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Machine maintenance is essential in many industries. How to schedule maintenance becomes especially important when achieving high shop performance is desired. This paper aims to consider the maintenance scheduling problem in a company where different maintenance situations exist.

The company, which is famous in South East Asia, specializes in the manufacturing of textiles such as polypropylene and chemical chip. In this paper an algorithm is presented, for five specific situations, for the company to minimize the number of tardy jobs.

Owing to machine overload in 24‐hour production, machine breakdowns usually occur in the shop. There is an urgent need for the company to derive a procedure that will incorporate the maintenance into the schedule to reduce the machine breakdown. Computational results have showed that the proposed algorithm outperforms the current method with an average improvement of 32.5 percent, although the actual average improvement is somewhat lower partly because some jobs are unexpectedly cancelled or changed.

This proposed algorithm is appropriate not only for the studied company, but for those companies where maintenance has to be operated one or several times a month and the maintenance time is so significant that it cannot be ignored.

Provides an algorithm to aid in the maintenance scheduling problem.

Aircraft maintenance down times together with maintenance activity durations and associated man‐hour expenditure are extremely important factors contributing to two major yardsticks of airline and civil aircraft performance: despatch reliability and direct maintenance costs, and have important cost implications. In maintaining an aircraft there is a need to predict fault diagnosis activities in quantitative terms of time. Traditionally estimating maintenance, in particular fault diagnosis, in terms of time has received scant attention. This paper identifies the need to evaluate all maintenance activity times. In addition, an approach is offered to estimate fault diagnosis activity times using knowledge‐based systems. Finally, the paper offers a vision outline in applying the technology and techniques to provide cost‐effective and timely fault diagnosis.

Organisations either keep spares for their own use, or‐for‐sale to other organisations. In either case, the ultimate need is to be able to replace worn or defective parts in operational machinery or equipment. In an economic sense, spares are kept to meet the needs of the situation in the cheapest way.

Maintainability is a critical design characteristic that shows how well a product can be maintained; maintenance time is a comprehensive parameter of product maintainability design. This paper aims to provide an integrated methodology for complex product maintainability verification and maintenance time prediction using virtual prototypes and humans in a virtual dynamic simulation of the maintenance process.

An integrated platform for maintainability verification and maintenance time prediction is designed. Decomposition of maintenance tasks, corrective measurement time method, and an impact matrix of maintenance therbligs and time are presented.

The proposed methodology can efficiently conduct complex product maintainability verification and maintenance time prediction.

Early and effective verification and prediction of the maintainability and maintenance time program can significantly improve the maintainability and availability of a complex product.

A universally applicable method for product maintainability verification and maintenance time prediction is presented.

Based on virtual maintenance, this paper aims to propose a time prediction method of assembly and disassembly (A&D) actions of product maintenance process to enhance existing methods’ prediction accuracy, applicability and efficiency.

First, a framework of A&D time prediction model is constructed, which describes the time prediction process in detail. Then, basic maintenance motions which can comprise a whole A&D process are classified into five categories: body movement, working posture change, upper limb movement, operation and grasp/placement. A standard posture library is developed based on the classification. Next, according to motion characteristics, different time prediction methods for each motion category are proposed based on virtual maintenance simulation, modular arrangement of predetermined time standard theory and the statistics acquired from motion experiment. Finally, time correction based on the quantitative evaluation method of motion time influence factors is studied so that A&D time could be predicted with more accuracy.

Case study of time prediction of products’ various A&D processes is conducted by implementing the proposed method. The prediction process of diesel cooling fan disassemble time is presented in detail. Through comparison, the advantages and effectiveness of the method are demonstrated.

This paper proposes a more accurate, efficient and applicable product A&D time prediction method. It can help designers predict A&D time of a product maintenance accurately in early design phases without a physical prototype. It can also provide basis for the verification of maintainability, the balance of the design of product structure and system layout.

The purpose of this paper is to develop an improved, enumerative solution procedure for solving the original selective maintenance problems. Selective maintenance refers to the process of identifying the set of maintenance actions to perform from a desirable set of maintenance actions.

A series of four improvements to a previously proposed enumerative solution procedure are presented. The improvements are defined and tested sequentially on an experimental set of problem instances. The improvements are characterized relative to the achieved reduction in CPU time for a software application.

The improved enumerative procedure reduces the CPU time required to solve the selective maintenance problems by as much as 99 per cent. There is a corresponding increase in practical problem size of more than 200 per cent.

