Preventive Maintenance Actions Research Articles

Integrated production scheduling and maintenance planning in a hybrid flow shop system: a multi-objective approach

This study deals with a hybrid flowshop system with sequence-dependent setup times. Two objectives have been considered. Minimizing makespan for production purpose along with minimizing unavailability of the system for maintenance purpose are the objectives of this problem. Two meta-heuristics have been developed for the research problem. First one is a non-dominated sorting genetic algorithm-II (NSGA-II), while the second one is a hybridized NSGA-II (HNSGA-II), which is accompanied by a local search procedure to create better results. These two algorithms allow the decision maker to find compromise solutions between production objectives and preventive maintenance ones. Two decisions should be taken at the same time: finding the best assignment and sequence of jobs on machines in order to minimize the makespan, and deciding how often to perform preventive maintenance actions in order to minimize the system unavailability. Three approaches have been suggested for evaluation and comparison the efficiency of algorithms. The results indicate that the HNSGA-II presents better solutions compared to the ordinal NSGA-II in terms of objective functions viewpoint, while the results are obviously reversed in balance degree of achieving both objectives simultaneously.

Integrated maintenance–quality policy taking into account the economical impact of reworking activities

ABSTRACTIn this paper, we study integrated strategies joining maintenance and quality. We consider an imperfect manufacturing system composed of a single machine subject to an increasing random failure rate and producing conforming (first and second choice) and non conforming items. In order to decrease the failure rate and its impact on teh quality of output products, a preventive maintenance (PM) policy, block type, with minimal reapair is applied. Indeed, two different strategies are developed: The first strategy consists on selling top quality products at the best price, selling products with substandard quality at a lower price and rejecting non-conforming items. For the second strategy, we propose reworking activities for products with substandard quality and non-conforming items in order to be sold at the best price. We aim to establish the optimal number of batches produced N* before each PM action maximizing the total profit per time unit. These two strategies are studied for both an infinite and finite horizon. A comparative study between these two strategies for an infinite horizon is developed in order to identify the most economical strategy. Numerical and sensitivity studies are developed, inspired from an industrial case, in order to illustrate the proposed models.

Performance evaluation of a two-machine line with a finite buffer and condition-based maintenance

This paper considers a two-machine continuous flow manufacturing system with a buffer of finite capacity, and where the machines are subjected to failures, minimal repairs, and condition-based preventive maintenance. Each machine can degrade into several discrete states, which are characterized by different performance parameters, ranging from perfect functioning to complete shutdown. When a specified degraded state is exited, a preventive maintenance action is performed to restore the machine to one of the higher performance states. We evaluate the impact of preventive maintenance strategies for each machine, in order to determine their impact on production rate and total cost. A methodology is developed to analyze the complex trade-off between the contributions of preventive maintenance and of the buffer to the system performance. It is shown that preventive maintenance should be scheduled not to optimize the performance for each machine individually; its scheduling is best considered from the perspective of the manufacturing system as a whole. The analysis also reveals the importance of considering machine speeds when optimizing the choice of maintenance policy.

Scheduling preventive railway maintenance activities with resource constraints

In this paper, we focus on the scheduling of preventive railway maintenance activities. The objective is to keep the railway infrastructure in good operating conditions at low costs, also taking into account the limited available resources in what concerns crew members. Equipments degrade with usage and age and a good preventive maintenance program can greatly reduce their unreliability in the sense that expectable failures can be anticipated. We propose a mixed integer programming formulation for the problem of scheduling preventive railway maintenance activities and a Variable Neighborhood Search (VNS) algorithm to solve large instances of the problem.

Joint optimization of maintenance, buffers and machines in manufacturing lines

ABSTRACTThis article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.

Modelling age replacement policy under multiple time scales and stochastic usage profiles

This paper offers an original methodology to set multi-dimensional maintenance policies for machines whose aging processes require using multiple time scales. It can be considered a generalization of the traditional approach, that usually employs a unique time scale and sets a single age limit to carry out preventive maintenance actions. The methodology also considers situations in which a set of machines are operated using multiple usage profiles. We define usage profile as the relationship between the use of a machine in terms of one main time scale and another scale. In our case study the age of a mining haul-truck component can be best modeled as a combination of operating hours and load cycles since the last overhaul. We compare the results obtained with respect to using a single time scale policy. The comparison shows the importance of the bias in decision making that may arise due to incomplete modelling of the components' aging process.

