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In many industrial environments, systems are required to perform a sequence of operations (or missions) with finite breaks between each operation. During these breaks, it may be advantageous to perform repair on some of the system’s components. However, it may be impossible to perform all desirable maintenance activities prior to the beginning of the next mission due to limitations on maintenance resources. In this paper, a mathematical programming framework is established for assisting decision‐makers in determining the optimal subset of maintenance activities to perform prior to beginning the next mission. This decision‐making process is referred to as selective maintenance. The selective maintenance models presented allow the decision‐maker to consider limitations on maintenance time and budget, as well as the reliability of the system. Selective maintenance is an open research area that is consistent with the modern industrial objective of performing more intelligent and efficient maintenance.

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.

Total productive maintenance (TPM) has been widely recognized as a strategic weapon for improving manufacturing performance. Evaluate efficiency of TPM implementation is considered as a key element in order to motivate staff and to give decision-makers more confidence.

This study consists in developing a new method of evaluating TPM implementation, relying on analytical models and considering two preventive maintenance strategies: periodic and age-dependent.

The preventive maintenance period and TPM period defined as decision variables are obtained simultaneously by maximizing the expected profit under TPM implementation. A numerical example is presented and a sensitivity study is developed to validate the proposed models.

The aim of this research is to quantify, through analytic development, the impact of TPM implementation in a company by calculating and comparing the profit made with and without TPM.

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.

The purpose of this study is to analyze the effect of human factor training in aircraft maintenance accident mitigation and aircraft safety in post COVID-19 aviation scenarios. The cause of aircraft accidents and details of three decades of selective aircraft maintenance accidents are analyzed to arrive to the significant aviation safety factor. The effect of COVID-19 pandemic and related technological applications to maintain high standards of safety and their applications in aircraft maintenance with respect to the view of human factors are discussed in details.

This paper details the overview of the human errors, error mitigation and need of human factor applications in aircraft maintenance industry for safe air travel. The criticality of aircraft maintenance in keeping aircraft in airworthy condition to provide safe air transportation without delay and to support airline economy is discussed in this study.

The cause of aircraft accidents and details of three decades of selective aircraft maintenance accidents are analyzed to arrive to the significant aviation safety factor. The effect of COVID-19 pandemic and related technological applications to maintain high standards of safety and their applications in aircraft maintenance with respect to the view of human factors are discussed in details. The route of error mitigation and need of high standard technological training with human factor knowledge, to aircraft maintenance students are analyzed in detail with the opportunity of percentages of error reduction.

This study bridges, gained knowledge for aircraft maintenance error mitigation, current accident rates and future training needs for safest air travel through high standard quality maintenance in aircraft and its systems.

While steady-state analysis is useful, it does not consider the inherent transient characteristics of repairable systems' behavior, especially in systems that have relatively short life spans, or when their transient behavior is of special concern such as the motivating example used in this paper, military systems. Therefore, a maintenance policy that considers both transient and steady-state availability and aims to achieve the best trade-off between high steady-state availability and rapid stabilization is essential.

This paper studies the transient behavior of system availability under the Kijima Type II virtual age model. While such systems achieve steady-state availability, and it has been proved that deploying preventive maintenance (PM) can significantly improve its steady-state availability, this improvement often comes at the price of longer and increased fluctuating transient behavior, which affects overall system performance. The authors present a methodology that identifies the optimal PM policy that achieves the best trade-off between high steady-state availability and rapid stabilization based on cost-availability analysis.

When the proposed simulation-based optimization and cost analysis methodology is applied to the motivating example, it produces an optimal PM policy that achieves an availability–variability balance between transient and steady-state system behaviors. The optimal PM policy produces a notably lower availability coefficient of variation (by 11.5%), while at the same time suffering a negligible limiting availability loss of only 0.3%. The new optimal PM policy also provides cost savings of about 5% in total maintenance cost. The performed sensitivity analysis shows that the system's optimal maintenance cost is sensitive to the repair time, the shape parameter of the Weibull distribution and the downtime cost, but is robust with respect to changes in the remaining parameters.

Most of the current maintenance models emphasize the steady-state behavior of availability and neglect its transient behavior. For some systems, using steady-state availability as the sole metric for performance is not adequate, especially in systems that have relatively short life spans or when their transient behavior affects the overall performance. However, little work has been done on the transient analysis of such systems. In this paper, the authors aim to fill this gap by emphasizing such systems and applications where transient behavior is of critical importance to efficiently optimize system performance. The authors use military systems as a motivating example.

