Preventive Maintenance Operations Research Articles

Quantitative analysis of the impact of maintenance management on the energy consumption of a hospital in Extremadura (Spain)

The aim of this paper is to analyse the impact of maintenance management on the energy consumption of a hospital in Extremadura (Spain) and to look for existing relationships between the time spent on maintenance operations and the energy consumption of the building.The results show that an average annual increase of 6% in time spent on preventive maintenance operations, over a period of 5 years, resulted in a 20% decrease in the demand for corrective maintenance and an average annual saving of 500 MWh in energy consumption. This in turn prevented the release of 186 tons of CO2 and other greenhouse gases into the atmosphere. This represents an annual saving of 75,000 €, with no increase in either human resources used nor in maintenance costs. Air conditioning installations, Low Voltage systems and Plumbing installations consume more than 80% of the resources applied to building maintenance.

Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines

In the current paper, a special class of buffer allocation problem (BAP)—buffer and preventive maintenance periods allocation problems—is studied. Three objective functions including the maximization of production rate, the minimization of total buffer size, and the total number of defective units are examined in order to solve the proposed model. In addition, an integrated simulation and meta-heuristic algorithm method is used for the same purpose. The proposed model and solution approach are applied to a case study, i.e., water heater production line in Polar Saunier Duval Company. Furthermore, the model and solutions will be assessed. In this integrated approach, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms are used as meta-heuristic algorithms. These algorithms are compared in terms of quality of solutions, computational time, and relative deviation of final solutions in 0.95 confidence level. Numerical results reveal that GA is superior to PSO in terms of the quality of obtained solutions and relative deviation from optimal solutions (RPD). Furthermore, PSO converges in shorter computational time. Finally, based on outputs of the integrated solution approach, some important points about the parameters of the production line are presented. Computational results show that production rate is the most influential objective function in optimal solutions. However, drawing from the characteristics of production line, the number of defective products has less impact on optimal solutions in comparison with other objective functions. It is also concluded that there is a direct relationship between the number of defective products and time periods between preventive maintenance operations; in other words, if the time periods between preventive maintenance operations increase, the number of defective products grow.

A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring

Maintenance and its cost continue, over the years, to draw the attention of production management since the unplanned failures decrease the reliability of the system and also the return of investments. Advanced maintenance techniques that capture and process shop-floor information can reduce costs and increase the sustainability of an enterprise. This paper presents a condition-based preventive maintenance approach integrated into a machine monitoring framework. The latter acquires data from shop-floor machine tools and analyses them through an information fusion technique to support the condition-based preventive maintenance operations. The proposed approach is developed into a software service, deployed on a Cloud environment. The service gathers and processes data, such as their actual processing time and machining time per tool, related to the operation of machine tools and equipment and calculates the expected remaining useful life of components. Moreover, it provides notifications to machine tool operators and maintenance departments, as well as it enables the communication among them using mobile technology. The framework is applied to a case study with data obtained from a machining SME.

On the combined maintenance and routing optimization problem

This work focuses on the problem of planning and scheduling maintenance operations for a set of geographically distributed machines, subject to non-deterministic failures with a set of technicians that perform preventive maintenance and repair operations on the machines at the customer sites within a specific time window. This study presents a two-step iterative approach. In the first step, a maintenance model determines the optimal time until the next preventive maintenance operation, its frequency, and the time window for each customer, while minimizing the total expected maintenance costs. In the second step, a routing model assigns and schedules maintenance operations to each technician over the planning horizon within the workday. This two-step iterative process balances the maintenance cost, the failure probabilities, and waiting times at each customer. The novelty of this work lies in the integration of maintenance scheduling and a routing model that considers several machines.

A case study of grouping machines based on failure components planning model

Preventive maintenance (PM) operations are normally practiced by organisations to maintain the integrity of equipment and to avoid occurrences of failure. However, machine downtime continues to be a major issue even if PM is done periodically. Machines fail to operate because of faulty components. This paper presents a planning model on grouping machines based on failure components that adopts group technology principle to group machines based on failure rate, thus allowing for PM to be conducted in groups. The model is then validated and applied to a case study conducted in a semiconductor company. The application can be used to simplify maintenance operations in terms of PM planning.

