Maintenance Scheduling Problem Research Articles

Integrated optimization of production and maintenance scheduling with third-party worker resource constraints in distributed parallel machines environment

Outsourcing machine maintenance to third parties has been a trend due to the increasing complex of machines and the benefits of maintenance outsourcing. However, this new phenomenon is ignored in previous studies pertaining to the integrated optimization of production and maintenance scheduling (IOPMS) problems, resulting the lack of theoretical guidance for production managers to formulate optimal scheduling schemes under this new trend. In this study, we investigate an IOPMS problem considering maintenance outsourcing in a distributed parallel machine environment, referred as IOPMSTW, which requires the use of third-party worker resources to perform preventive maintenance. Makespan and total cost are two optimization objectives. We first formulate the problem by developing a mixed integer linear programming. Then a memetic algorithm incorporating iterated greedy method (IG) is proposed to solve the IOPMSTW, in which an improved decoding method and a problem-dependent local search operator based on IG are designed to respectively ensure the feasibility of new generated individuals and improve the searching efficiency. The validity of proposed mathematical model is verified by CPLEX based on eight small instances. Based on 240 constructed instances, a set of comprehensive experiments are conducted. The results demonstrate that the local search operator improved the searching performance of the proposed algorithm by 100%. Comparison results with other well-known algorithms show that the proposed algorithm achieved the best results on more than 85% of the tested instances.

Machine hearing for predictive maintenance of BLDC motors

PurposeThe purpose of this paper is to approach predictive maintenance (PdM) of brushless direct current (BLDC) motors using audio signal processing and extracting statistical and spectral features to train classical machine learning (ML) models.Design/methodology/approachThe proposed methodology relies on classification predictive model that shows the motors prone to failure. To verify this, the model was implemented and tested with audio data. The trained models are then deployed to an Industrial Internet of Things (IIoT) application built using Django.FindingsThe implementation of the methodology allows for achieving performance as high as 92% accuracy, proving that spectral features should be considered when training ML models for PdM.Originality/valueThe proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for BLDC motors.

Air cargo transportation, loading, and phase-based maintenance service scheduling in demand channel routes

The article proposes an approach for modeling and solving the air cargo transportation, loading, and phase-based maintenance scheduling problems within demand channels. Demand channel transportation presents unique challenges to meeting the earliest pickup and delivery requirements during emergencies. The complexities in transportation involve issues with airport-aircraft compatibility, fleet selection, task assignments, route construction, and effective loading–unloading procedures. The challenges include coping with flight connections due to airport-aircraft compatibility, dealing with limited refueling and maintenance facilities, and swiftly adapting to unforeseen maintenance events by identifying and utilizing available fleets. In this context, the article delineates a demand channel route planning strategy that considers airport-aircraft compatibility, task integrity, priority, partial refueling strategy, and maintenance schedules spanning short to long-haul phases. Formulated as a Mixed Integer Linear Programming (MILP) problem, the solution employs a novel acyclic route construction procedure within a fuel-conscious network. We propose an insertion heuristic with multi-faceted algorithmic interconnections for producing a near-optimal solution suitable for real-world applications. Our insertion procedure has been shown to have very high computational performance, with an average 1.32 percent optimality gap. It can be scaled up to the most critical cargo mission, with up to 190 ports, 95 aircraft, and 380 tasks.

Scheduling maintenance activities subject to stochastic job-dependent machine deterioration

This paper considers a maintenance scheduling problem on a single machine with a fixed job sequence involving job-dependent, stochastic deterioration. If the machine breaks down, then a time-consuming emergency maintenance activity needs to be conducted. Instead, planned maintenance activities can be conducted between jobs in order to improve the machine’s state and, thus, prevent the machine from breaking down. We distinguish two types of machines differing in the flexibility regarding the point of time when the maintenance activities need to be scheduled. The goal is to minimize the total expected completion time of jobs. We formalize two optimization problems, analyze their computational complexities, develop exact solution approaches, and conduct a computational study. Here, we show that our approaches can solve large instances to optimality within short run times and we analyze the advantage gained by planning maintenance activities depending on the actual state of the machine.

