A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sectors,including healthcare,transportation,and energy,where maintenance efficiency and resource optimization are equally critical.

A Two-Stage Scenario-Based Robust Optimization Model and a Column-Row Generation Method for Integrated Aircraft Maintenance-Routing and Crew Rostering

Motivated by a critical issue of airline planning process,this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions.Following the route network scheme and generated flight timetables,aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management.This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints,rules,and regulations.Considering multiple locations for airline maintenance and crew bases,we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering(AMRCR)to achieve the minimum airline cost.One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights.Due to the fact that disruption scenarios are expressed discretely with a specified probability,and we provide adjustable decisions under disruption to deal with this disruption risk,we provide a Two-Stage Scenario-Based Robust Optimization(TSRO)model.In this model,here-and-now or first-stage variables are the initial resource assignment.Furthermore,to adapt itself to different disruption scenarios,the model considers some adjustable variables,such as the decision to cancel the flight in case of disruption,as wait-and-see or second-stage variables.Considering the complexity of integrated models,and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance,we apply the column and row generation(CRG)method that iteratively considers the disruption scenarios.The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability.To evaluate the proposed TSRO model,which solves the AMRCR problem in an integrated and robust manner,five Key Performance Indicators(KPIs)like Number of delayed/canceled flights,Average delay time,and Average profit are taken into account.As key results driven by conducting a case study,we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models.The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems.However,for large-scale instances the proposed TSRO model falls short in terms of computational efficiency.Conversely,the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level.

Joint optimization of maintenance inspection and spare provisioning for aircraft deteriorating parts

With the wide application of condition based maintenance(CBM) in aircraft maintenance practice, the joint optimization of maintenance and inventory management, which can take full advantage of CBM and reduce the aircraft operational cost, is receiving increasing attention. In order to optimize the inspection interval, maintenance decision and spare provisioning together for aircraft deteriorating parts, firstly, a joint inventory management strategy is presented, then, a joint optimization of maintenance inspection and spare provisioning for aircraft parts subject to the Wiener degradation process is proposed based on the strategy.Secondly, a combination of the genetic algorithm(GA) and the Monte Carol method is developed to minimize the total cost rate.Finally, a case study is conducted and the proposed joint optimization model is compared with the existing optimization model and the airline real case. The results demonstrate that the proposed model is more beneficial and effective. In addition, the sensitivity analysis of the proposed model shows that the lead time has higher influence on the optimal results than the urgent order cost and the corrective maintenance cost, which is consistent with the actual situation of aircraft maintenance practices and inventory management.

Availability-based maintenance optimization under unequal inspection period and imperfect repair

Taking the multi-component system as research object, a maintenance optimization model based on the unequal inspection period and imperfect repair is established by considering the requirement of expected availability for improving the system＇s availability. An age reduction factor is used to describe the effect of imperfect repair, and the modelling approach for the unequal inspection period is proposed. Unavailable situations are classified into three kinds of independent cases, and the availability is calculated accordingly. Based on the analysis of the relationship between the unavailable cases and the unequal inspection period, an optimization model under imperfect repair is established to optimize the system＇s expected availability. A case study of a wind turbine is provided, and three key components, i.e. gearbox, generator and spindle, are considered. The optimization results of the unequal inspection period model and the equal inspection period model are compared. The results show that the unequal inspection period model based on availability can update the maintenance plan so as to optimize maintenance activities and improve the system＇s availability.

Joint optimization of inspection, maintenance, and spare ordering policy considering defective products loss

This paper proposes a joint inspection-based maintenance and spare ordering optimization policy that considers the problem of integrated inspection,preventive maintenance,spare ordering,and quality control for a four-state single-unit manufacturing system.When an inspection detects a minor defect,a second phase inspection is initiated and a regular order is placed.Product quality begins to deteriorate when the system undergoes a severe defect.To counter this,an advanced replacement of the minor defective system is carried out at the Jth second phase inspection.If a severe defect is recognized prior to the Jth inspection,or if system failure occurs,preventive or corrective replacement is executed.The timeliness of replacement depends on the availability of spare.We adopt two modes of ordering:a regular order and an emergency order.Meanwhile,a threshold level is introduced to determine whether an emergency order is preferred even when the regular order is already ordered but has not yet arrived.The optimal joint inspection-based maintenance and spare ordering policy is formulated by minimizing the expected cost per unit time.A simulation algorithm is proposed to obtain the optimal two-phase inspection interval,threshold level and advanced replacement interval.Results from several numerical examples demonstrate that,in terms of the expected cost per unit time,our proposed model is superior to some existing models.

