English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a bi‐objective optimization model for finding the optimal number and optimal aperiodic times for the inspections of finite‐life repaira‐ ble systems when the availability of the component and the total maintenance cost are under consideration. The model utilizes the delay‐time concept under perfect inspection assumption. The defect arrival process is modelled using the nonhomogeneous Poisson process and the failure times are probabilistic. The solution to this problem is NP‐hard, therefore, a mutation‐based genetic algorithm has been designed to solve the model. The effectiveness of the model was demonstrated using seven illustrative examples and compared to an existing classical periodic inspection model that uses a fixed number of inspections. The results showed that the proposed model did better (in all of the attributes) than the aperiodic model that using a fixed number of inspec‐ tions. Furthermore, the results showed that the proposed model gave better results than a single‐objective aperiodic model. The proposed model is a general model that can be implemented with different rates of occurrence of defects and different delay‐time distributions. Also this model can be extend‐ ed easily to cover complex systems and imperfect inspection cases. © 2016 PEI, University of Maribor. All rights reserved.

A bi-objective inspection policy optimization model for finite-life repairable systems using a genetic algorithm