A restless bandit approach for capacitated condition based maintenance scheduling

AbstractThis paper considers the maintenance scheduling problem of multiple non-identical machines deteriorating over time. The deterioration gradually decreases a machine’s performance, which results in revenue losses due to lower output quality. The maintenance cost is dependent on the degradation state, and the number of maintenance activities that can be carried out simultaneously is restricted by the number of maintenance workers. Our main goal is to propose a heuristic with low complexity that consistently produces solutions close to the optimal strategy for problems of real size. We cast the problem as a restless bandit problem and propose an index based heuristic (Whittle’s index policy) which can be computed efficiently. We also provide a lower bound that can be computed by linear programming. We numerically compare the performance of the index heuristic with alternative policies. In addition to achieving superior performance over failure-based and threshold policies, Whittle’s policy numerically converges to our lower bound when the number of machines is moderately high and/or maintenance workload is high.

An efficient generator maintenance scheduling model based on unit clustering and linear relaxation

The formulation of periodic maintenance schedules for generators is crucial for the efficient and reliable operation of power systems. Traditional generator maintenance scheduling models often have low temporal resolution and ignore significant information. To solve this problem, this paper proposes a mid/ long-term generator maintenance scheduling model with an hourly time resolution. Given the challenges of large integer variable quantities and computational complexity in directly solving mid/ long-term maintenance schedules for power system generation units, an efficient solution method is proposed based on unit clustering and linear relaxation. Initially, a basic maintenance model is established based on the Unit Commitment (UC) model. Subsequently, the basic UC maintenance model is linearly relaxed to obtain an efficient approximate model, termed the Relaxed Clustered Unit Commitment (RCUC) maintenance model, which involves the operation of annual regulating hydropower stations, energy storage devices, pumped storage stations, wind power plants, and photovoltaic (PV) plants. Simulation results from an adapted IEEE RTS-79 system and a provincial scale system case study are provided to demonstrate the effectiveness of the proposed model in coping with mid/long-term maintenance scheduling and annual hydropower allocation problems of large-scale power systems.

A NEW SOLUTION FOR MAINTENANCE SCHEDULING OF DISTRIBUTED GENERATIONS BASED ON MONTE-CARLO SIMULATION AND GAME THEORY

This paper presents a new solution for maintenance scheduling of Distributed Generation (DG) in deregulated power markets by applying an independent market for DG maintenance scheduling based on game theory. Maintenance scheduling is one of the important issues in restructured power electricity markets, because each distribution generation company desires to maximize its own payoffs and independent system operator (ISO) has its own reliability concerns that mostly, these two points of view are against with each other. The paper presents a dynamic Distribution Resources Maintenance Market (DRMM) based on dynamic game theory for resolving maintenance scheduling problem of distributed generation resources in deregulated environment. In this market, distribution generation companies are set their strategies to participate in DRMM by considering load, primary resources and seasonal forced outage rate uncertainties and behaviour of other DG companies that participate in market. On the other hand, ISO manages the maintenance market based on reliability and offering incentives /penalties for DG companies. This paper studied the ISO acts by using Monte-Carlo simulation for reliability indices assessment relying on load and resources uncertainties and interactions between DG companies. Numerical results are determined by using an applicable case study for testing accuracy and applicability of the new proposed solution.

Accounting for Traffic Dynamics in Pavement Maintenance Scheduling

Effective maintenance, rehabilitation, and reconstruction (MR&R) of road pavements is crucial for ensuring a safe and efficient transportation network. Existing research has proposed various models to aid decision-making in MR&R planning. However, these models often overlook the impact of traffic dynamics, such as shockwaves and queue spillovers caused by reduced capacity during maintenance activities. To bridge this gap, we present a novel methodology that incorporates traffic dynamics into the selection and sequencing of MR&R activities over a planning horizon. Our approach combines numerical evaluation of traffic flow theory using the link transmission model (LTM) with a deterioration model, considering travel time cost, fuel cost, and agency cost, to assess the optimal sequence of MR&R activities. To solve this multi-objective problem, we employ a genetic algorithm (GA) to minimize the objective function. Through a numerical example, we demonstrate the methodology’s effectiveness and sensitivity to pavement deterioration. The results clearly indicate the effectiveness of using the proposed methodology over the widely used Bureau of Public Roads (BPR)-function-based methods for pavement maintenance scheduling problems.

