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An optimal maintenance policy is key to the improvement of the availability and reliability of a system at an acceptable level without a significant increase in investment. However, the selection process is a complicated task because it requires in-depth knowledge on maintenance policies and on the technical requirements of maintenance. The difficulties and complexity of the selection process arise from the combination of conflicting maintenance constraints such as available spares, size of workforce, and maintenance skills. The paper aims to discuss these issues.

The proposed maintenance policy selection (MPS) model is separated into three major phases. The first phase identifies the critical system (CS) based on failure frequency. The failure mechanism in the CS is then analyzed by using a failure mode and effect analysis in the second phase. In the third phase, a multi-criteria decision making method, called the technique for order of preference by similarity to ideal solution, is adopted to identify an optimal maintenance policy that can minimize the failures.

Through a case study, preventive maintenance was selected as the optimal maintenance policy for the reduction of system failures. The results obtained from the case study not only provide evidence of the feasibility and practicability of the developed model, but also test the acceptability and rationale of the developed model from the industry perspective. Valuable knowledge and experience from employees were extracted and utilized through the proposed model to rank the optimal maintenance policy based on the capability to reduce failure.

The practicality of the MPS model is justified through an implementation in the palm oil industry. The application of the MPS model can also be extended to other manufacturing industries.

The purpose of this paper is to select the best maintenance policy for different types of equipment of a manufacturer integrating the fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) models.

The decision hierarchy of this research includes three levels. The first level aims to choose the best maintenance policy for different types of equipment of an acid manufacturer. These equipment pieces include molten sulfur ponds, boiler, absorption tower, cooling towers, converter, heat exchanger and sulfur fuel furnace. The second level includes decision criteria of added-value, risk level and the cost. Lastly, the third level comprises time-based maintenance (TBM), corrective maintenance (CM), shutdown maintenance and condition-based maintenance (CBM) as four maintenance policies.

The best maintenance policy for different types of equipment of a manufacturer is the main finding of this research. Based on the obtained results, CBM policy is suggested for absorption tower, boiler, cooling tower and molten sulfur ponds, TBM policy is suggested for converters and heat exchanger and CM policy is suggested for a sulfur fuel furnace.

This research develops a novel model by integrating FAHP and an interval TOPSIS with concurrent consideration of added-value, risk level and cost to select the best maintenance policy. According to the highlights of the previous studies conducted on maintenance policy selection and related tools and techniques, an operative integrated approach to combine risk, added-value and cost with integrated fuzzy models is not developed yet. The majority of the previous studies have considered classic fuzzy approaches such as FAHP, FANP, Fuzzy TOPSIS, etc., which are not completely capable to reflect the decision makers’ viewpoints.

The purpose of this paper is to develop a fuzzy analytic network process (FANP) model to select the maintenance policy of an acid manufacturing company.

Four maintenance strategies of Corrective Maintenance (CM), Time-Based Maintenance (TBM), Condition-Based Maintenance (CBM) and Shutdown Maintenance (SM) are investigated to be considered for seven equipment of the case study. These equipment are almost new and include boiler, molten sulfur ponds, cooling towers, absorption tower, converter, sulfur fuel furnace and heat exchanger. Chang’s extended analysis has been employed to deal with fuzzy data and analyze the fuzzy decision matrices. The proposed approach is applied to a sulfuric acid production plant and the suitable maintenance policy is found for all seven equipment of the company.

Based on the obtained results, the CBM policy is appropriate for high-risk (cooling tower) and high added value equipment (absorption tower). In addition, TBM is selected for boiler and converters while SM is selected for molten sulfur ponds. Finally, high-cost, low-risk and low added value equipment (sulfur fuel furnace and heat exchanger) are more appropriate with CM policy.

This research presents a novel idea to consider cost, risk and added value in the context of maintenance policy selection. From the methodological and theoretical features, this research offers new insights in this area since, to the best of the authors’ knowledge, no comparable study has been conducted before.

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

First by using improved AHP with fuzzy set theory, the weight of each criterion is calculated to overcome the criticism of unbalanced scale of judgments, uncertainty, and imprecision in the pair‐wise comparison process. Then this paper introduces a model that integrates improved fuzzy AHP with TOPSIS algorithm to support maintenance strategy selection decisions.

An efficient pair‐wise comparison process and ranking of alternatives can be achieved for maintenance strategy selection through the integration of AHP with fuzzy set theory and TOPSIS.

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

The purpose of this paper is to present a reliability centered maintenance (RCM) embedded integer linear programming approach (suited to the budget monetary resources allocation task) to the maintenance strategies mix selection for an industrial plant equipment.

The developed approach allows to determine the optimal maintenance strategies mix for a set of equipment in a more quantitative way than the classic RCM approach. The proposed model takes into account, for each potential failure determined using the FMECA and for each admissible strategy, the costs and the potential risk priority number (RPN) reduction. Finally, an industrial case concerning an Italian paper-mill plant is reported to demonstrate the effectiveness of the approach presented.

The paper finds that the application of the proposed approach allows to optimally allocate the budget monetary resources, determining which suitable maintenance practice apply to each failure, taking into account the costs of each strategy and the potential reduction of the RPN.

