Scheduling Of Maintenance Tasks Research Articles

Research on Optimization of Maintenance Task Scheduling for Metro Systems Based on Resource Constraints

Optimizing the maintenance scheduling of metro systems is a crucial task that necessitates meticulous coordination of labor, equipment, and workspaces to ensure optimal system performance and safety. A mathematical model and a two-stage teaching-learning-based optimization (TLBO)-resource operators crossover (ROC) algorithm are proposed aiming at optimizing the scheduling of maintenance tasks for metro systems. The mathematical model focuses on minimizing the makespan, which represents the total duration or time required to complete a set of tasks or activities within a project. In addition, it takes into account the need to balance the load on labor and workspaces, considering environmental constraints, limited resources, and strict scheduling requirements. A two-stage TLBO-ROC algorithm is specifically designed to enhance the scheduling process. It achieves this by iteratively updating the local best individual matrix, dividing it into groups, and adjusting the resource allocation. This algorithm effectively reduces the makespan while also achieving improved balance in the workspace load. The model and algorithm are tested on the Shenzhen metro system. Experimental results demonstrate that our proposed approach significantly reduces the makespan. In comparison to manual scheduling plans, the algorithm achieved a remarkable 28.06% reduction in the makespan. Moreover, when compared to benchmark algorithms, our proposed algorithm not only improves the makespan but also ensures more equitable occupation of workspaces by maintaining a similar balance in labor load.

An integrated framework of preventive maintenance and task scheduling for repairable multi-unit systems

Preventive maintenance and task scheduling are both crucial activities in industrial practice. Although they are interdependent and mutually influential, most existing studies typically analyze them separately. In this paper, an integrated framework of preventive maintenance planning and task scheduling is explored. On one hand, while most scholars focus on the system as a whole in scheduling studies, we further take into account the degradation and maintenance of multiple units during the task scheduling process. On the other hand, instead of only using "direct replacement" as the main maintenance method, we further consider the repairability of the unit by incorporating imperfect maintenance effects into the maintenance modeling. Additionally, the lifecycle partitioning method is proposed to analyze the maintenance groups for multiple units at the completion of scheduling tasks and their corresponding probabilities. Based on this foundation, an integrated decision model is established with the preventive maintenance threshold and scheduling task sequence as decision variables to minimize the total weighted expected completion time. Finally, a novel genetic algorithm based on hybrid encoding is proposed, and the effectiveness of integrated decision-making was validated using a practical example.

Machine Learning and Statistical Test–Based Culvert Condition Impact Factor Analysis

For managers of road infrastructure, culvert deterioration is a major concern since culvert failures can cause serious risks to the traveling public. The efficiency of the cost- and labor-intensive culvert inspection and maintenance process can be improved by properly identifying the key impact factors on culvert condition deterioration. Although the use of machine learning (ML) techniques to predict culvert conditions has been proven to be a promising tool for enhancing culvert management and enabling proactive scheduling of maintenance tasks, the information provided by the developed ML models has been given little attention for further use and analysis. By utilizing the predictor importance results of an evaluated decision tree (DT) culvert condition prediction model and the Mann–Whitney U test, this study provided insights to the identification of the key variables influencing culvert deterioration. According to the findings, five impact factors, including culvert span, pH, age, rise, and cover height, often have significant impact on the condition ratings of culverts made of various materials. In addition, such a statistical test-assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development.

