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Reconfigurable vibrating screen (RVS) is an innovative beneficiation machine designed at Tshwane University of Technology, Republic of South Africa (RSA); with adjustable screen structure to ensure sorting, sizing and screening of varying mineral particles (sizes and quantities) demanded by the customers in a cost-effective manner through the screen structure geometric transformation. In order to ensure that this machine is optimally maintained and managed when utilized in surface and underground mining industries, there is a need to establish or ascertain the best maintenance practices that would be used in optimally managing the RVS machine using decision making techniques. In view of this, the purpose of this paper is to ascertain the best maintenance practices that would be used to optimally maintain and manage the RVS machine when used in surface and underground mines.

Decision making techniques such as weighted decision matrix (WDM) and analytical hierarchy process (AHP) were used in this research work to establish the best maintenance practice for optimally maintaining and managing the RVS machine using relevant literature survey on maintenance management systems as well as the different maintenance criteria decision indices obtained from different conventional vibrating screen machine manufacturers and maintenance experts.

Based on the results obtained from the WDM analysis, it was anticipated that e-maintenance (e-M) system embedded with diagnosing and prognosing algorithms; with a cumulative weight score of 2.37 is the best maintenance practice for managing the RVS machine when used in surface mines, while AHP with deeper decision making analysis anticipated that the robotic-driven maintenance (RM) system with an important decision criteria; safety, and a cumulative hierarchy score of 28.6 percent, supported by e-M management system with a cumulative hierarchy score of 17.6 percent are the best maintenance mix that could be used in optimally maintaining and managing the RVS machine, when used in a craggy and hazardous underground mining environment.

To this effect, it could be anticipated that e-M management system (endowed with the ability to detect fault on the machine, diagnose and prognose the different subsystems of the RVS machine and ascertain the reconfiguration time and process of the RVS machine in recovering production loss during the maintenance of the machine as well as meeting customers demand, etc.) is the best maintenance practice for optimally maintaining the RVS machine when utilized in surface mines while both e-M management system and RM management system (endowed with the ability to carry out automated maintenance tasks achievement under little or no maintenance manager intervention) are also anticipated as the best customized maintenance practices mix that could be used in optimally maintaining the RVS machine, when used in dangerous and hazardous underground mining environment.

This maintenance management system evaluation and selection for optimal RVS machine functionality will serve as a useful information to different mining machines (and other related machines) maintenance managers, in selecting the best maintenance management system for ensuring optimal functionality, reliability and maintainability of machines used in their industries.

Attempts to develop a model of maintenance decision making using the analytic hierarchy (AHP). Describes problems in maintenance arising from not having clear criteria and not having robust decisions with which to maintain failing equipment. The objective being to develop a dynamic and adaptable maintenance system that utilises existing data and supports decisions accordingly. Proposes a three‐stage system that can handle multiple criteria decision analysis, conflicting objectives, and subjective judgements. Moreover, the methodology facilitates and supports a group decision‐making process. This systematic, and adaptable, approach will determine what specific actions to perform given current working conditions. The first stage involves identifying the criteria upon which engineering personnel wish to formulate a maintenance decision, or action. The second stage is to prioritise the different criteria by implementing a multiple‐criteria evaluation method. Finally, based on different criteria, machines are ranked according to criticality. This is followed by an analysis of failures in a graphical and a hierarchical format.

In today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an extensively difficult problem for maintenance engineers. Over the years, a diverse range of solution methodologies have been developed for solving this multi-criteria decision-making (MCDM) problem. In this paper, the authors have presented a comprehensive review of latest maintenance strategy paradigms and solution approaches proposed for the selection of an appropriate strategy in various industries. It would provide a systematic mapping of developments in this field and identify some research gaps to explore further studies.

A systematic state-of-the-art comprehensive literature review on maintenance strategy paradigms and selection approaches is presented in this study. In this study, 87 research articles published in peer-reviewed journals, since year 2012, are reviewed.

For the selection of a suitable maintenance strategy, a variety of criteria are considered to better evaluate the alternatives. In this study, contemporary strategies are discussed, and their applications in different industries are also depicted. Moreover, through the analysis of extant literature, critical criteria are selected and classified in six major categories (namely, economic, technical, safety, environmental, feasibility and social) and further sub-categorized in quantitative and qualitative classes. These clusters of criteria can be helpful as an initial set of criteria for survey and then case- or industry-specific criteria can be shortlisted for further alternative evaluation.

