State of the art in simulation-based optimisation for maintenance systems

Abstract

Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on Preventive Maintenance and optimising Preventive Maintenance frequency that will lead to the minimum cost. Discrete Event Simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and Condition Based Maintenance especially in a production context.

A Novel Framework for Simulation-based Optimisation of Maintenance Systems

“…To extend the set of relevant publications, reference lists in resulting papers were searched for related papers. In total, ten publications were identified [1,10,[15][16][17][18][19][20][21][22].…”

“…In order to include papers published in other databases or those that did not use our search terms, citations in the review papers were traced. In total, ten relevant journal articles were incorporated [1,2,[7][8][9][10][11][12][13][14]. Survey papers were examined paragraph by paragraph with specific focus on review findings, research gaps and limitations and recommendations for further research.…”

“…A number of authors [1,8,14,25] have reported that the availability of accurate data is a challenge in maintenance optimisation. In practical situations, it is almost always necessary to make assumptions or approximations.…”

“…Surveys such as that conducted by Alrabghi and Tiwari [1] and Horenbeek et al [2] revealed that the approaches to optimise maintenance varied significantly in the literature. This includes a wide range of optimisation objectives, decision variables and optimisation algorithms.…”

“…Improving maintenance is seen as an investment that will have a positive impact on product quality, asset availability and asset productivity. Simulation-based optimisation has a strong potential in supporting maintenance managers to make the right decisions in complex maintenance systems [1].…”