A Data‐driven Optimization Model for Generating Robust Templates of Scheduling Offshore Wind Farm Maintenance Tasks

This paper studies the offshore wind farm maintenance scheduling from a new and more practical angle, generating templates of scheduling maintenance tasks robust to uncertain maintenance demand in scenarios with limited data. To realize such data-driven optimization of wind farm maintenance task scheduling, a distributionally robust optimization model with a ϕ-divergence-based ambiguity set is formulated to maximally meet the maintenance requirement while minimizing resource consumption under the worst-case distribution of maintenance demand. The proposed model can be easily transformed into its equivalent computationally tractable formulation and efficiently solved by off-the-shelf solvers. The ambiguity set is appropriately chosen by cross-validation techniques based on simulated data of maintenance demand. Computational results demonstrate that the proposed method can generate maintenance task schedules with a better out-of-sample performance compared to deterministic, stochastic programming, and robust optimization approaches given that only a small size of related data is available. Generated templates can support decision-making in scheduling maintenance tasks and can be particularly valuable to such needs for offshore wind farms having limited data collected.

Adaptive reinforcement learning for task scheduling in aircraft maintenance

This paper proposes using reinforcement learning (RL) to schedule maintenance tasks, which can significantly reduce direct operating costs for airlines. The approach consists of a static algorithm for long-term scheduling and an adaptive algorithm for rescheduling based on new maintenance information. To assess the performance of both approaches, three key performance indicators (KPIs) are defined: Ground Time, representing the hours an aircraft spends on the ground; Time Slack, measuring the proximity of tasks to their due dates; and Change Score, quantifying the similarity level between initial and adapted maintenance plans when new information surfaces. The results demonstrate the efficacy of RL in producing efficient maintenance plans, with the algorithms complementing each other to form a solid foundation for routine tasks and real-time responsiveness to new information. While the static algorithm performs slightly better in terms of Ground Time and Time Slack, the adaptive algorithm excels overwhelmingly in terms of Change Score, offering greater flexibility in handling new maintenance information. The proposed RL-based approach can improve the efficiency of aircraft maintenance and has the potential for further research in this area.

A Non-Intrusive Monitoring System on Train Pantographs for the Maintenance of Overhead Contact Lines.

The condition monitoring of an overhead contact line (OCL) is investigated by developing an innovative monitoring system for a pantograph on an electrical multiple unit of a regional line. Kinematic and dynamic modelling of the pantograph is conducted to support the designed monitoring system. The modelling is proved through rigorous test-rig experiments, while the proposed methodology is then validated through extensive field tests. The field tests serve a dual purpose: First, to validate the monitoring system using benchmark measurements of the tCat® trolley, and second, to assess the reproducibility of measurements in a realistic case. This paper presents the OCL monitoring system developed in the framework of the H2020 project SIA. The accuracy of our results is not far from that of other commercial systems, with just 12 mm of absolute error in the height measurement. Therefore, they provide reliable information about trends in various key performance indicators (KPIs) that facilitates the early detection of failures and the diagnosis of anomalies. The results highlight the importance of model calibration and validation in enabling novel health monitoring capabilities for the pantograph. By continuously monitoring the parameters and tracking their degradation trends, our approach allows for optimized scheduling of maintenance tasks for the OCL.

Single Objective Optimization Methods in Electrical Power Systems: A Review

Although the scheduling of maintenance tasks for generators is not a new issue, it has recently attracted new attention due to the significant rise in demand for expanding power system size in modern power systems. Generator Maintenance Scheduling (GMS) is a nonlinear optimization problem, highly dimensional and constrained, and determines when power-producing units must undertake well-planned preventative maintenance. The objective function includes binary variables to indicate whether a generator is undergoing maintenance at a given time and is subject to several restrictions described in this paper. However, the biggest concern of GMS is to produce a precise timetable for preventive maintenance of generating units with low cost and high reliability. Despite that, regrettably, a large volume of research works has accomplished solutions towards a model of GMS with the consideration of either maximizing system reliability or minimizing operation costs as an objective of their research work. This is called Single-Objective Problem (SOP), which involves one objective function that needs to be optimized. SOP is solved by Single-Objective Optimization Method (SOOM). The primary purpose of the research is to present a review of SOOM methods used in solving GMS problems.