Toward an Integrated Approach of HV and MV Circuit-Breakers Optimization Maintenance Planning and Reliability Assessment: A Case Study

This paper investigates technical and organizational tools to improve maintenance planning performances. Indeed, maintaining a high level of reliability and availability of a Medium Voltage electrical network protection system such as the Medium Voltage and High Voltage circuit-breaker and its numerical protection relay at a low operating expenses cost is one of the most critical and challenging tasks for MV electrical distribution network operators. This work has mainly two goals. Firstly, to propose an operating expenses budget function that evaluates the Planned Scheduled Preventive Maintenance Policy combined with a Condition-based maintenance fora real series-parallel multi-assets MV electrical distribution system with active redundancy under the reliability and the maintenance frequency visits of these components. Secondly, to implement an integrated genetic algorithm approach in order to look for the optimal perfect and planned preventive maintenance scheduling policy and condition-based maintenance that minimizes the maximum operating expenses cost of the entire system.The method determines the optimal schedule of preventive maintenance actions based on minimization both reliabilty and operating expenses costs. Conclusions and recommendations for practice are made on the basis of obtained results.

Optimizing upgrade and imperfect preventive maintenance in failure-prone second-hand systems

Upgrade operations are improvement actions that can be carried out on recovered end-of-life systems to rejuvenate them and make them fit for subsequent lifecycles. These operations are costly but improve the reliability of these second-hand systems and consequently can reduce their maintenance costs. In this paper, a mathematical model for the joint determination of the optimal acquisition age, upgrade level, and imperfect preventive maintenance strategy is developed for a second-hand system. The system is acquired and upgraded, if necessary, before being put into operation and preventively maintained each time its reliability reaches a minimum required reliability threshold. Preventive maintenance actions are imperfect and modeled using the hybrid hazard rate approach. Optimality conditions are derived and discussed for general cost structures of the acquisition and upgrade actions. A test case is provided to illustrate the validity of the proposed approach. Numerical experiments and sensitivity analysis are also conducted to investigate the interactions between the upgrade level decisions, the optimal maintenance policy decisions and the total costs incurred during the lifetimes of these refreshed second- hand systems.

A Smart Geofabric for Detecting Transportation Substructure Damage

Highways, railways, airport runways and levees (dikes) consist of multiple structural layers which ultimately rest on an earthen subgrade. Over time, erosion, nearby construction, weather, seismic activity and other causes can weaken or create gaps in the lower layers including the subgrade. Initially there is no sign of damage on the surface, until the structure subsides, buckles, or even collapses. This event implies, at a minimum, expenses for immediate repair. It may cause facilities to be shut down, and in the extreme may cause damage to equipment and human fatalities. At present there is no practical way to routinely monitor the condition of the earthen subgrade which supports a road, railway, dike or airport runway. In this paper we describe a "smart" geofabric which addresses this problem. Our inexpensive Smartroad Tools geofabric can be embedded into or below pavements and other infrastructure during construction or reconstruction. Subsequently, an easy- to-use sensor array can be used to verify the integrity of the subgrade. This process can provide alerts regarding immediate threats to public safety, as well as longitudinal data for optimizing preventive maintenance budgets and activities. This paper describes and presents the results from our second-generation laboratory prototype. We also describe plans for further stages of testing.

An uncertain programming model for preventive maintenance scheduling

PurposeThe purpose of this paper is to illustrate an uncertain programming model for scheduling of preventive maintenance (PM) actions. The PM scheduling, in which PM actions are performed under fixed intervals, is solved by grey systems theory.Design/methodology/approachThe paper applied the grey evaluation method based on triangular whitenization weight functions which includes two classes: endpoint evaluation method and center-point evaluation method.FindingsTwo methods give the same results based on endpoint and center-point triangular whitenization weight functions. For validation, the results were compared by Cassady’s method.Originality/valueThe scheduling of PM is crucial in reliability and maintenance engineering. Hundreds of parts compose complex machines that require replacement and/or repairing. It is helpful to reduce the outage loss on frequent repair/replacement parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency.