A stochastic reward net approach for reliability analysis of a flexible manufacturing module

The machines and robots in a flexible manufacturing module (FMM) are more prone to failures as compared to the traditional manufacturing systems. The failures due to wear and tear of the machine components can be eliminated by properly scheduling the preventive maintenance operations but the failures due to random causes are unpredictable and cannot be eliminated. A corrective maintenance strategy is necessary to deal with such failures and due to these failures a lot of production time is lost. Keeping in mind the higher initial investments required in acquiring the FMM systems it is necessary to study the effect of the system reliability in system performance. Markov models have been used extensively for such studies but in many cases the system designer or reliability engineer faces a major difficulty in transforming the problem into a Markov chain due to the huge state space associated with the models. To this effect the authors of this paper make use of a higher class of Petri nets called stochastic reward nets (SRNs) to model and analyze the FMM system. The use of SRNs helps in combining the modeling power of Petri nets and analytical tractability of Markov processes for carrying out the reliability analysis of the FMM.

Analysis and Scheduling of Maintenance Operations for a Chain of Gas Stations

Maintenance is one of the central issues in operational activities, which involve any type of equipment. In this paper we have considered analysis, modeling, and scheduling of preventive maintenance operations for fuel dispensers in a chain of gas stations. A gas station company with more than 570 dispensers in more than 40 stations is considered and the maintenance problem is studied in detail. Operations research tools, including maintenance models and linear programming, were used to establish optimum schedules for preventive maintenance operations. Detailed cost analyses were carried out to determine feasibility of the proposed preventive maintenance schedules. Models and procedures presented in this paper could guide operation engineers and maintenance managers in solving similar problems for operational improvements.

Joint scheduling of jobs and Preventive Maintenance operations in the flowshop sequencing problem: a resolution with sequential and integrated strategies

This paper presents a comparative study on joint production and Preventive Maintenance (PM) scheduling according to the most interesting joint scheduling strategies: sequential and integrated ones regarding Flow Shop Problems (FSPs). For each strategy, we present three heuristics: Genetic Algorithms (GAs), Taboo Search (TS) and NEH heuristic. The proposed heuristics have been applied to non-standard test problems, which represent joint production and maintenance benchmark flowshop scheduling problems taken from Benbouzid et al. (2003). A comparison of the solutions yielded by the heuristics developed in this paper with the heuristic solutions given by Taillard (1993) is undertaken with respect to the minimisation of performance loss after maintenance insertion. The comparison shows that the proposed integrated GAs are clearly superior to all the analysed algorithms.

A new policy for scheduling maintenance on a single machine

Preventive maintenance has been well-recognised as an essential tool for productivity, quality and efficiency in manufacturing. There has been an increasing interest to explore the impact of incorporating preventive maintenance under various scenarios commonly encountered in manufacturing environment. However, besides integrating the preventive maintenance into manufacturing operations, it is also important to adopt a sound preventive maintenance strategy. This research discusses a new policy for scheduling jobs and preventive maintenance operations on a single machine. The proposed policy explores an option of procrastinating the preventive maintenance operations when they are due versus delaying these preventive maintenance operations with an additional requirement to carry out these maintenance operations at later times when it is opted to delay the preventive maintenance operations. The minimisation of the total completion time is considered as a performance measure. The problem is identified NP-hard. The properties of an optimal schedule are identified and a single pass heuristic algorithm is proposed using these properties. Two metaheuristics, tabu search and simulated annealing are also developed using the aforementioned properties. A lower bound is suggested in order to analyse the performance of the proposed heuristics in a numerical experimentation with randomly generated large size problems. The study shows that the performance of the single pass heuristic and the lower bound is sensitive to the maintenance related parameters. It is also reported that the metaheuristics outperform the single pass heuristic as well as are the most robust and efficient methods to solve the problem.