Joint optimization of inspection-based and age-based preventive maintenance and spare ordering policies for single-unit systems

This paper presents a joint optimization policy of preventive maintenance(PM)and spare ordering for single-unit systems,which deteriorate subject to the delay-time concept with three deterioration stages.PM activities that combine a non-periodic inspection scheme with age-replacement are implemented.When the system is detected to be in the minor defective stage by an inspection for the first time,place an order and shorten the inspection interval.If the system has deteriorated to a severe defective stage,it is either repaired imperfectly or replaced by a new spare.However,an immediate replacement is required once the system fails,the maximal number of imperfect maintenance(IPM)is satisfied or its age reaches to a pre-specified threshold.In consideration of the spare’s availability as needed,there are three types of decisions,i.e.,an immediate or a delayed replacement by a regular ordered spare,an immediate replacement by an expedited ordered spare with a relative higher cost.Then,some mutually independent and exclusive renewal events at the end of a renewal cycle are discussed,and the optimization model of such a joint policy is further developed by minimizing the long-run expected cost rate to find the optimal inspection and age-replacement intervals,and the maximum number of IPM.A Monte-Carlo based integration method is also designed to solve the proposed model.Finally,a numerical example is given to illustrate the proposed joint optimization policy and the performance of the Monte-Carlo based integration method.

Multi-objective optimization in highway pavement maintenance and rehabilitation project selection and scheduling:A state-of-the-art review

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.

Optimal Maintenance Policy for a Storage System with Finite Number of Inspections

The expected cost per unit of time for a sequential inspection policy is derived. It still has some difficulties to compute an optimal sequential policy numerically, which minimizes the expected cost of a system with finite number of inspections. This paper gives the algorithm for an optimal inspection schedule and specifies the computing procedure for a Weibull distribution. Using this algorithm, optimal inspection times are computed as a numerical result. Compared with the periodic point inspection, the policies in this paper reduce the cost successfully.

Condition-based Maintenance Optimization for Gamma Deteriorating Systems under Performance-based Contracting

With the further development of service-oriented,performance-based contracting(PBC)has been widely adopted in industry and manufacturing.However,maintenance optimization problems under PBC have not received enough attention.To further extend the scope of PBC’s application in the field of maintenance optimization,we investigate the condition-based maintenance(CBM)optimization for gamma deteriorating systems under PBC.Considering the repairable single-component system subject to the gamma degradation process,this paper proposes a CBM optimization model to maximize the profit and improve system performance at a relatively low cost under PBC.In the proposed CBM model,the first inspection interval has been considered in order to reduce the inspection frequency and the cost rate.Then,a particle swarm algorithm(PSO)and related solution procedure are presented to solve the multiple decision variables in our proposed model.In the end,a numerical example is provided so as to demonstrate the superiority of the presented model.By comparing the proposed policy with the conventional ones,the superiority of our proposed policy is proved,which can bring more profits to providers and improve performance.Sensitivity analysis is conducted in order to research the effect of corrective maintenance cost and time required for corrective maintenance on optimization policy.A comparative study is given to illustrate the necessity of distinguishing the first inspection interval or not.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Load Testing in Bridge Maintenance

Highway bridges are a crucial component in road transportation networks.These bridges must be maintained according to usage requirements regularly.Test results must be considered before devising a maintenance plan.Load testing is a vital method of assessing the quality and performance of highway bridges.The outcomes of these tests facilitate the formulation of maintenance plans.This article examines the definition of load testing,its significance,and the process of execution,with the goal of providing support for bridge inspection and maintenance.

Analysis of a POMDP Model for an Optimal Maintenance Problem with Multiple Imperfect Repairs

I consider a system whose deterioration follows a discrete-time and discrete-state Markov chain with an absorbing state. When the system is put into practice, I may select operation (wait), imperfect repair, or replacement at each discrete-time point. The true state of the system is not known when it is operated. Instead, the system is monitored after operation and some incomplete information concerned with the deterioration is obtained for decision making. Since there are multiple imperfect repairs, I can select one option from them when the imperfect repair is preferable to operation and replacement. To express this situation, I propose a POMDP model and theoretically investigate the structure of an optimal maintenance policy minimizing a total expected discounted cost for an unbounded horizon. Then two stochastic orders are used for the analysis of our problem.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines (WTs) to avoid breakdowns andreduce maintenance costs. Many maintenance models have been developed for WTs’ maintenance planning, suchas corrective, preventive, and predictive maintenance. Due to communities’ dependence on WTs for electricityneeds, preventive maintenance is the most widely used method for maintenance scheduling. The downside tousing this approach is that preventive maintenance (PM) is often done in fixed intervals, which is inefficient. In thispaper, a more detailed maintenance plan for a 2 MW WT has been developed. The paper’s focus is to minimize aWT’s maintenance cost based on a WT’s reliability model. This study uses a two-layer optimization framework:Fibonacci and genetic algorithm. The first layer in the optimization method (Fibonacci) finds the optimal numberof PM required for the system. In the second layer, the optimal times for preventative maintenance and optimalcomponents to maintain have been determined to minimize maintenance costs. The Monte Carlo simulationestimates WT component failure times using their lifetime distributions from the reliability model. The estimatedfailure times are then used to determine the overall corrective and PM costs during the system’s lifetime. Finally,an optimal PM schedule is proposed for a 2 MW WT using the presented method. The method used in this papercan be expanded to a wind farm or similar engineering systems.

