A novel approach for modelling complex maintenance systems using discrete event simulation
Introduction
Maintenance aims to retain assets in their operational states. It has emerged as a fundamental success ingredient in the modern industry. Enhancing the performance of maintenance systems through modelling and optimisation has been the focus of a large volume of published studies.
Analytical modelling of maintenance prevailed for a long time. The foundations were laid by researchers such as Barlow and Proschan [1]. This was later developed extensively to include a large number of maintenance optimisation models [2]. In general, most of these models are developed for a specific system compromising of a single unit or several identical components [3]. However, maintenance systems in the industry are becoming much more complex which limits the applicability of analytical modelling techniques [4], [5].
The use of simulation to model maintenance systems is on the rise [6]. Simulation enables the modelling of complex behaviour and requires fewer assumptions compared to analytical modelling [7]. Although simulation is well-established in manufacturing in general, it appears to be still developing for maintenance [8].
Few researchers presented conceptual frameworks for modelling maintenance using simulation [9], [10]. These frameworks were developed for specific systems without detailing the modelling approach or providing numerical examples.
Fig. 1-1 shows a popular approach used in several DES studies [11], [12], [13]. The maintenance strategy and its parameters are entered manually in the simulation model. The simulation then samples a Time To Failure (TTF). If the scheduled maintenance intervention will occur before the failure, maintenance will be conducted resulting in updating the cost function, scheduling the next maintenance intervention and sampling a new TTF. However, if the breakdown occurred before the maintenance intervention, a CM will be conducted. The process continues running for the simulation run length. However, such approaches have a number of limitations. The maintenance system is modelled separately from other inter-related systems such as production and spare parts logistics. This in turn limits the utilisation of the dynamic feature of DES since interactions between machines and the effect of maintenance on production are not modelled. In addition, these approaches are used to model one maintenance strategy only. As a result, the choice of maintenance strategies cannot be optimised using frameworks such as the one suggested by Alrabghi and Tiwari [14].
Arab et al. [15] modelled both maintenance and production systems. However, they used manual DES calculations without utilising the strengths of available DES softwares such as rapid modelling and visual interactive simulation. On the other hand, Oyarbide-Zubillaga et al. [16] used an external tool to model the maintenance system and used that as an input to the DES model.
The examination of surveys in the field [4], [7], [17], [18] reveals a number of common research gaps relating to the modelling of maintenance systems:
- 1.
Modelling the maintenance system in isolation of other significant and inter-related systems such as production and spare parts management.
- 2.
Modelling various maintenance strategies and policies simultaneously.
- 3.
Making over-simplifying assumptions resulting in a model that cannot be implemented in real-world systems. Such assumptions include perfect maintenance/inspections, immediate maintenance actions and a single-unit system.
It appears as if these gaps are a result of the limitations present in the existing modelling approaches. Despite the potential of simulation to model complex maintenance systems, there remains a paucity of studies outlining adequate modelling approaches.
The present study fills a gap in the literature by proposing a modelling approach that can be used to model and optimise maintenance systems in practice. In addition to addressing the abovementioned limitations, the approach further exploits the advantages of DES such as rapid modelling and visual interactive simulation. As a result, the proposed approach is expected to pave the way for more advanced maintenance applications.
Section snippets
Modelling maintenance strategies
The degradation of operational assets is inevitable. Maintenance actions are designed to improve the condition of assets to keep it in a functional state. Often maintenance strategies can be categorised into CM, PM and CBM. In CM, the asset degrades until it breaks down unexpectedly. In some cases, the asset can breakdown suddenly without warnings. PM was introduced to minimise the effect of unscheduled breakdowns by interfering in a planned manner. CBM is an advanced strategy that aims to
A novel approach for modelling complex maintenance systems
Notation
MA: A single maintenance action resulting from a maintenance strategy.
SMA: A scheduled maintenance action resulting from a maintenance strategy.
n: Total number of assets in the system.
i: A single asset in the system where i = 1…n.
T: simulation run length.
A novel generic approach for modelling maintenance strategies is presented in Fig. 3-1. The approach assumes the availability of a valid DES model for the manufacturing system in interest as well as the availability of required
Case study application
Notation
MSi Maintenance strategy for machine i
PMfreqi Preventive maintenance frequency for machine i
Qi Order quantity for SPi
si Reorder level for SPi
SPi Spare part for machine i
In this section, we demonstrate the application of the modelling approach through a simulation optimisation study of a published case [24]. In order to optimise the maintenance system, we follow the simulation-based optimisation framework suggested by Alrabghi and Tiwari [14].
Discussion
This study set out with the aim of developing an approach for modelling complex maintenance systems using DES. A generic approach as well as approaches for common maintenance strategies were presented.
The proposed approach enables the modelling of the complexity found in real maintenance systems. In particular, the approach enables the modelling of the following:
- •
Multi-unit manufacturing systems. Without restrictions on the number of units.
- •
Non-identical units. Without restrictions placed on the
Conclusions and future work
Existing approaches for modelling maintenance rely on oversimplified assumptions which prevent them from reflecting the complexity found in industrial systems. Such assumptions are related to the scope of the simulation model, the number of assets, the manufacturing and maintenance characteristics of assets or the number of applicable maintenance strategies in the model.
In this paper, we develop a novel approach for modelling complex maintenance systems. The proposed approach enables the
Acknowledgements
The authors would like to thank the University of Jeddah for funding this research.
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