A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies

Even if more reliable than the past, the performance of modern manufacturing systems is still affected by machine’s deteriorations and breakdowns. As a consequence, adequate maintenance programs must be implemented to adequately satisfy demands during manufacturing stops due to unexpected failures or preventive maintenance (PM) actions. Despite production and maintenance are closely related issues, their joint optimization has become an important research topic just during the last decade. Therefore, the present paper proposes a model for the combined optimization of production/inventory control and PM policies with the aim of minimizing the total expected cost per unit time. The model is formulated referring to a continuous production system characterized by a random deteriorating behavior so that the presence of a buffer is considered to ensure a continuous products supply during interruptions of service caused by breakdowns or planned maintenance actions on the production system. Unlike the main part of the existing literature, non-restriction on the failure occurrence is here forced, namely that the manufacturing system may fail at any age within the production cycle as well as more than one failure may occur during the same period. A Simulated Annealing-based algorithm combined with a Monte Carlo simulation module is proposed as a resolution approach. The robustness of the developed algorithm is demonstrated by means of repeated runs of different simulated scenarios characterized by diverse sets of cost parameters. Results also confirm the effectiveness of the proposed three-level theoretical inventory profile.

Preventive maintenance for systems with repairable minor failures

Reliability modeling of repairable systems deals mostly with two types of repair. Perfect repair brings a system to “as good as new” state. Minimal repair, on the contrary, returns a system to the state immediately prior to failure. In this article, we consider perfect and imperfect preventive maintenance actions for a system subjected to minor and major failures. Minor failures are minimally repaired, whereas a major failure terminates the operational function of the system and can be considered as an end-of-life event. The preventive maintenance strategies that we propose and analyze increase mission success probability and extend the expected lifetime of the system. The modeling is illustrated with numerical examples.

Improved preventive maintenance in the framework of forecasting problem under subcontractor constraint

The paper presents a new improved preventive maintenance strategy for a forecasting problem of production and maintenance optimisation under subcontractor constraint. In order to satisfy the customer, the manufacturing system consists of a principal machine M1 and called upon subcontractor machine Ms. Knowing that both machines are subjected to random failures, failure rate of main machine increases with time and according to production rates. An improved preventive maintenance strategy is used for control of the machine M1, whereas subcontractor machine Ms is uncontrollable from preventive maintenance point of view. An analytic formulation of problem has been proposed in order to determine the economical production plans for M1 and Ms. An improved maintenance strategy (IMS) is developed in order to minimise the total production loss, when the subcontractor machine is unavailable. It consists of determining the best time to perform preventive maintenance actions taking into account production rates, history of M1 and the state of subcontractor machine Ms. Numerical results and sensitivity analysis are presented to highlight the performance measure and the usefulness of the IMS.

An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach

This paper investigates integrated two-stage assembly flow shop problem with preventive maintenance (PM) activities under the multi-objective optimisation approaches. Reliability models are employed to carry out the maintenance activities. This paper attempts to find the appropriate sequence of jobs on machines in order to minimise the makespan and determining when to perform the PM activities in order to minimise the system unavailability. As this problem is proven to be NP-hard two multi-objective optimisation methods that are named non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are employed to find the Pareto-optimal front. The parameters of proposed algorithms are calibrated by artificial neural network (ANN) and the performances of the algorithms on the problem of various sizes are analysed based on four metrics. The computational results reveal NRGA is statistically better than NSGA-II.

An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach

This paper investigates integrated two-stage assembly flow shop problem with preventive maintenance (PM) activities under the multi-objective optimisation approaches. Reliability models are employed to carry out the maintenance activities. This paper attempts to find the appropriate sequence of jobs on machines in order to minimise the makespan and determining when to perform the PM activities in order to minimise the system unavailability. As this problem is proven to be NP-hard two multi-objective optimisation methods that are named non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are employed to find the Pareto-optimal front. The parameters of proposed algorithms are calibrated by artificial neural network (ANN) and the performances of the algorithms on the problem of various sizes are analysed based on four metrics. The computational results reveal NRGA is statistically better than NSGA-II.

Condition-based maintenance policy for linear consecutive-k-out-of-n: F system

ABSTRACTIn the article, a condition-based maintenance policy is proposed for a linear consecutive-k-out-of-n: F system. The failure times of components are assumed to be independent and identically distributed. It is assumed that the component states in the system can be known at any time and the system failure can be detected immediately. The preventive maintenance action is based on the number of working components in minimal cut sets of the system. If there is at least one minimal cut set consisting of only one working component, the system is maintained preventively after a certain time interval. The proposed policy is compared with corrective maintenance and age-based maintenance policies. As an extended case, it is assumed that the component states can only be known by inspection, but the system failure can be detected immediately. In this case, the system is inspected periodically and is also maintained preventively based on the system state at inspection. Numerical examples are studied to evaluate the performance of the proposed policy and investigate the effects of cost parameters on the expected cost rate.

