Incorporating repair information into maintenance optimization models for repairable systems

2011 ◽  
Author(s):  
E. Lorna Wong ◽  
Andrew K. S. Jardine ◽  
Dragan Banjevic
Keyword(s):  
Optimization Models ◽  
Maintenance Optimization ◽  
Repairable Systems

Nonparametric preventive maintenance optimization models under earning rate criteria

1998 ◽  
Vol 30 (12) ◽  
pp. 1099-1108 ◽  
Author(s):  
TADASHI DOHI ◽  
TAKASHI AOKI ◽  
NAOTO KAIO ◽  
SHUNJI OSAKI
Keyword(s):  
Preventive Maintenance ◽  
Optimization Models ◽  
Maintenance Optimization

scholarly journals Maintenance Objective Selection Framework Applicable to Designing and Improving Maintenance Programs

2019 ◽  
Vol 43 ◽  
pp. 127-144 ◽  
Author(s):  
James Wakiru ◽  
Liliane Pintelon ◽  
Peter Muchiri ◽  
Peter Chemweno
Keyword(s):  
Real Life ◽  
Analytic Network Process ◽  
Optimization Models ◽  
Maintenance Cost ◽  
Maintenance Optimization ◽  
Business Context ◽  
Network Process ◽  
Selection Framework ◽  
Additional Value ◽  
Maintenance Requirements

Maintenance optimization is applied by organizations to develop robust maintenance programs while attempting to establish a trade-off between competing maintenance requirements and resources. For this reason, maintenance decisions derived from maintenance optimization models are adversely affected, if (a) the maintenance objective(s) applied as input to the optimization models is not dynamically reviewed as the organizations environment context changes, and (b) there is continued use of historical maintenance objectives, oftentimes the practice for organizations lacking a framework for selecting maintenance objectives (MO’s). To address this, an interactive maintenance objective selection framework for stakeholders that aligns with and considers changes in the organization’s business, operational, and technical context, where dynamic maintenance objectives are selected and prioritized for application in real-life maintenance optimization models is proposed. The framework uses an analytic network process (ANP) based methodology, for selecting the relevant MO’s in view of competing dynamic criteria, for instance, employing remanufactured spares to optimize availability and maintenance cost. The applicability of the framework is demonstrated in case studies of companies operating in diverse industries like aviation and manufacturing in Africa. The study highlights the effects of dependencies between competing maintenance objectives, where the dependencies invariably influence how organizations prioritize MO’s to use for maintenance optimization programs. The additional value of the proposed framework lies in assisting organizations select maintenance objectives applicable to the organization while considering competing objectives and evolving business context.

Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System

2019 ◽  
Vol 28 (2) ◽  
pp. 219-230 ◽  
Author(s):  
Imane Maatouk ◽  
Iman Jarkass ◽  
Eric Châtelet ◽  
Nazir Chebbo
Keyword(s):  
Genetic Algorithm ◽  
Preventive Maintenance ◽  
Optimization Problem ◽  
Parallel Systems ◽  
Optimization Models ◽  
Maintenance Optimization ◽  
Maintenance Period ◽  
Characteristic Parameters ◽  
Maintenance Schedule ◽  
The Cost

Abstract In this research, different optimization models are developed to solve the preventive maintenance (PM) optimization problem in a maintainable multi-state series–parallel system. The objective is to determine for each component in the system the maintenance period minimizing a cost function under the constraint of required availability and for a specified horizon of time. Four genetic models based on the cost associated with maintenance schedule and availability characteristic parameters are constructed and analyzed. They are genetic algorithm (GA), hybridization GA and local search (GA-LS), fuzzy logic controlled GA (FLC-GA), and hybridization FLC-GA and LS. The experiment analyzes and compares the efficiency between them. These experiments investigate the effect of the parameters of the GA on the structure of optimal PM schedules in multi-state multi-component series–parallel systems. Results show that the hybridization FLC-GA and LS outperform the other algorithms.

scholarly journals A Methodology for Maintenance Evaluation and Improvement of Repairable Systems in a Mine

2021 ◽  
Author(s):  
Uthman Said
Keyword(s):  
Preventive Maintenance ◽  
Failure Process ◽  
Maintenance Optimization ◽  
Repairable Systems ◽  
System Availability ◽  
Failure Characteristics ◽  
Dump Trucks ◽  
Maintenance Policies ◽  
Component Failures

In this thesis, a maintenance evaluation and improvement methodology is presented, which makes use of maintenance data to determine failure characteristics of repairable systems and the effectiveness of maintenance policies being conducted on them. The objective is to provide a way in which maintenance data can be collected, organized, cleaned and formatted to provide information on component failures analytics, system availability and utilization so as to determine flaws in maintenance strategies. The methodology also provides context for the study of maintenance effectiveness, and synthesizes its importance within the grander scheme of maintenance optimization of repairable systems. We consider a repairable system whose failures follow a Non-Homogenous Poisson Process (NHPP) with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. We assume the effects of different preventive maintenance on the system are not identical, and estimate the parameters of the failure process as well as the effects of preventive maintenance. Ultimately, the methodology serves to guide maintenance designers in measuring the effectiveness of current maintenance policies and providing granular analysis on current failure trends to arrive at data-driven options for maintenance improvement. The proposed methodology was applied to a real case study of four AC-powered dump trucks used at an underground mine in Sudbury, Canada.

Maintenance optimization models and criteria

2010 ◽  
Vol 1 (3) ◽  
pp. 189-200 ◽  
Author(s):  
Adriaan Van Horenbeek ◽  
Liliane Pintelon ◽  
Peter Muchiri
Keyword(s):  
Optimization Models ◽  
Maintenance Optimization
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