A railway track reconstruction method using robotic vision on a mobile manipulator: A proposed strategy

Autonomous robot integration in railways infrastructure maintenance accelerates the digitization and intelligence of infrastructure survey & maintenance, providing high-efficiency and low-cost execution. This paper proposes a health assessment based on 3D reconstruction technology for railway track maintenance using a mobile robotic sensing platform. By combining multiple sensing and taking advantage of a robotic manipulator, a digital model of the target track components is built by a robot-actuated vision system which provides better 3D structural and surface condition reconstruction. Global geo-location and surrounding laser scanning are integrated to reinforce the digital completeness of the model for intelligent management. The new method consists of the following steps: First, according to scheduled maintenance tasks, a Robotics Inspection and Repair System (RIRS) navigates to the task location and uses the onboard depth camera for positioning. Then robot-mounted vision system is guided with an automated trajectory to build the 3D reconstruction of the track or repair object using the vision modeling technique. Finally, the 3D reconstructed model is fused with surrounding mapping of depth vision and Lidar scanning. Both laboratory tests and a realistic track test validated the feasibility of the proposed method by creating an accurate 3D reconstructed model. The modeled rail steel section size is quantitively compared with the ground truth in dimension, demonstrating good accuracy with a size error of less than 0.3 cm. The main contribution includes: (1) unmanned automatic 3D reconstruction by a robotic mobile manipulator, (2) the technique trims the reconstruction details & data to the specific maintenance goal or components, which supports the infrastructure maintenance towards the high-detailed & target-oriented digital management. This combination strategy of robotic automation and sensor fusion lies down a promising foundation for automated digital twin establishment for railway maintenance with autonomous RIRS, and upgrades technology readiness and digital intelligence for maintenance management

Deep-Learning-Driven Proactive Maintenance Management of IoT-Empowered Smart Toilet

The recent proliferation of Internet of Things (IoT) sensors has driven a myriad of industrial and urban applications. Through analyzing massive data collected by these sensors, the proactive maintenance management can be achieved such that the maintenance schedule of the installed equipment can be optimized. Despite recent progress in proactive maintenance management in industrial scenarios, there are few studies on proactive maintenance management in urban informatics. In this article, we present an integrated framework of IoT and cloud computing platform for the proactive maintenance management in smart city. Our framework consists of: 1) an IoT monitoring system for collecting time-series data of operating and ambient conditions of the equipment and 2) a hybrid deep learning model, namely, convolutional bidirectional long short-term memory (CBLM) model for forecasting the operating and ambient conditions based on the collected time-series data. In addition, we also develop a naïve Bayes classifier to detect abnormal operating and ambient conditions and assist management personnel in scheduling maintenance tasks. To evaluate our framework, we deployed the IoT system in a Hong Kong public toilet, which is the first application of proactive maintenance management for a public hygiene and sanitary facility to the best of our knowledge. We collected the sensed data more than 33 days (808 h) in this real system. Extensive experiments on the collected data demonstrated that our proposed CBLM outperformed six traditional machine learning algorithms.

Developing and Extending Status Prediction Models for Railway Tracks Based on On-Board Monitoring Data

Assigning inspection trains to monitor track quality is a standard procedure for maintaining railway system safety. The main challenges lie in lacking time and resources to perform the inspections because of the increasing traffic nowadays. To overcome these challenges, many consider adopting the on-board monitoring (OBM) technique for performing the inspections. This technique assigns commercial trains, instead of traditional track recording vehicles (TRVs), to monitor the track status, allowing railway operators to perform more inspections without affecting the traffic and using expensive inspection trains as well. However, compared with TRV data, the new OBM data are of lower data quality and have fewer features, although they can be recorded more frequently. Therefore, new methods should be developed for effectively applying the new data. This study develops four models, namely the linear regression model, Markov model, ordinary Kriging model, and Kalman filter model, for predicting the track status based on the OBM data. Data collected from the Switzerland railway network are used for verifying the models. Results show that the proposed models can effectively predict the degradation of the track status in different ways and, therefore, assist railway operators in scheduling maintenance tasks.

Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range

Abstract. The number of wind farms and amount of wind power production in Europe, both on- and offshore, have increased rapidly in the past years. To ensure grid stability and on-time (re)scheduling of maintenance tasks and to mitigate fees in energy trading, accurate predictions of wind speed and wind power are needed. Particularly, accurate predictions of extreme wind speed events are of high importance to wind farm operators as timely knowledge of these can both prevent damages and offer economic preparedness. This work explores the possibility of adapting a deep convolutional recurrent neural network (RNN)-based regression model to the spatio-temporal prediction of extreme wind speed events in the short to medium range (12 h lead time in 1 h intervals) through the manipulation of the loss function. To this end, a multi-layered convolutional long short-term memory (ConvLSTM) network is adapted with a variety of imbalanced regression loss functions that have been proposed in the literature: inversely weighted, linearly weighted and squared error-relevance area (SERA) loss. Forecast performance is investigated for various intensity thresholds of extreme events, and a comparison is made with the commonly used mean squared error (MSE) and mean absolute error (MAE) loss. The results indicate the inverse weighting method to most effectively shift the forecast distribution towards the extreme tail, thereby increasing the number of forecasted events in the extreme ranges, considerably boosting the hit rate and reducing the root-mean-squared error (RMSE) in those ranges. The results also show, however, that such improvements are invariably accompanied by a pay-off in terms of increased overcasting and false alarm ratio, which increase both with lead time and intensity threshold. The inverse weighting method most effectively balances this trade-off, with the weighted MAE loss scoring slightly better than the weighted MSE loss. It is concluded that the inversely weighted loss provides an effective way to adapt deep learning to the task of imbalanced spatio-temporal regression and its application to the forecasting of extreme wind speed events in the short to medium range.

Airline maintenance task rescheduling in a disruptive environment

Airline maintenance task scheduling takes place in a disruptive environment. The stochastic arrival of corrective maintenance tasks and changes in both fleet and resource availability require schedules to be continuously adjusted. An optimal schedule ensures that all tasks are executed before their due date in both an efficient (at minimum use of ground-time) and a stable (limited number of schedule changes) manner. This paper is the first study to address disruption management for the hangar maintenance task scheduling problem, proposing a practical and efficient modeling framework. The framework comprises a mixed integer linear programming model for airline maintenance task rescheduling in a disruptive environment, in which task scheduling is constrained by the availability of resources. The model’s capabilities include creating and adjusting maintenance schedules continuously and dynamically reacting to new information when this becomes available. The modeling framework was tested in a case study provided by a large airline, and its performance was compared to the current practice of the airline. The results show that the proposed approach produces more efficient and stable results. A 3% ground time decrease was achieved, while the number of schedule changes in the last days before operations was decreased by more than half.

An Improved Optimization Algorithm for Aeronautical Maintenance and Repair Task Scheduling Problem

The maintenance of carrier-based aircraft is a critical factor restricting the availability of aircraft fleets and their capacity to sortie and operate. In this study, an aeronautical maintenance and repair task scheduling problem for carrier-based aircraft fleets in hangar bays is investigated to improve the maintenance efficiency of aircraft carrier hangar bays. First, the operational process of scheduling aeronautical maintenance tasks is systematically analyzed. Based on maintenance resource constraints and actual maintenance task requirements, a wave availability index and load balance index for the maintenance personnel are proposed for optimization. An aeronautical maintenance task scheduling model is formulated for carrier-based aircraft fleets. Second, model abstraction is performed to simulate a multi-skill resource-constrained project scheduling problem, and an improved teaching-learning-based optimization algorithm is proposed. The algorithm utilizes a serial scheduling generation scheme based on resource constraint advancement. Finally, the feasibility and effectiveness of the modeling and algorithm are verified by using simulation cases and algorithm comparisons. The improved teaching-learning-based optimization algorithm exhibits improved solution stability and optimization performance. This method provides theoretical support for deterministic aeronautical maintenance scheduling planning and reduces the burden associated with manual scheduling and planning.