Contribution of simulation to the optimization of maintenance strategies for a randomly failing production system

This paper compares two strategies for operating a production system composed of two machines working in parallel and a downstream inventory supplying an assembly line. The two machines, which are prone to random failures, undergo preventive and corrective maintenance operations. These operations with a random duration make the machines unavailable. Moreover, during regular subcontracting operations, one of these machines becomes unavailable to supply the downstream inventory. In the first strategy it is assumed that the periodicity of preventive maintenance operations and the production rate of each machine are independent. The second strategy suggests an interaction between the periods of unavailability and the production rates of the two machines in order to minimize production losses during these periods. A simulation model for each strategy is developed so as to be able to compare them and to simultaneously determine the timing of preventive maintenance on each machine considering the total average cost per time unit as the performance criterion. The second strategy is then considered, and a multi-criteria analysis is adopted to reach the best cost-availability compromise.

Production and preventive maintenance control in a stochastic manufacturing system

This paper considers the problem of production and preventive maintenance control in a stochastic manufacturing system. The system is subject to multiple uncertainties such as random customer demands, machine failure and repair, and stochastic processing times. A threshold-type policy is proposed to control the production rate and the preventive maintenance operation simultaneously. The explicit form of the stationary distribution of the system state is derived analytically and used to produce the formula for various steady-state performance measures. The optimal threshold values can then be obtained by optimising the relevant formula. Numerical examples demonstrate that the proposed threshold policy is indeed optimal or extremely close to the optimal policy in a range of scenarios.

On the integrated production, inventory and preventive maintenance problem in manufacturing systems with backorder

The integrated Production, Inventory and Preventive Maintenance Problem (PIPMP) is concerned with coordinating production, inventory and preventive maintenance operations to meet customer demand with the aim of minimising costs. The particular PIPMP considered in this paper involves the dynamics of an unreliable production environment that strongly depends on an age-dependent likelihood of machine breakdowns. We provide optimality conditions for more realistic manufacturing systems and use numerical methods to obtain the optimal preventive maintenance policy and the relevant age-dependent production policy. Compared with available production and preventive maintenance policies, the numerical solution obtained shows that the proposed age-dependent optimal production and maintenance policies significantly reduce the overall incurred cost.

A hybrid artificial immune algorithm for a realistic variant of job shops to minimize the total completion time

This paper investigates an extended problem of job shop scheduling to minimize the total completion time. With aim of actualization of the scheduling problems, many researchers have recently considered realistic assumptions in their problems. Two of the most applied assumptions are to consider sequence-dependent setup times and machine availability constraints (MACs). In this paper, we deal with a specific case of MACs caused by preventive maintenance (PM) operations. Contrary to the previous papers considering fixed or/and conservative policies, we consider flexible PM operations, in which PM operations may be postponed or expedited as required. A simple technique is employed to schedule production jobs along with the flexible MACs caused by PM. To solve the given problem, we present a novel meta-heuristic method based on the artificial immune algorithm (AIA) incorporating some advanced features. For further enhancement, the proposed AIA is hybridized with a simple and fast simulated annealing (SA). To evaluate the proposed algorithms, we compare our proposed AIA with three well-known algorithms taken from the literature. Finally, we find that the proposed AIA outperforms other algorithms.

Modelling of multi-stage production lines with maintenance operations

A multi-stage production line consists of a series of machines, which are subject to failures owing to wearouts and other random causes. While random failures are unavoidable and need corrective maintenance (CM), wearout failures can be avoided by preventive maintenance (PM), including tool change operations. Both types of maintenances can cause significant production losses, particularly if the production line is automated and the stations are rigidly linked. Therefore, intermediate buffers are introduced to decouple the rigidly linked machinery and to localize the losses caused by equipment stoppages. Reliability and productivity calculations of multi-stage lines with storage units can be quite complex. Furthermore, closed form solutions are generally not possible when preventive and corrective maintenances are incorporated into the model. In this paper, we present a discrete mathematical model integrated into an iterative simulation procedure to determine the production output rate of a multi-stage line with corrective and preventive maintenance operations.

