Maintenance optimization in industry 4.0

Pinciroli, Luca; Baraldi, Piero; Zio, Enrico

doi:10.1016/j.ress.2023.109204

Cited by 69 publications

(14 citation statements)

References 162 publications

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“…A review on maintenance optimization, asset maintenance planning and simulation-optimization based maintenance planning in Industry 4.0 era are available in [6,[35][36][37] respectively. Based on those review papers, this study focuses on asset preventive and corrective maintenance planning and workforce scheduling under uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

An Assets Maintenance-Workforce Planning Problem Under Uncertainty a Chance Constraints Assisted Simulation-Optimization Approach

Akl¹,

Rahman²,

Chakrabortty³

et al. 2023

Preprint

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Section: Literature Reviewmentioning

confidence: 99%

An Assets Maintenance-Workforce Planning Problem Under Uncertainty a Chance Constraints Assisted Simulation-Optimization Approach

Akl¹,

Rahman²,

Chakrabortty³

et al. 2023

Preprint

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“…The outlined sections provide a comprehensive overview of the user roles and access levels within the Digitalized Ship Maintenance Management System (Pinciroli et al, 2023) (ClassNk, 2013. The roles are categorized into ownership and operator levels, each further divided into management and user roles, ensuring a structured and controlled access environment based on the users' responsibilities and tasks.…”

Section: Operator Managementmentioning

confidence: 99%

Digitalized Ship Maintenance Management System Website Application Innovation Strategy Using Design Thinking Method

Wicaksono,

Pratiwi,

Wahyurini

2023

ijebd

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Purpose: The purpose of the study is to explore the benefits of digitalization, specifically in the context of PT Biro Klasifikasi Indonesia's (PT. BKI) digital transformation efforts. The focus is on how digitalization can enhance operational efficiency, productivity, customer service, and global competitiveness in the maritime industry. Method: The research employs the design thinking method to identify and address customer needs and challenges. It highlights the development of the "Digitalized Ship Management Maintenance System" application, showcasing its role in simplifying ship maintenance processes. The study uses a customer-centric approach to determine modules that provide effective solutions to identified issues. Result: The Digitalized Ship Management Maintenance System application is designed to streamline ship maintenance for customers. Utilizing design thinking, the study identifies customer problems and develops modules to address them. The application offers direct integration with PT. BKI's survey status, strengthening its international classification standing. Additionally, it supports government data integration, ensuring safety and environmental protection in Indonesia's maritime areas. Implication: The implications of the study underscore the significance of digital transformation for companies in the maritime sector, particularly for PT. BKI. The "Go Global" and "Big Top 5 Asia" targets align with the broader benefits of digitalization, including enhanced operational efficiency, global market access, and improved customer service. The developed application serves as a tangible outcome of the digitalization process, contributing to PT. BKI's competitiveness and its role as a trusted classification body.

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“…Maintenance optimization can be classified in different ways. A recent thorough classification can be found in [16]. Concerning the optimization outcome and decision variables, the discrete maintenance model can be classified as a process that finds optimized parameter values defining a single maintenance strategy selected a priori; e.g., in this paper, the type of action performed (repair, replacement).…”

Section: Discrete Maintenance Modelmentioning

confidence: 99%

Really Ageing Systems Undergoing a Discrete Maintenance Optimization

Briš

Jahoda

2022

Mathematics

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In general, a complex system is composed of different components that are usually subject to a maintenance policy. We take into account systems containing components that are under both preventive and corrective maintenance. Preventive maintenance is considered as a failure-based preventive maintenance model, where full renewal is realized after the occurrence of every nth failure. It offers an imperfect corrective maintenance model, where each repair deteriorates the component or system lifetime, the probability distribution of which gradually changes via increasing failure rates. The reliability mathematics for unavailability quantification is demonstrated in the paper. The renewal process model, involving failure-based preventive maintenance, arises from the new corresponding renewal cycle, which is designated a real ageing process. Imperfect corrective maintenance results in an unwanted rise in the unavailability function, which can be rectified by a properly selected failure-based preventive maintenance policy; i.e., replacement of a properly selected component respecting both cost and unavailability after the occurrence of the nth failure. The number n is considered a decision variable, whereas cost is an objective function in the optimization process. The paper describes a new method for finding an optimal failure-based preventive maintenance policy for a system respecting a given reliability constraint. The decision variable n is optimally selected for each component from a set of possible realistic maintenance modes. We focus on the discrete maintenance model, where each component is realized in one or several maintenance mode(s). The fixed value of the decision variable determines a single maintenance mode, as well as the cost of the mode. The optimization process for a system is demanding in terms of computing time because, if the system contains k components, all having three maintenance modes, we need to evaluate 3k maintenance configurations. The discrete maintenance optimization is shown with two systems adopted from the literature.

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Maintenance optimization in industry 4.0

Cited by 69 publications

References 162 publications

An Assets Maintenance-Workforce Planning Problem Under Uncertainty a Chance Constraints Assisted Simulation-Optimization Approach

An Assets Maintenance-Workforce Planning Problem Under Uncertainty a Chance Constraints Assisted Simulation-Optimization Approach

Digitalized Ship Maintenance Management System Website Application Innovation Strategy Using Design Thinking Method

Really Ageing Systems Undergoing a Discrete Maintenance Optimization

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