From the perspective of maintenance managers, maintenance management can be a very difficult task, considering the numerous factors affecting the decision-making process. In order to help in the decision-making process, this study presents the contemporary maintenance strategies in a systematic manner. In a previous study (Kothamasu et al., 2006), these strategies were classified into repair and prevent classes only. With the developments of autonomous maintenance and design out maintenance (DOM), it was fair to include continuous improvement class. It will help managers and practitioners to identify, according to organization policy, appropriate maintenance strategy alternatives for the asset. A benchmark set of state-of-the-art maintenance strategies are laid out with their applications. The industrial case studies discussed in this study summarizes the optimal maintenance strategies for respective industries. Also, most critical criteria are identified from the existing studies for various industries that can help maintenance practitioners in acknowledging the critical factors and making appropriate decisions. Evaluation parameters for the maintenance strategy selection (MSS) generally conflict with each other, and considering the difficulty of quantifying the qualitative measures, it is a challenging task to determine the optimal trade-off. In order to overcome these challenges, popular MCDM approaches, demonstrating effective results across different industries are discussed with their limitations and applications. Decision-makers can refer this study to identify best suitable decision-making technique for the MSS problem in the industry of their choice. Maintenance managers and engineers can refer the case studies illustrated in Tables 1 and 2 to analyse the MSS techniques proposed by previous studies with industry-specific applications.

This study is an attempt to provide a reference point for research scholars interested in the field of maintenance management and/or development of maintenance strategy framework. This study provides a critical state-of-the-art review of efforts made in the field of MSS. The prominent maintenance strategies being implemented in contemporary industries are discussed with respective case studies. Interested researchers and academicians can familiarize themselves with these strategies and their distinct features in this study. In order to guide future studies and provide a reference point for academicians, MSS critical criteria used in extant literature are identified and classified into a comprehensive benchmark framework. Moreover, the industrial case studies are discussed with the most critical criteria of MSS for different industries and which strategy is most suitable for the respective industries based on these criteria. Table 1 presents different MCDM techniques and their hybrid applications for solving MSS problem that can help researchers in identifying research gaps. Future research can be directed at addressing the limitation of MCDM approach employed in existing studies and comparing the differences in results obtained by the proposed approach. Different industrial case studies with considered maintenance strategy alternatives are presented in Table 2, which can help researchers in identifying the industries that have not been studied yet. Moreover, not all of the existing studies are carried out by considering all the presented benchmark strategies, which can be addressed in future studies by interested researchers. More detailed discussion on research gaps is presented in the following section.

From the analysis of the extant literature, the authors could observe that the decision-making process adopted in numerous studies was limited to the classical maintenance strategies and not inclusive of aggressive maintenance strategy alternatives. To overcome these limitations and help maintenance managers in the decision-making, this study depicts the contemporary maintenance strategies, critical evaluation criteria and MCDM frameworks (employed to solve the MSS problem with industrial case studies) in a structured manner.

The purpose of this paper is to present a hybrid multi-criteria decision-making (MCDM) method, by combining the AHP, ELECTRE II, ELECTRE III, ELECTRE IV and Copeland techniques for road maintenance prioritization, in which the roads are evaluated and ranked based on various criteria. The proposed method is applied to four streets in Tehran, as a case study.

First, a set of criteria for road maintenance was determined and their weights were obtained using the AHP method. Four streets in Tehran, Iran were considered as alternatives and prioritized using the ELECTRE II, ELECTRE III and ELECTRE IV methods. Finally, the results of employing the three methods were integrated using the Copeland method and a final result was obtained.

The findings of the study suggested that “road safety” is the most important criterion in maintenance and “traffic volume” and “pavement quality index (PCI)” have the second and third rank in importance. Moreover, “The width of the street” is the least important criterion in road maintenance. Additionally, the streets' final ranking was obtained using the proposed method.

The proposed method helps managers effectively assign their limited budget and resources to roads with higher maintenance priority and as the result, increase the roads efficiency.

In this research, eight main criteria were collected using previous researches and experts' opinions. Also, a new combination of different MCDM techniques is proposed in this research.

The purpose of this paper is to focus on the use of analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to select an optimum maintenance strategy for a textile industry.

The relative importance of multiple evaluation criteria and the extension of the TOPSIS are prioritized using AHP. The TOPSIS method is applied to compensate for the imprecise ranking of the AHP in the selection of a maintenance policy mix.

An efficient ranking of alternatives can be achieved for maintenance strategy selection through the combination of AHP and TOPSIS.

The paper highlights a new insight into multi‐criteria decision‐making techniques to select an optimum maintenance policy for a process industry with the use of a case study.