Maintenance is a critical business function with a great impact on economic, environmental and social aspects. However, maintenance decisions' planning has been driven by merely economic and technical measures with inadequate consideration of environmental and social dimensions. This paper presents a review of the literature pertaining to sustainable maintenance decision-making models supported by a bibliometric analysis that seeks to establish the evolution of this research over time and identify the main research clusters.

A systematic literature review, supported with a bibliometric and network analysis, of the extant studies is conducted. The relevant literature is categorized based on which sustainability pillar, or possibly multiple ones, is being considered with further classification outlining the application area, modeling approach and the specific peculiarities characterizing each area.

The review revealed that maintenance and sustainability modeling is an emerging area of research that has intensified in the last few years. This fertile area can be developed further in several directions. In particular, there is room for devising models that are implementable, based on reliable and timely data with proven tangible practical results. While the environmental aspect has been considered, there is a clear scarcity of works addressing the social dimension. One of the identified barriers to developing applicable models is the lack of the required, accurate and timely data.

This work contributes to the maintenance and sustainability modeling research area, provides insights not previously addressed and highlights several avenues for future research. To the best of the authors' knowledge, this is the first review that looks at the integration of sustainability issues in maintenance modeling and optimization.

To be successful, a company has to capture the initiative in an industry from its competitors. The author offers a detailed prescription for getting and keeping the strategic initiative in the face of countermoves by your opponent.

The findings of this study have lightened the focal research areas in maintenance planning and scheduling. These also served as effective guidelines for future studies in this area. This research, therefore, contributes in fulfilling the gap by carrying out an SLR of contemporary research studies in the area of models for maintenance planning and scheduling. At present, SLR rooted in BA has not been carried focusing on a survey over models for maintenance planning and scheduling. SLR uses advanced scientific methodologies from BA tools to unveil thematic structures.

We have systematically reviewed over 1,021 peer-reviewed journal articles. Advanced contemporary tools from Bibliometric Analysis (BA) are used to perform a Systematic Literature Review (SLR). First, exploratory analysis is presented, highlighting the influential authors, sources and region amongst other key indices. Second, the large bibliographical data is visualized using co-citation network analyses, and research clusters (themes) are identified. The co-citation network is extended into a dynamic co-citation network and unveils the evolution of the research clusters. Last, cluster-based content analysis and historiographical analysis is carried out to predict the prospect of future research studies.

BA tools first outlined an exploratory analysis that noted influential authors, production countries, top-cited papers and frequent keywords. Later, the bibliometric data of over 1,021 documents is visualized using co-citation network analyses. Later, a dynamic co-citation analysis identified the evolution of research clusters over time. A historiographical direct citation analysis also unveils potential research directions. We have clearly observed that there are two main streams of maintenance planning and scheduling applications. The first has focused on joint maintenance and operations on machines. The second focused on integrated production and maintenance models in an echelon setting for unrealizable production facilities.

There are many literature review-based research studies that have contributed to maintenance scheduling research surveys. However, most studies have adopted traditional approaches, which often fall short in handling large bibliometric data and therefore suffer from selection biases from the authors. As a result, in this area, the existing reviews could be non-comprehensive. This study bridges the research gap by conducting an SLR of maintenance models, which to the best of our knowledge, has not been carried out before this study.

During the last decade, many companies have made large investments in the development and implementation of enterprise resource planning (ERP) systems. However, only few of these systems developed or installed have actually considered maintenance strategies. Maintenance is a complex process that is triggered by planned periodic repair (scheduled or planned maintenance), equipment breakdown or deterioration indicated by a monitored parameter (unplanned or emergency maintenance). This process requires planning, scheduling, monitoring, quality assurance and deployment of necessary resources (workshop, manpower, machines, equipment, tools, spare parts, materials). Proper design and integration of maintenance management into ERP systems enable enterprises to effectively manage their production planning and scheduling, as well as to analyze their maintenance history so as to carry out cost analysis and produce future projections of failure trends. The present work presents the design of an object‐oriented maintenance management model and its integration into an ERP system. The proposed model was designed towards the development of innovative industrial software regarding the optimum management of maintenance in a wide range of business areas.