MAINTENANCE BRIGADE STRATEGIC PLANNING AND ORGANIZATIONAL CHANGE

This research paper addresses the problem of maintenance planning and scheduling in hospital maintenance. It discusses the effective utilization of mechanics in strategic maintenance planning advocates the use of relay-type of maintenance brigade system (MBS), a system-based mechanic assignment techniques based on self-balancing bucket system created by Bartholdi and Eisenstein was thus adopted and tested. The MBS was compared to traditional assignment techniques via mechanic availability and mechanic utilization each at different levels according to maintenance performance assessment values. The results obtained with MBS showed some improvements compared to the traditional method of maintenance. The MBS had performance, utilization and productivity scores of: 91.7%, 92.3%,93.2% respectively compared to 71.4%, 69.2%, 69.2% respectively. The replacement time to service power generating set1 using MBS was approximately eleven hours lesser than the actual time compared to the traditional method.

Dynamic inspection and maintenance scheduling for multi-state systems under time-varying demand: Proximal policy optimization

Inspection and maintenance activities are effective ways to reveal and restore the health conditions of many industrial systems, respectively. Most extant works on inspection and maintenance optimization problems have assumed that systems operate under a time-invariant demand. Such a simplified assumption is oftentimes violated by a changeable market environment, seasonal factors, and even unexpected emergencies. In this article, with the aim of minimizing the expected total cost associated with inspections, maintenance, and unsupplied demand, a dynamic inspection and maintenance scheduling model is proposed for Multi-State Systems (MSSs) under a time-varying demand. Non-periodic inspections are performed on the components of an MSS and imperfect maintenance actions are dynamically scheduled based on the inspection results. By introducing the concept of decision epochs, the resulting inspection and maintenance scheduling problem is formulated as a Markov Decision Process (MDP). The Deep Reinforcement Learning (DRL) method with a Proximal Policy Optimization (PPO) algorithm is customized to cope with the “curse of dimensionality” of the resulting sequential decision problem. As an extra input feature for the agent, the category of decision epochs is formulated to improve the effectiveness of the customized DRL method. A six-component MSS, along with a multi-state coal transportation system, is given to demonstrate the effectiveness of the proposed method.

Counterfactual-attention multi-agent reinforcement learning for joint condition-based maintenance and production scheduling

Maintenance and production scheduling are interactive activities that should be considered simultaneously to maintain production systems’ reliability and high production delivery rate. This study investigates a problem of joint maintenance and production scheduling in a multi-stage hybrid flow shop experiencing machine deterioration processes. To address the structural dependency among production stages and machines, a decentralized partially observable Markov decision process (Dec-POMDP) is considered. However, the large state and action space pose a challenge for existing reinforcement learning methods to provide satisfactory solutions. To overcome this problem, the study proposes a counterfactual attention multi-agent reinforcement learning (CAMARL) solution framework that comprises three functional modules: The attention mechanism module, which adaptively shrinks the dimension of the state space; the action abstraction representation module that deals with the high-dimension of the action space; and the coordination control unit, which accelerates the exploration for the optimal production policy. Numerical experiments demonstrate the effectiveness of the proposed method by comparing it with seven benchmarks under different production scenarios.

