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Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

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.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

This paper aims to highlight the expanding link between facility management (FM) and building automation and control systems (BACS) through a review of literature. It examines the opportunities and challenges of BACS for facility managers and proposes solutions for mitigating the risks associated with BACS implementation.

This paper reviews various research papers to explore the positive influences of BACS on FM, such as support with strategic decision-making, predictive maintenance, energy efficiency and comfort improvement. It also discusses the challenges of BACS, including obsolescence, interoperability, vendor lock-in, reliability and security risks and suggests potential solutions based on existing literature.

BACS offers numerous opportunities for facility managers, such as improved decision-making, energy efficiency and comfort levels in office buildings. However, there are also risks associated with BACS implementation, including obsolescence, interoperability, vendor lock-in, reliability and security risks. These risks can be mitigated through measures such as hardware and software obsolescence management plans, functional requirement lists, wireless communication protocols, advanced feedback systems and increased awareness about BACS security.

To the best of the authors’ knowledge, no prior academic research has been conducted on the expanding link between FM and BACS. Although some papers have touched upon the opportunities and challenges of BACS for FM, this paper aims to provide a comprehensive overview of these findings by consolidating existing literature.

Technological change drives transformation in most sectors of the economy. Industry 4.0 technologies have been applied at different stages of a building’s lifecycle. However, limited studies exist on their application in real estate facilities management (REFM). This study aims to assess the existing knowledge on the topic to suggest further research directions.

Scopus-indexed literature from 2013 to 2023 was examined and visualised using VOSviewer software to output quantitative (descriptive) results. Content analysis was used to complement the quantitative findings.

Findings indicated a concentration of research in China, Norway and Italy. The knowledge areas included three clusters: lifecycle integration and management, data curation and management and organisational and management capabilities. The benefits, challenges and support strategies were highlighted.

More collaboration is needed across countries and territories on technology integration in REFM. Future research using alternative methodologies is recommended, with a focus on adopting and non-adopting REFM organisations. Further, implications for facility managers, employees, technology suppliers or vendors, training, organisations and management exist.

Further, implications for facility managers, employees, technology suppliers or vendors, training, organisations and management exist.

The study reveals the knowledge base on technology use in REFM. It adds to the evidence base on innovation and technology adoption in REFM.

This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.

The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.

Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.

An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

The expansion of bike-sharing enterprises has led to a series of social problems, and due to the high breakage rate and high recycling cost of bike sharing, enterprises are reluctant to maintain them. Therefore, government regulation does play a leading role in maintaining bike sharing. This study’s purpose is to investigate how the government should regulate the bike-sharing enterprises that maintain bicycles.

Firstly, the authors assume that there is only one bike-sharing enterprise and establish a game model that the government regulates the enterprise. Furthermore, the authors extend the model to the case that there are two competing enterprises in the market. Finally, through numerical analysis, the influence of various factors on the government strategy and revenue is analyzed.

The authors find that the regulatory probability of two enterprises are regulated by the government is larger than that of one enterprise. When two bike-sharing enterprises compete without government regulation, both will choose the non-self-discipline strategy, thus falling into the Prisoner's Dilemma. If the government regulates them, then both enterprises will choose the self-discipline strategy. Finally, through numerical analysis, it is found that the self-discipline behavior of bike-sharing enterprises is related to the government regulatory probability, the cost of self-discipline, the probability of being reported and the penalties. Interestingly, the cost of government regulation will not affect the regulation probability of government.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

The chemical plant (CP) maintenance industry has been under increasing pressure by process designers to demonstrate its evaluation and information management of model checking (MC) on the durability’s performance and design of plant control instrument. This main problem has been termed as imperfect maintenance actions (IMAs) level. Although IMAs have been explored in interdisciplinary maintenance environments, less is known about what imperfect maintenance problems currently exist and what their causes are, such as the recent explosion in the Beirut city (4 August 2020, about 181 fatalities). The aim of this paper is to identify how CP maintenance environments could integrate MC within their processes.

To achieve this aim, a comprehensive literature review of the existing conceptualisation of MC practices is reviewed and the main features of information and communication technology tools and techniques currently being employed on such IMA projects are carried out and synthesised into a conceptual framework for integrating MC in the automation system process.

The literature reveals that various CP designers conceptualise MC in different ways. MC is commonly shaped by long-term compliance to fulfil the requirement for maintaining a comfortable durability risk on imperfect maintenance schemes of CP projects. Also, there is a lack of common approaches for integrating the delivery process of MC. The conceptual framework demonstrates the importance of early integration of MC in the design phase to identify alternative methods to cogenerate, monitor and optimise MC.

Thus far, this study advances the knowledge about how CP maintenance environments can ensure MC delivery. This paper highlights the need for further research to integrate MC in CP maintenance environments. A future study could validate the framework across the design phase with different CP project designers.

The purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.