Many maintenance decisions require the evaluation of alternative solutions in terms of complex maintenance criteria such as cost, repairability, reliability and availability requirements. Such problems can be formulated as multi‐criteria decision‐making problems. The relative importance of maintenance criteria is difficult to assess, and therefore a sensitivity analysis becomes a necessity. The sensitivity analysis approach presented reveals some counter‐intuitive results and can considerably enhance the application of decision analysis in complex maintenance management.

In this paper an experience dealing with the analysis of maintenance outsourcing by means of multi‐criteria decision methods (MCDM) is reported. In particular, the analytic hierarchy process technique (AHP) is used as a managerial decision support system to select the best alternative between different outsourcing contracts in terms of maintenance services. The proposed methodology has been tested on an industrial case study dealing with an important italian brickwork. This application shows how the AHP is able to support the choice of the correct level of the maintenance activities outsourcing. In particular, the hierarchic decisional structure developed represents an instrument able to give a well balanced synthesis of several different factors that must be taken into account during this type of decision problem.

The purpose of this paper is to present issues and challenges of scheduled maintenance task development within the maintenance review board (MRB) process, and to find potential areas of improvement in the application of the MSG‐3 methodology for aircraft systems.

The issues and challenges as well as potential areas of improvement have been identified through a constructive review that consists of two parts. The first part is a benchmarking between the Maintenance Steering Group (MSG‐3) methodology and other established and documented versions of reliability‐centred maintenance (RCM). This benchmarking focuses on the MSG‐3 methodology and compares it with some RCM standards to identify differences and thereby find ways to facilitate the application of MSG‐3. The second part includes a discussion about methodologies and tools that can support different steps of the MSG‐3 methodology within the framework of the MRB process.

The MSG‐3 methodology is closely related to the RCM methodology, in which the anticipated consequences of failure are considered for risk evaluation. However, MSG‐3 considers neither environmental effects of failures nor operational consequences of hidden failures. Furthermore, in MSG‐3, the operational check (failure‐finding inspection) is given priority before all other tasks, whereas in RCM it is considered as a default action, where there is no other applicable and effective option. While RCM allows cost‐effectiveness analysis for all failures that have no safety consequences, MSG‐3 just allows it for failures with economic consequences. A maintenance program that is established through the MRB process fulfils the requirements of continuous airworthiness, but there is no foundation to claim that it is the optimal or the most effective program from an operator's point‐of‐view. The major challenge when striving to achieve a more effective maintenance program within the MRB process is to acquire supporting methodologies and tools for adequate risk analysis, for optimal interval assignments, and for selection of the most effective maintenance task.

The paper presents a critical review of existing aircraft scheduled maintenance program development methodologies, and demonstrates the differences between MSG‐3 and other RCM methodologies.

The purpose of this paper is to propose the best method of maintenance for electrical equipment of a building and especially elevators in order to achieve various benefits.

Literature review shows that there are various methods such as corrective maintenance, condition-based maintenance, time-based maintenance and reliability-centered maintenance (RCM) for the maintenance of electrical equipment of a building. Selecting the most appropriate maintenance method, especially in large buildings such as commercial ones, is a multiple-criteria decision-making problem. To solve this problem, the fuzzy analytic hierarchy process is used in the present study. It considers the uncertainties of the experts' judgments to provide an optimized maintenance method.

Theoretically, the RCM method will take a lot of time and cost. But in prioritizing the methods, RCM gained more points with regard to safety criteria and value added. The main finding of this study suggests that RCM can be used in the case of elevators and would improve the safety and reliability of buildings.

The authors believe that this study will be valuable because one of the problems in building maintenance complexes is the lack of knowledge about the best method to prevent the risks of elevators. The proposed method has compared with other methods in various sub-criteria, and the results have been fully analyzed.

The purpose of this paper is to demonstrate the use of two general purpose decision‐making techniques in selecting the most appropriate maintenance strategy for organizations with critical production requirements.

The Analytical Hierarchical Process (AHP) and the Analytical Network Process (ANP) are used for the selection of the most appropriate maintenance strategy in a local newspaper printing facility in Turkey.

The two methods were shown to be effective in choosing a strategy for maintaining the printing machines. The two methods resulted in almost the same results. Both methods take into account the specific requirements of the organization through its own available expertise.

The techniques demonstrated in this paper can be used by all types of organizations for selecting and adopting maintenance strategies that have higher impact on maintenance performance and hence overall business productivity. The two methods are explained in a step‐by‐step approach for easier adaptation by practitioners in all types of organizations.

The value of the paper is in applying AHP and ANP decision‐making methodologies in maintenance strategy selection. These two methods are not very common in the area of maintenance, and hence add to the pool of techniques utilized in selecting maintenance strategies.