Availability Analysis of the Critical Production System in SMEs Using the Markov Decision Model

Small and medium-sized enterprises (SMEs) in today’s world have numerous issues in ensuring the availability and safety of critical machines and their components in the working environment. As a result, SMEs considered planning and scheduling maintenance tasks to be a significant threat. The goal of this research is to identify the critical subsystem of the automobile spare parts production plant in the southern region of Tamil Nadu, India, and then to prioritize the maintenance activity and set up an architecture for autonomous preventive maintenance (PM) in SMEs that includes an optimal decision support system. The transition state diagram of the individual and simultaneous production system has been developed with the application of the Markov decision model approach. It was used to analyze the present variables of the production systems and forecast the optimal maintenance parameters such as the failure rate and the repair rate, through the production system’s availability analysis. This availability analysis reveals that system B (Piercing) is classified as the most critical because of the abrupt availability variation compared to all other production systems, concerning the corresponding maintenance parameters such as a failure rate of 0.0371, a repair rate of 0.7094, and the availability of the piercing system of 0.5056. Finally, the use of an autonomous PM management system and the most effective maintenance workforce has enhanced productivity and customer satisfaction in SMEs. The predictive maintenance management system has been further investigated to determine the real-time remaining useful life (RUL) of critical systems in the automobile spare parts manufacturing plant in the southern region of Tamil Nadu, India.

A Hierarchical Framework for Spares-Driven Maintenance Tasks’ Reviewing, Planning and Scheduling

This paper explores the impacts of reviewing the time to replace of spare parts under the lenses of reliability- and cost-based optimization with the attempt to reduce the cost of a maintenance plan within a harshly constrained maintenance tasks’ scheduling problem. A hierarchical procedure for a robust reliability prediction and scheduling of maintenance tasks is introduced to this purpose. This study takes inspiration from real applications of the preventive maintenance (PM) scheduling process on high-throughput packing and manufacturing lines counting hundreds of functional groups and thousands of components subject to failures and replacements. We investigate the role and the criticality of data entry in the not polynomial (NP)-hard cost-based and reliability-based scheduling problem proposed by Manzini et al. (R. Manzini, R. Accorsi, T. Cennerazzo, E. Ferrari and F. Maranesi, The scheduling of maintenance. A resource-constraints mixed-integer linear programming (MILP) model, Comput. Ind. Eng. 87 (2015) 561–568). The proposed reliability-based analysis aids the setting of proper time to failures for spare parts and combines with an analytical single-component and cost-based model to set which maintenance tasks are the most sensitive to the overall cost reduction of a maintenance plan.

Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics

The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. However, only few studies consider the integration of such prognostics into maintenance planning. In this paper we propose a dynamic, predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance. With our alarm-based maintenance framework, the costs with engine failures account for only 7.4% of the total maintenance costs. In general, we provide a roadmap to integrate imperfect RUL prognostics into the maintenance planning of a fleet of vehicles.

The Similarity Index to Decompose Two-Stage Stochastic Scheduling Problems

Two-stage stochastic scheduling problems often involve a large number of continuous and discrete variables, so fnding solutions in short time periods is challenging and computationally expensive. However, for online or closed-loop scheduling implementations, optimal or near-optimal solutions are required in real-time. We propose a decomposition method based on the so-called Similarity Index (SI). An iterative procedure is set up so that each sub-problem (corresponding to a scenario) is solved independently, aiming to optimize the original cost function while maximizing the similarity of the frst-stage variables among the scenarios solutions. The SI is incorporated into each subproblem cost function, multiplied by a penalty parameter that is updated in each iteration until reaching complete similarity in the frst-stage variables among all subproblems. The method is applied to schedule production and maintenance tasks in an evaporation network. The tests show that signifcant benefts are expected in terms of computational demands as the number of scenarios increases.