Resource allocation and maintenance scheduling for distributed multi-center renewable energy systems considering dynamic scope division

The continuity of activity and competition in the global renewable energy market has driven manufacturers to shift from centralized to decentralized structures and to develop multi-center service networks. Appropriate and timely preventive maintenance (PM) is essential for reducing the operation and maintenance (O&M) costs and promoting long-term stable operation for the equipment of distributed renewable energy systems. Traditionally, decision-makers separated the service network into regions and established regional centers for services. However, due to the dynamic characteristics of equipment degradation, the O&M requirement has shifted in real-time. This is an ambitious and challenging O&M task, which requires considering many aspects, including the equipment failure rate, the availability of maintenance resources, the variability of PM requirements, and the long-term horizons of renewable energy system operations. In this paper, we develop a novel multi-center collaborative maintenance (MCM) strategy to solve this resource allocation and maintenance scheduling problem (RAMSP). And a hybrid solution approach is developed by decomposing the original problem and solving two subproblems sequentially. Numerical examples are used to illustrate the economic and computational advantages of the proposed maintenance framework. By effectively integrating the computational efficiency of hierarchical modeling and the quality benefit of holistic modeling, the proposed MCM strategy emphasizes 13% average cost saving and 70% average computation time reduction. The cyclic maintenance schemes with minimal total O&M service cost are obtained to support the system's proper operation, maintenance resource management, and service route optimization.

Service-oriented multi-skilled technician routing and scheduling problem for medical equipment maintenance with sudden breakdown

Motivated by the service-oriented intelligent maintenance activity under the development of the digital economy, we study the operation and maintenance strategies of the Maintenance Scheduling and Technician Routing Problem (MSTRP) for medical equipment implemented by multi-skilled technicians. We consider the maintenance services provided for both preventive maintenance (PM, e.g., periodic maintenance based on the contract schedule) and corrective maintenance (CM, e.g., repair action in the event of a sudden breakdown). As these activities are performed on equipment with various types of requirements at different geographically distributed systems, technicians with diverse qualifications need to build teams and visit the matching maintenance requests. Meanwhile, we consider the maintenance actions concerning the dynamic change in equipment failure rate and establish a flexible maintenance mechanism by balancing the total cost. This paper develops an efficient hybrid heuristic based on a genetic algorithm, variable neighborhood search, and other optimization strategies to solve this multi-period equipment maintenance scheduling and multi-skilled technician routing and scheduling problem. Based on the settings of the studied MSTRP, this paper designs different sizes of testing instances and multi-period explanatory cases before conducting extensive comparative experiments and sensitivity analysis to demonstrate the effectiveness of our proposed dynamic maintenance strategy and optimization method.

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

Modular-multilevel-converters (MMCs) are vital components in direct current transmission networks. Predictive maintenance scheduling of MMCs requires estimations of the failure probabilities of MMCs during a period of time in the future. Particularly, the predicted future failure probabilities are influenced by two main factors, the mission profiles of the MMCs and the maintenance decisions on the MMCs during the prediction period. This paper proposes a failure probability prediction model (FPM) for MMCs by considering these two factors. First, the expectations of the failure probabilities of the components for all the scenarios of mission profiles are obtained. Second, in predictive maintenance scheduling problems, the decisions to perform the maintenance actions are represented by binary variables. When the number of submodules is very large, using the binomial probability form currently used in reliability engineering to express the “r-out-of-n” failure probability of arms of the MMCs is intractable. Thus, this paper proposes a tractable form (T-form) in FPM by observing that the submodules on one arm are homogeneous. Furthermore, an approximation method, i.e., clustering and assignment (C&A), is proposed to reduce the computation times for calculating the parameters needed by the proposed T-form. Then, we perform a case study that assesses the accuracy and computation time of the C&A approach. The results show that the accuracy of the C&A approach is high and that the computation time is reduced significantly compared with the accurate method. We also show that the computation time for solving the predictive maintenance scheduling problem can be reduced hugely by using the T-form instead of the binomial probability form.