Reliability-based Maintenance Optimization under Imperfect Predictive Maintenance

The reliability-based maintenance optimization model has been focused by the engineers and scholars but it has never been solved effectively to formulate the effect of a maintenance action on the optimization model. In existing works, the system reliability was assumed to be increased to 1 after a predictive maintenance. However, it is very difficult in the most practical systems. Therefore, a new reliability-based maintenance optimization model under imperfect predictive maintenance (PM) is proposed in this paper. In the model, the system reliability is only restored to R i (0<R i <1, i∈N, N is natural number set) after the ith PM. The system uptimes and the corresponding probability in two cases whether there is an unexpected fault in one cycle are derived respectively and the system expected uptime model is given. To formulate the system expected downtime, the probability of each imperfect PM number in one cycle is calculated. Then, the system expected total time model is obtained. The total expected long-term operation cost is composed of the expected maintenance cost, the expected loss due to the downtime and the expected additional cost due to the occurrence of an unexpected failure. They are modeled respectively in this work. Jointing the system expected total time and long-term operation cost in one cycle, the expected long-term operation cost per time could be computed. Then, the proposed maintenance optimization model is formulated where the objective function is to minimize the expected long-term operation cost per time. The results of numerical example show that the proposed model could scheme the optimal maintenance actions for the considered system when the required parameters are given and the optimal solution of the proposed model is sensitive to the parameters of effective age model and insensitive to other parameters. The proposed model effectively solves the problem of evaluating the effect of an imperfect PM on the system reliability and presents a more practical optimization method for the reliability-based maintenance strategy than the existing works.

Collaborative optimization of maintenance and spare ordering of continuously degrading systems

A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to ob- tain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.

Reliability Simulation and Design Optimization for Mechanical Maintenance

Reliability model of a mechanical product system will be newly reconstructed and maintenance cost will increase because failed parts can be replaced with new components during service, which should be accounted for in system design. In this paper, a reliability model and reliability-based design optimization methodology for maintenance are presented. First, based on the time-to-failure density function of the part of the system, the age distributions of all parts of the system during service are investigated, a reliability model of the mechanical system for maintenance is developed. Then, reliability simulations of the systems with WeibuU probability density functions are performed, the system minimum reliability and steady reliability for maintenance are defined based on reliability simulation during the life cycle of the system. Thirdly, a maintenance cost model is developed based on replacement rates of the parts, a reliability-based design optimization model for maintenance is presented, in which total life cycle cost is considered as design objective and system reliability as design constrain. Finally, the reliability-based design optimization methodology for maintenance is used to design of a link ring for the chain conveyor, which shows that optimal design with the lowest maintenance cost can be obtained, and minimum reliability and steady reliability of the system can satisfy requirement of system reliability during service of the chain conveyor.

An estimation method for direct maintenance cost of aircraft components based on particle swarm optimization with immunity algorithm

A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented. Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune selection mechanisms were used to prevent the undulate phenomenon during the evolutionary process. The algorithm was introduced through an application in the direct maintenance cost (DMC) estimation of aircraft components. Experiments results show that the algorithm can compute simply and run quickly. It resolves the combinatorial optimization problem of component DMC estimation with simple and available parameters. And it has higher accuracy than individual methods, such as PLS, BP and v-SVM, and also has better performance than other combined methods, such as basic PSO and BP neural network.

Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions

Condition-based maintenance(CBM) is receiving increasing attention in various engineering systems because of its effectiveness. This paper formulates a new CBM optimization problem for continuously monitored degrading systems considering imperfect maintenance actions. In terms of maintenance actions,in practice, they scarcely restore the system to an as-good-as new state due to residual damage. According to up-to-data researches, imperfect maintenance actions are likely to speed up the degradation process. Regarding the developed CBM optimization strategy, it can balance the maintenance cost and the availability by the searching the optimal preventive maintenance threshold.The maximum number of maintenance is also considered, which is regarded as an availability constraint in the CBM optimization problem. A numerical example is introduced, and experimental results can demonstrate the novelty, feasibility and flexibility of the proposed CBM optimization strategy.

Optimization of post-warranty sequential inspection for second-hand products

This paper considers an optimal sequential inspection schedule for a second-hand product after that the free nonrenewable warranty is expired. The length of warranty is prespecified and during the warranty period, the product is minimally repaired by the dealer when it fails. Following the expiration of the non-renewing warranty, the product is inspected and upgraded sequentially a fixed number of times at the expenses of the customer.At each inspection, the failure rate of the product is reduced proportionally so that the product is upgraded. The product is assumed to deteriorate as it ages and the replacement of the product occurs when a fixed number of inspections are rendered. In addition,the intervals between two successive inspections are assumed to decrease monotonically. The main objective of this paper is to determine the optimal improvement level to upgrade the product at each inspection so that the expected maintenance cost during the life cycle of the product is minimized from the perspective of the customer. Under the given cost structures, we derive an explicit formula to obtain the expected maintenance cost incurred during the life cycle of the product and discuss the method to find the optimal level of the improvement analytically in case the failure times follow the Weibull distribution. Numerical results are analyzed to observe the impact of relevant parameters on the optimal solution.

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.