Almost all organizations use a variety of repairable systems to achieve their mission. Typically, these systems have to share the limited maintenance resources possessed by the organization. Therefore, an improved ability to solve selective maintenance problem is relevant to many industries.

The body of knowledge relative to selective maintenance continues to grow. However, this is the first study aimed at improving the capability of engineers to solve practically‐sized selective maintenance problems.

The great need for an optimum preventive maintenance strategy coupled with the fast‐developing condition‐monitoring techniques has given rise to the invention of relevant condition predictor (RCP)‐based maintenance approach. The main purpose of this approach is to prevent the failures due to gradual deterioration of mechanical items in order to improve system reliability and availability. This is done by monitoring relevant condition predictors of constituent maintenance significant items of the system, taking into account the availability and cost‐effectiveness of the monitoring techniques. A comprehensive review of all constituent items is carried out and a systematic approach is used to decide an optimum maintenance policy for each corresponding group of items. An optimum time to the examination of relevant condition predictors is derived mathematically with required reliability as the optimisation criterion in order to implement the RCP‐based maintenance activities.

The traditional practice for maintenance, quality control and production scheduling is to plan independently irrespective of an interrelationship exist between them. The purpose of this paper is to develop an approach for integrating maintenance, quality control and production scheduling. The objective is to investigate the benefits of the integrated effect in terms of the expected total cost of system operation of the three functions.

The proposed approach is based on the conditional reliability of the components. Cost model for integrating selective maintenance, quality control using sampling-based procedure and production scheduling is developed using the conditional reliability. An integrated approach is such that, first an optimal schedule for the batches to be processed is obtained independently while the maintenance and quality control decisions are optimized considering the optimal schedule on the machine. The expected total cost of conventional approach, i.e. “No integration” is calculated to compare the effectiveness of integrated approach.

The integrated approach have shown a higher cost saving as compared to the independent planning approach. The approach is practical to implement as the results are obtained in a reasonable computational time.

The approach presented in this paper is generic and can be applied at planned as well as unplanned opportunities. The proposed integrated approach is dynamic in nature, as during maintenance opportunities, it is possible to optimize the decision on maintenance, quality control and production scheduling considering the current age of components and production requirement.

The originality of the paper is in the approach for integration of the three elements of shop floor operations that are usually treated separately and rarely touched upon by researchers in the literature.

Degradation of repairable components may not be similar after each maintenance activity; thus, the classic (traditional-time based) maintenance policies, which consider preventive maintenance (PM), age-based maintenance and overhauls to be done at fixed time interval, may fail to monitor the exact condition of the component. Thus, a progressive maintenance policy (PMP) may be more appropriate for the industries that deal with large, complex and critical repairable systems (RS) such as aerospace industries, nuclear power plants, etc.

A progressive maintenance policy is developed, in which hard life, PM scheduled time and overhaul period of the system are revised after each service activity by adjusting PM interval and mean residual life (MRL) such that the risk of failure is not increased.

A comparative study is then carried out between the classic PM policy and developed PMP, and the improvement in availability, mean time between failures and reduction in maintenance cost is registered.

The proposed PMP takes care of the equipment degradation more efficiently than any other existing maintenance policies and is also flexible in its application as the policy can be continuously amended as per the failure profile of the equipment. Similar maintenance policies assuming lifetime distributions are available in the literature, but to ascertain that the proposed PMP is more suitable and applicable to the industries, this paper uses Kijima-based imperfect maintenance models. The proposed PMP is demonstrated through a real-time data set example.

The purpose of this paper is to propose a new formulation of the overall equipment effectiveness (OEE) relying on the process approach (PA) in maintenance. This new indicator will be designated by OEE-TCQ.

Methodology followed in this paper is based on using two major parts. The first part deals with the use of time-driven activity-based costing (TDABC) and program evaluation and review technique (PERT) methods to formulate the temporal and financial components of the OEE-TCQ. The second part concerns the quality component formulation of OEE-TCQ by using the analytical hierarchy process (AHP) method and quality’s improvement (Six Sigma).

It is a new formulation of the OEE, based on the PA applied to operational maintenance processes, and also is interesting for improvement maintenance.

The new OEE-TCQ indicator can be used to support maintenance managers to have a three-dimensional analysis based on time, financial and quality criteria, also to improve time, cost and quality of each activity of maintenance operational processes.

This paper is one of few papers in maintenance proposing a new formulation of the OEE by using the combination of several methods especially TDABC, PERT, AHP and Six Sigma. It proposes in part a new quality evaluation approach for each activity of maintenance operational processes. Also, it suggests a new formulation for defects by millions of opportunities.