The proposed model permits to assign (during the budget monetary resources allocation task) to each failure the optimal strategy, among a set of suitable maintenance practices, considering the costs and the estimated RPN reduction.

The paper proposes a completely new RCM embedded approach to the maintenance strategies selection, in order to optimally allocate the budget monetary resources. This model overcomes the limits of the traditional RCM approach, taking into account quantitative aspects, i.e. the compatibility constraint between failures and policies, the maintenance strategies costs, and the RPN estimated reduction.

This paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.

The critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.

For a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.

Only three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.

The proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.

This research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

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.

The purpose of this paper is to primarily focus on labor in maintenance areas, addressing human rights issues, labor standards and safety standards. The main issue is to investigate how these factors are considered to drive the prioritization of maintenance interventions within maintenance plans. In particular, a method for criticality analysis of production equipment is proposed considering specific labor issues like age and gender, which can be useful to steer maintenance plans toward a more social perspective.

The authors focus on the two main social issues of SA 8000 norms, age and gender, exploring how these issues may drive the selection of maintenance policies and the relative maintenance plans. The research is conducted through fuzzy analytical hierarchy process (AHP) implemented within a failure mode effects analysis (FMEA).

The research is conducted through fuzzy AHP implemented within a FMEA. The maintenance plans resulting from the FMEA driven by social issues are evaluated by a benchmark of three different scenarios. The results obtained allowed the firm to evaluate maintenance plans, considering the impact on workers’ health and safety, the environment, social issues like gender and age.

One of the main limitation of this research is that it should also encompass maintenance costs under social and safety perspective. The method developed should be extended by further study of maintenance planning decisions subject to budget constraints. Moreover, it would be worth evaluating the effect of adopting more proactive maintenance policies aimed at improving plant maintainability in view of what emerged during the test case in the presence of an aged workforce and the subsequent need to prevent and/or protect people from hidden risks.

With reference to the results obtained from the two models of this scenario, the authors observed an increase of equipment criticality, from B class to the A class, and similarly from C class to B class. No equipment has reduced its criticality. This depends on the particular context and the relative weights of drivers indicated in its AHP matrixes.

The paper addressed the main social implication as well as other social issues represented by age and gender factors, which are normally neglected. The Action Research (AR) proved the effects resulted from considering either gender factor or gender and age factors at the same time for maintenance policy selection. All in all, an increase of criticality is evident even if “people” is a driver with less importance than “environment” and “structures.”

The present work focussed on a new definition of a criticality ranking model to assign a maintenance policy to each component based on workers’ know-how and on their status. The approach is conceived by the application of a fuzzy logic structure and AHP to overcome uncertainties, which can rise during a decision process when there is a need to evaluate many criteria, ranging from economic to environmental and social dimensions.

The purpose of this paper is to propose an empirical investigation targeting companies operating in Northern Italy and focusing on some main topics of maintenance.

Through a combined application of factor analysis and cluster analysis, the paper first identified the main approaches implemented by Italian companies with the purpose of preventing failures and reducing their consequences. Then, the paper grouped companies on the basis of such approaches, and derived the profile of each cluster, in terms of both the company's characteristics and some key maintenance elements, including the maintenance policy or the use of advanced techniques to make maintenance interventions more effective.

Results of this study suggest that the approach to maintenance management could be related to the implementation of specific maintenance policies, as well as to other topics, such as the company characteristics, the use of advanced techniques for enhancing maintenance effectiveness and the criteria used for selecting the maintenance policy.

The sample of companies investigated in this study is quite limited, and prevented the possibility of providing statistical evidence of all outcomes.

The outcomes of this study can be useful for practitioners to understand the positioning of companies towards the implementation of maintenance policies, as a function of their approach to maintenance management.

This study complements previous empirical works on maintenance by exploring the relationships between maintenance management, maintenance policies, the application of different tools and techniques for maintenance management, and the criteria adopted for the maintenance policy selection.

The purpose of this paper is to radically redesign the methodology of plant maintenance and develop a three phase framework. A framework for effective and continuous maintenance improvement is presented.

The first phase determines the key performance area, the second phase makes decision about the appropriate policy selection and third phase aims to generate capability in an organization to compete at world level. A case is presented here, of the implementation of a “maintenance reengineering framework”, developed with the aid of certain maintenance components.

The maintenance reengineering framework, when implemented, proved successful with a more than 90 per cent rise in production rate.

The paper contributes some guidelines which can be helpful in making decision about the selection of maintenance policy and for the achievement of world‐class effectiveness.

Maintenance reengineering (MR) framework includes the core concepts of preventive maintenance practices, total maintenance management and system perspectives of maintenance. Its implementation goes through three phases from identification of key performance area, via suitable maintenance policy selection to generating the capability for world‐class effectiveness. The experience of this pilot implementation in a cement company in India suggested the feasibility of this framework in elevating the performance of maintenance management.

This article offers a framework that addresses research on maintenance reengineering. It attempts to establish a relationship among the components of maintenance, which can bring potential benefits to the corporations of similar operations and nature.