Simulating dynamic RUL based CBM scheduling. A case study in the railway sector

This paper uses continuous time computer simulation tools to analyze the maintenance scheduling problem of a fleet of assets that is subjected to a CBM (Condition-Based Maintenance) program for critical components under a maintenance 4.0 environment. Detection of component anomalies, their diagnosis and prognosis are considered as built-in capabilities of the organization. Once the remaining useful life (RUL) of a component at risk is known, the organization must react and determine when the component’s on-condition maintenance can be released. Maintenance on-condition activities are released controlling a variable named accumulated excess of anomalies over the CBM capacity constraint. When the existing CBM capacity is not enough to service all the components with their lowest possible RUL, components could be replaced with a higher RUL, as few times as possible, to ensure the largest component´s lifecycle. The paper explores the implications of different CBM capacity levels modelling a cost function. Cost function factors considered are cost of lost RUL, cost of CBM capacity, cost of overdue CBM and cost of asset unavailability due to overdue CBM. Empirically, the paper shows how capacity can be optimized to minimize this cost function. Once all different possibilities to schedule CBM activities are modeled, together with the cost of the selected CBM strategy, the paper compare results with those obtained for a base case where the organization could detect anomalies in components but not schedule CBM activities according to their RUL limitations and the maintenance organization capacity constraints. The paper demonstrates the different benefits of this opportunistic CBM task scheduling, according to assets stops for their predetermined PM activities. The tool that is developed has been tested in the railway sector, for a fleet of trains. Interesting results are obtained for different strategies, and they are discussed to understand possible implications of changes in the different factors and parameters of the problem.

Energy-conscious maintenance and production scheduling for single machine systems under time-of-use tariffs

In view of the joint optimization problem of preventive maintenance and production scheduling for modern production systems under time-of-use tariffs, a two-stage joint decision-making policy is proposed to achieve the peak-shifting reduction of production power. In the first stage, a dynamic preventive maintenance schedule is sequentially obtained based on the availability of machine. In the second stage, the production scheduling optimization of multi-workpiece processing is further carried out. The power consumption cost and the delay penalty cost under the time-of-use electricity tariff are considered, and the mixed integer programming model is established to achieve the balance of energy consumption and production delay. Numerical experiments have shown that by reasonably planning the idle time at the time of production batch conversion, the proposed model can effectively shift the on-peak power demand to off-peak, meet the stable electricity demand of enterprises, and improve the sustainable utilization level of power.

Optimization for a Joint Predictive Maintenance and Job Scheduling Problem With Endogenous Yield Rates

While job scheduling problems have been studied extensively, scheduling problems with endogenous yield rates that may be affected by predictive maintenance is not thoroughly investigated. In this study, we consider the optimization of a joint predictive maintenance and job scheduling problem for the minimization of total shortage penalty. As maintenance may be conducted to raise machine yield rates, machine production rates and job processing times become endogenous, and the optimization problem is different from traditional scheduling problems. We formulate a mixed integer program for this problem and develop a heuristic algorithm based on Tabu search. We demonstrate the effectiveness of our algorithm through numerical experiments and a way of estimating the yield declining rate with industry defect and maintenance records. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the real need of our industry collaborator, red electronics manufacturer. Every morning, the manufacturer chooses up to three out of eleven photolithography machines to conduct maintenance. Conducting maintenance helps raise machine yield rates to decrease the number of defects in expectation. However, the production schedule of some jobs must be postponed, and delay and shortage may arise. The decision is thus to schedule jobs as well as maintenance to find a balance between yield loss and shortage loss. We help the manufacturer by formulating an optimization model and develop an algorithm to solve the model. The algorithm may be applied to similar cases when one needs to schedule maintenance and production processes at the same time.

Modelling maintenance scheduling strategies for highway networks.

