeling. Importance analysis is performed to quantify the reliability contribution of each individual component towards a specific load point, which also gives the priority of a component's maintenance significance. Moreover, economical analysis is also included in this paper, which calculates the expected benefit that can be achieved for a load point with corresponding maintenance schedule. An application of using the proposed method for evaluating substation maintenance schedule is given. The optimal maintenance schedule and the corresponding expected reliability and benefit are presented in this paper.

A number of objective criteria for optimal maintenance scheduling of thermal generators is discussed. The criteria are based on generation operating cost, reliability indices, deviations from a desired schedule and/or constraints violation penalties. A comparison of the performance of all these criteria is presented by maintenance scheduling a realistic 30 thermal-unit system. Also, the relationships between the several realibility indices are dicussed.

Many of the components on a gas turbine are subject to fouling and degradation over time due to debris buildup. For example, axial compressors are susceptible to degradation as a result of debris buildup on compressor blades. Similarly, air cooled lube oil heat exchangers incur degradation as a result of debris buildup in the cooling air passageways. In this paper, we develop a method for estimating the degradation rate of a given gas turbine component that experiences recoverable degradation due to normal operation over time. We then establish an economic maintenance scheduling model which utilizes the derived rate and user input economic factors to provide a locally optimal maintenance schedule with minimized operator costs. The rate estimation method makes use of statistical methods combined with historical data to give an algorithm with which a performance loss rate can be extracted from noisy data measurements. The economic maintenance schedule is then derived by minimizing the cost model in user specified intervals and the final schedule results as a combination of the locally optimized schedules. The goal of the combination of algorithms is to maximize component output and efficiency, while minimizing maintenance costs. The rate estimation method is validated by simulation where the underlying noisy data measurements come from a known probability distribution. Then, an example schedule optimization is provided to validate the economic optimization model and show the efficacy of the combined methods.

The importance of preventive maintenance management has been gradually recognized specially with the great attention to the role of health technology management. Finding the optimal schedule to perform preventive maintenance for medical equipment is rarely considered in the literature. This research suggests using ant colony optimization method to solve the problem of finding the optimal preventive maintenance schedule. We developed 2 versions of the algorithm, both starting from a prioritized medical equipment list and differing in the heuristic function. The experimental results indicate the effectiveness of the ant colony optimization algorithm for this kind of problems.

Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.

Hydrocarbon supply chain (HCSC) is a complex network that extends from oil and gas fields to demand nodes. Integrating operation and maintenance activities along this complex network is crucial since the hydrocarbon industry is the most influential sector in the world economy, and any disruptions or variations in hydrocarbon product supply will affect the whole world economy. Therefore, effective and thoughtful maintenance extends the life of an asset and enhances its reliability. To prevent huge losses in production and ultimately satisfy customer needs, the maintenance jobs are preferred to be performed during times of low demand. Thus, operation planning and maintenance scheduling decisions are dependent and should be optimized simultaneously. Therefore, the aim of this study is to develop an integrated mathematical model for the operation and maintenance planning of the oil and gas supply chain. The utility of the proposed model has been demonstrated using the Saudi Arabian HCSC. The proposed model effectively produces optimal operation and maintenance schedule decisions. A sensitivity analysis was conducted to study the effect of critical parameters on the obtained results.

Maintenance scheduling of hydropower units is essential for ensuring the operational security and stability of large-scale cascaded hydropower systems and for improving the efficiency of water energy utilization. This study takes the Cascaded Hydropower System of the Lower Jinsha River (CHSJS) as a representative case, identifying four key challenges facing maintenance planning: multi-dimensional influencing factor coupling, spatial and temporal conflicts with generation dispatch, coordination with transmission line maintenance, and compound uncertainties of inflow and load. To address these issues, four strategic recommendations are proposed: (1) identifying and quantifying the impacts of multi-factor influences on maintenance planning; (2) developing integrated models for the co-optimization of power generation dispatch and maintenance scheduling; (3) formulating coordinated maintenance strategies for hydropower units and associated transmission infrastructure; and (4) constructing joint models to manage the coupled uncertainties of inflow and load. The strategy proposed in this study was applied to the CHSJS, obtaining the weight of the impact factor. The coordinated unit maintenance arrangements of transmission line maintenance periods increased from 56% to 97%. This study highlights the critical need for synergistic optimization of generation dispatch and maintenance scheduling in large-scale cascaded hydropower systems and provides a methodological foundation for future research and practical applications.

This paper considers a time window periodic maintenance strategy with different duration windows and job scheduling activities in a single machine environment. The aim is to minimize the number of tardy jobs through the integration of production scheduling and periodic maintenance intervals. A mixed-integer linear programming model (MILP) is proposed to optimize small-sized test instances. Furthermore, an ant colony optimization (ACO) algorithm is developed to solve larger sized test instances. Subsequently, to measure the efficiency of the solutions obtained by ACO, Moore's algorithm is also developed to benchmark with ACO. To test the efficiency and the effectiveness of the ACO algorithm, a set of data for small and large sized problems was generated in which several parameters were adopted and then ten replicates were solved for each combination. The small sized instances were solved by the MILP. Then, the results obtained showed that the proposed ACO was able to obtain the exact solutions within reasonable CPU times, thus, it outperformed the CPLEX solver with respect to CPU. The large sized instances were solved by the Moore's algorithm and compared to ACO. Then, the results obtained showed that the ACO outperforms Moore's algorithm for all the instances tested. It can be concluded that the developed ACOis very efficient and effective in solving the problem considered in this paper.