A model for preventive maintenance operations and forecasting

Equipment costs constitute the greatest majority of overall costs for semiconductor manufacturing. Therefore, maintaining high equipment availability has been regarded as one of the major goals in the industry. The ability to forecast correctly equipment preventive maintenance (PM) timing requirements not only can help optimizing equipment uptime but also minimizing negative impacts on manufacturing production efficiency. This research used grey theory and evaluation diagnosis to construct a PM forecasting model for prediction of PM timing of various machines. The results showed significant improvements of PM timing predictions compared to the existing method based on experience and an alternative method proposed by Li and Chang (Semiconductor Manufacturing Technology Workshop 2002: 10–11, pp. 275–277) for the same fab cases.

Considering scheduling and preventive maintenance in the flowshop sequencing problem

The aim of this paper is to propose tools in order to implicitly consider different preventive maintenance policies on machines regarding flowshop problems. These policies are intended to maximize the availability or to keep a minimum level of reliability during the production horizon. It proposes a simple criterion to schedule preventive maintenance operations to the production sequence. This criterion demonstrates the significance of taking into consideration preventive maintenance together with sequencing and the consequences of not doing so. The optimization criterion considered consists in minimizing the makespan of the sequence or C max . In total, six adaptations of existing heuristic and metaheuristic methods are evaluated for the consideration of preventive maintenance and they are applied to a set of 7200 instances. The results and experiments carried out indicate that modern Ant Colony and Genetic Algorithms provide very effective solutions for this problem.

Failure replacement and preventive maintenance spare parts ordering policy

This paper addresses inventory policy for spare parts, when demand for the spare parts arises due to regularly scheduled preventive maintenance, as well as random failure of units in service. A stochastic dynamic programming model is used to characterize an ordering policy which addresses both sources of demand in a unified manner. The optimal policy has the form ( s( k), S( k)), where k is the number of periods until the next scheduled preventive maintenance operation. The nature of the ( s( k), S( k)) policy is characterized through numeric evaluation. The efficiency of the optimal policy is evaluated, relative to a simpler policy which addresses the failure replacement and preventive maintenance demands with separate ordering policies.

Optimizing Periodic Maintenance Operations for Schindler Elevator Corporation

Schindler, the world's largest escalator company and second-largest elevator company, maintains tens of thousands of elevators and escalators throughout North America. Thousands of technicians are on the road each day to maintain, repair, and help in emergencies. Each technician's route requires precise and optimized planning. Schindler Elevator Corporation turned to Environmental Systems Research Institute (ESRI) to develop an automated route-scheduling and planning system. ESRI provided a geographic-information-system-integrated application that employs operations research techniques to optimize preventive maintenance operations. It relies on a series of algorithms to assign maintenance work to technicians and to create efficient day-routes by solving the periodic-vehicle-routing problem. These automated tools allow Schindler to restructure and streamline service areas. The optimization system saves over $1 million annually and increases Schindler's managers' awareness of operating revenue.

A mountain process with state dependent input and output and a correlated dam

We consider a single machine, subject to breakdown, that produces items to inventory. While the machine is working (an ON period) there is a net flow, with general deterministic production rate, into the buffer. As a result, the production rate minus the demand rate generate a certain deterministic increase rate which is state dependent. There are two different types of OFF periods. One is the repair operation initiated after a breakdown; the second is a preventive maintenance operation. The length of an OFF period depends on the preceding ON period. During the OFF periods there is a deterministic outflow (whose release rule is general but state dependent), generated by the demand rate. The buffer content process can then be described as a fluid model called the mountain process. The main tool employed in determining its steady state law exploits an equivalence relationship between the mountain process and a special dam process with general release rule whose jumps are also state dependent. This study is a continuation and generalization of a previous work [Oper. Res. Lett. 26 (2000) 137–147], in which both the production rate and the demand rate are assumed to be constant.

Preventive maintenance and RCM II

The future role of preventive maintenance operations requires upheaval in the way industry perceives the act of successful maintenance. The author analyses the strategic background of preventive maintenance by introducing the reliability centred maintenance (RCM II) methodology.