Maintenance Activities Optimization via Modelling Dedicated to Manufacturing-Distribution Systems: Selected Case Studies Discussion

Optimizing the maintenance activities coordination-scheduling can be a daily challenge in any technical system and becomes more difficult when the size of the system is large and economic and logistic variables are involved. In general, as we can guess, the coordination-scheduling process modelling can be defined as an optimization problem once the target or goal to be met and the system model is well known. In order to address the above statements, well-defined methodologies have been proposed to model any coordination-scheduling process. Among them, simulation-based risk assessment is a comprehensive, flexible, and adaptable methodology to solve this problem. In this paper, we generalize a risk assessment approach and analyze case studies in different domains where the methodological model remains the same, but the parameters, variables and system under study are constantly changing. The article builds, based on different case studies, a general, adaptive, and simple simulation-based risk assessment model to optimize the coordination scheduling of maintenance tasks, which is able to intuitively and easily calibrate data-driven variables and parameters using a machine learning approach. Maintenance activities optimization via modelling approach is dedicated to manufacturing-distribution systems and is presented based on selected case studies.

Model of Production System Evaluation with the Influence of FDM Machine Reliability and Process-Dependent Product Quality.

This paper investigates the Job Shop Scheduling Problem (JSSP) with FDM (Fused Deposition Modeling) machine unavailability constraints due to Predictive Maintenance (PdM) tasks, under the objective of minimizing the makespan, total tardiness and machine idle time. The Ant-Colony Optimization (ACO) algorithm is elaborated to deal with the JSSP. The reliability characteristics of the critical machine (FDM) influence the product as well as the production system quality. PdM periods are estimated based on historical data on failure-free times of the FDM machine components and deviations from the standards established for the key process parameters: infill density, layer thickness and extruder temperature. The standards for the key process parameters are identified based on investigation of the mechanical properties of printed elements. The impact of failure time and the number of nonstandard measurements of parameters on the quality of the Job Shop System (JSS) are observed. Failure rate of the FDM machine is corrected with the probability of a stoppage in the future period due to the “outlier” in measurements of any key parameters of the additive process. The quality robustness of production schedules increases with the disturbance-free operation of the FDM up to the peak value. After reaching the peak value the quality robustness decreases. The original issue of this paper is a model of scheduling production and maintenance tasks in a job shop system with an FDM machine as a bottleneck using ACO. Additionally, an original FDM-reliability model is also proposed. The model is based on weighted p-moving averages of the observed number of deviations from the norms, established for key process parameters such as fill density, layer thickness and extruder temperature.

A model-based reinforcement learning approach for maintenance optimization of degrading systems in a large state space

Scheduling maintenance tasks based on the deteriorating process has often been established on degradation models. However, the formulas of the degradation processes are usually unknown and hard to be determined for a system working in practices. In this study, we develop a model-based reinforcement learning approach for maintenance optimization. The developed approach determines maintenance actions for each degradation state at each inspection time over a finite planning horizon, supposing that the degradation formula is known or unknown. At each inspection time, the developed approach attempts to learn an optimal assessment value for each maintenance action to be performed at each degradation state. The assessment value quantifies the goodness of each state-action pair in terms of minimizing the accumulated maintenance costs over the planning horizon. To optimize the assessment values when a well-defined degradation formula is known, we customize a Q-learning method with model-based acceleration. When the degradation formula is unknown or hard to be determined, we develop a Dyna-Q method with maintenance-oriented improvements, in which an environment model capturing the degradation pattern under different maintenance actions is learned at first; Then, the assessment values are optimized while considering the stochastic behavior of the system degradation. The final maintenance policy is acquired by performing the maintenance actions associated with the highest assessment values. Experimental studies are presented to illustrate the applications.