Although a wide range of literature has investigated the network-level highway maintenance plans and policies, few of them focused on the maintenance scheduling problem. This study proposes a methodology framework to model and compare two different maintenance scheduling strategies for highway networks, i.e., minimal makespan strategy (MMS) and minimal increased travel delay strategy (MITDS). We formulate MMS as a mixed integer linear programming model subject to the constraints of the quantity of manpower and the worst-first maintenance sequence. A bi-level programming model is proposed to quantify and optimize MITDS. The upper level model determines the optimal scheduling to minimize the increased traffic delays during the maintenance makespan. In the lower level, a modified day-to-day traffic assignment model is put forward to reflect the traffic evolution dynamics by simulating travelers’ route choice behaviors. A simulated annealing algorithm and augmented Lagrange algorithm are employed to solve the two proposed models, respectively. Finally, a numerical example using a highway network is developed. The two proposed strategies are tested considering different traffic demands, numbers of engineering teams, and travelers’ sensitivities to traffic congestion. The experiment results reveal that compared with MMS, MITDS extends makespan by 2 days though, it reduces the total increased travel delays by 4% and both MMS and MITDS can obtain the minimum total increased travel delays when the number of engineering teams is 6. The sensitivity analysis indicates that both the two strategies have the maximum and minimum total increased travel delays when the weight of prediction in travelers’ perception is 0.3 and 0.7, respectively. The proposed framework has the potential to provide reference in implementing highway maintenance activities reasonably.

Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem

This paper addresses a problem faced by maintenance service providers: performing maintenance activities at the right time on geographically distributed machines subjected to random failures. This problem requires determining for each technician the sequence of maintenance operations to perform to minimize the total expected costs while ensuring a high level of machine availability. To date, research in this area has dealt with routing and maintenance schedules separately. This study aims to determine the optimal maintenance and routing plan simultaneously. A new bi-objective mathematical model that integrates both routing and maintenance considerations is proposed for time-based preventive maintenance. The first objective is to minimize the travel cost related to technicians’ routing. The second objective can either minimize the total preventive and corrective maintenance cost or the failure cost. New general variable neighborhood search (GVNS) and variable neighborhood descent (VND) algorithms based on the Pareto dominance concept are proposed and performed over newly generated instances. The efficiency of our approach is demonstrated through several experiments. Compared to the commercial solver and existing multi-objective VND and GVNS, these new algorithms obtain highly competitive results on both mono-objective and bi-objective variants.

An approach to investigate the supplementary inconsistency between time series data for predicting road pavement performance models

ABSTRACT Road agencies commonly face high level complexity of comprehensive pavement maintenance scheduling complications. This could be due to uncertainty of some parameters that substance in the predicting models. Disregarding this uncertainty of such parameters in the road maintenance scheduling problems may lead to imprecise solutions and unreliable road conditions. Frequently time series data that have a hierarchical structure are used for predicting road surface or pavement structure distresses models. However, further realistic and accurate predicted models are those consider supplementary inconsistency between time series data with utilising a predictive approach that imposes knowledge of the input parameters. The purpose of this study is to test this theory by comparing the predictions of models developed using two approaches and their accuracies in utilising the same data. These approaches are the traditional regression model (TRM) with only one level of heterogeneity approach and the hierarchical regression model (HRM) with many levels of heterogeneities approach. A Metro North Western (MNW) urban road network in Victoria (Australia) was considered as the case study for this study investigation where covering the condition of approximately 305 Km of asphalt surfaced pavements. By considering the structure of time series data, the study revealed that more accurate models could predict the pavement behaviour, the deterioration rate, and significantly influenced parameters. In addition, the study highlighted that it is essential to deliberate and process the hierarchical structure of datasets to consider the observed and unobserved heterogeneities in time series data for developing any pavement performance model using either deterministic or probabilistic prediction approaches. Multiple simulation scenarios have been performed for both approaches and the HRM approach shows more reasonable outputs. For predicted deterministic SIR models, road intervention (SIR ≥ 20) are triggered at age 12.5 when using TRM approach and at age 10.5 when using HRM approach. For predicted probabilistic SIR models, the surface condition of a section with SIR weighted average condition values of 2 is triggered at age 17.5 when using TRM approach and at age 10.5 when using HRM approach. This also refers to earlier maintenance scheduling is required when using the later approach because it deals confidentially with each road sections time series data. In that regard, ignoring the uncertainty in time series data of such factors in the road surfacing maintenance scheduling problems may lead to suboptimal solutions and unstable pavement conditions.