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The purpose of this paper is to propose an availability modeling method of complex multiple units system (CMUS) based on the multi-agent technique.

Based on the multi-agent technique, this paper describes the availability model structure for CMUS and develops agent-based models of components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff, as well as the communication method among the different agents. On the basis of the agent-based availability modeling theory, the availability simulation scheme of CMUS is given using MATLAB. Thus, the availability modeling theory of CMUS and its simulation method are developed. To demonstrate the applicability of the proposed availability modeling method, a numerical example is given.

The proposed agent-based modeling method is applicable to availability modeling of CMUS, including the modeling of component failure, maintenance tools/fields/staff, maintenance policy, and structural/economic dependence among components.

As a bottom-top, modular, expandable, and reusable modeling theory, the agent-based modeling method might be useful for availability modeling of different CMUSs in reality.

The multi-agent technique is introduced into availability modeling of multi-component systems in this paper. Thus, it is possible to model failure of many components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff together for availability analysis of complex systems of equipment.

Total productive maintenance, or TPM, represents a major shift in the way an organization approaches the maintenance function and implementation typically requires a significant change in organizational culture. Most references on TPM suggest a variety of ways to promote this cultural change, including top management support, training and changes in reward systems. Despite these efforts, many organizations still find it difficult to create the necessary change in culture. This paper proposes an additional means to help bring about the cultural change necessary to make TPM work: mathematical modeling. Using examples of four mathematical models in the maintenance field, the paper describes how such models might be used to promote this cultural change by making the potential benefits of TPM more tangible and objective to employees and by improving employees’ understanding of how their involvement in TPM can affect the organization and its customers.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

The simultaneous scheduling of resource‐constrained maintenance and operations is addressed in this work. The purpose of the paper is to capture the uncertainty in the development of a model that schedules both preventive maintenance and operational activities. Fuzzy logic is employed to transform the human expertise into IF‐THEN rules.

The approach has the advantage of revealing semantic uncertainty with the associated non‐specifying measures. The methodology applied tracks the error values in terms of results in linguistic variable.

The results obtained indicate the feasibility of tracking the uncertain measures in the model discussed. Thus, the study may be applicable to both production system and transportation organizations that are engaged in both maintenance and operational activities.

The research has serious implication in terms of the ability to monitor the imprecision that were introduced in the previous models. This obviously provides a more reliable framework for researchers and practitioners interested in maintenance scheduling activities.

The paper is new in that it demonstrates the application of fuzzy logic in a form that was never documented.

Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues.

This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring.

This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective.

The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs.

A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

The purpose of this paper is to review the literature on maintenance optimization models and associated case studies. For these optimization models critical observations are made.

The paper systematically classifies the published literature using different techniques, and also identifies the possible gaps.

The paper outlines important techniques used in various maintenance optimization models including the analytical hierarchy process, the Bayesian approach, the Galbraith information processing model and genetic algorithms. There is an emerging trend towards uses of simulation for maintenance optimization which has changed the maintenance view.

A limited literature is available on the classification of maintenance optimization models and on its associated case studies. The paper classifies the literature on maintenance optimization models on different optimization techniques and based on emerging trends it outlines the directions for future research in the area of maintenance optimization.

The paper provides many references and case studies on maintenance optimization models and techniques. It gives useful references for maintenance management professionals and researchers working on maintenance optimization.

A major role of facilities management is ensuring the useability, reliability, and safety of the asset being managed. To achieve this management must use a system to control the maintenance function. The purpose of the paper is to identify and describe the various maintenance management models and systems available for facilities managers to consider.

Two comprehensive reviews of the literature were undertaken to categorise the various maintenance management models and identify popular models in practice.

The review identified 37 maintenance management models. From these, four were found to be popular: total productive maintenance (TPM), condition-based maintenance (CBM), reliability-centred maintenance (RCM), and condition monitoring (CM). While many thousands of papers can be found of these four models, the support in the literature for the remaining 33 models is very limited.

While providing a sound foundation for future research, the papers findings are based solely on reviewing literature.

For facilities managers seeking to expand their knowledge of a particular model or maintenance management systems in general, the paper provides a practical understanding.

Papers focused solely on identifying and describing maintenance management models are scarce and this paper makes a concerted attempt to link academic research with management practitioners.

The purpose of this paper is to review the literature on maintenance management and suggest possible gaps from the point of view of researchers and practitioners.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

The paper finds that important issues in maintenance management range from various optimization models, maintenance techniques, scheduling, and information systems etc. Within each category, gaps have been identified. A new shift in maintenance paradigm is also highlighted.

Literature on classification of maintenance management has so far been very limited. This paper reviews a large number of papers in this field and suggests a classification in to various areas and sub areas. Subsequently, various emerging trends in the field of maintenance management are identified to help researchers specifying gaps in the literature and direct research efforts suitably.

The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. The paper will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the importance of maintenance management

The purpose of this paper is to create a cost model at the general equipment level for industrial maintenance services.

The study is divided into two main sections. In the first phase the idea is to create a framework for a cost model with a literature review. The second, empirical part of the study is based on costing information from interviews and information given by network companies: a pulp mill, a maintenance company and an equipment provider. The maintenance of three different equipment processes is examined in the network through a case study, to get more specific information from real world situations to develop the model.

The findings concern the cost items that should be considered in the model, the structure of the model, and how the general cost model is constructed. During the research the model has been extended, and new cost categories included.

The cost model can be used in various performance measurement and decision‐making situations, such as maintenance service pricing, contract negotiations, outsourcing decisions, and life cycle cost management.

The cost model differs from traditional cost models. Earlier models have focused on the perspective of either the service provider or the customer, but not on both perspectives at the same time. However, in order to achieve a win‐win situation in a business network, open books practice is expected from each member of the network.

In this study, recovery factor was considered in the maintenance management model that integrates the quantitative method and qualitative concept. The model is developed to solve the practical parameters in a maintenance management task, such as the number of maintenance personnel and maintenance cycle time. The stochastic model is applied to construct the relationships among maintenance cycle, maintenance personnel allocation, human recovery factor, and a system's tolerance time. In addition, a simulated experiment was conducted to find out the supplementary parameters such as individual latent human error, individual critical human error, recovery rate, and a system's tolerance time. Since system availability is the criterion of this maintenance management model, the final solution of this model provides a system availability reference table of the combination of the number of maintenance personnel and the maintenance cycle time. This system availability reference table is a practical tool for maintenance managers.