On a single discrete scale for preventive maintenance with two shock processes affecting a complex system

A new approach to optimal maintenance of systems (networks) is suggested. It is applied to systems subject to two external independent shock processes. A system ‘consists’ of two parts, and each shock process affects only its own part. A new notion of bivariate signature is suggested and used for obtaining survival characteristics of a system and further optimization of the preventive maintenance actions. The preventive maintenance optimization is considered in the univariate discrete scale that counts the overall numbers of shocks of both types. An example of a transportation network is considered. Copyright © 2016 John Wiley & Sons, Ltd.

Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints

In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework.

예방적 유지관리를 통한 교량의 생애주기비용 절감 효과 분석

본 논문은 교량의 예방적 유지관리 효과를 생애주기비용 절감 측면에서 분석 하였다. 예방적 유지관리는 현재 실시되고 있는 대응적 유지관리 전략과 대비 되는 유지관리 전략으로 교량의 열화지연을 통해 궁극적으로는 교량의 사용수명을 연장 시키며 생애주기비용을 절감시키는 유지관리 전략이다. 이러한 예방적 유지관리 효과를 분석하기 위해 교량의 공용연수에 따른 건전도 점수 변화 모델과 FHWA 자료를 바탕으로 한 국내 고속도로 교량의 건전도 점수에 따른 보수 보강 비용 모델을 제시 하였다. 제시한 복수의 모델과 국내외 참고 문헌을 바탕으로 제안된 예방적 유지관리 항목(청소와 도장)의 비용과 주기를 활용하여 사용수명 100년까지의 표준교량(<TEX>$1,730m^2$</TEX>/교량) 당 생애주기 비용과 향후 20년간의 고속도로 총 유지관리 비용을 산정 하였다. 분석 결과 100년간 예방적 유지관리 활동으로 인해 교량 당 5억원의 절감효과를 볼 수 있으며 현재 예방적 유지관리 활동을 실시할 경우 20년 후(2035년)에는 총 1,837억원, 즉 연간 약 92억원의 비용 절감효과를 볼 수 있는 것으로 분석 되었다. The paper aims at evaluating effects of preventive maintenance on life cycle cost(LCC) reduction of bridges. The preventive maintenance activities capable to delay bridge deteriorations can reduce overall maintenance costs and extend service life of a bridge by regularly providing maintenance activities and avoiding larger maintenance(repairs or rehabilitations) costs. Couple of prediction models were proposed in order to calculate LCC of a typical bridge: a health score model and repair rehabilitation cost model. In addition, the maintenance activities such as wash and painting were also suggested in order to consider effects of preventive maintenance in the analysis based on literature reviews. According to analysis results, new maintenance strategy(reactive maintenance + preventive maintenance) can save \0.5 billion per bridge for future life-cycle costs over 100 year analysis or \184 billion for entire HBMS(Highway Bridge Management System) inventory over 20 years. Small investments for preventive maintenance in improved bridge management can have a very significant return when considering the large bridge inventory.

The “Dual-Ants Colony”: A novel hybrid approach for the flexible job shop scheduling problem with preventive maintenance

Due to their importance in the fields of both manufacturing industries and operations research, production scheduling and maintenance planning have received considerable attention both in academia and in industry. This paper investigates the Flexible Job Shop Scheduling Problem (FJSSP) with machine unavailability constraints due to Preventive Maintenance (PM) activities, under the objective of minimizing the makespan. We propose two new formulations: the first one in the form of a Mixed Integer Nonlinear Program (MINLP) and the second corresponding to a bi-level disjunctive/conjunctive graph. To deal with this variant FJSSP with PMs (FJSSP/PM), we develop the “Dual-Ants Colony” (DAC), a novel hybrid Ant Colony Optimization (ACO) approach with dynamic history, based on an ants system with dual activities. This optimization provides an effective integration of a local search and a set of dispatching rules. Three regular performance measures are also implemented. To show the efficiency of the DAC algorithm, computational experiments are carried out on a large range of well-known benchmarks from the literature and others newly generated. We address first the classical JSSP case, then the flexible FJSSP for partial flexibility. Finally, we study the case with preventive maintenance based on well-chosen PM periods. Obtained results demonstrate the viability and performance of the proposed approach, especially for the FJSSP/PM.