Modelling complex maintenance systems using discrete event simulation

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Modelling complex maintenance systems using discrete event simulation
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Reliability Engineering and System Safety 154 (2016) 160–170 Contents lists available at ScienceDirect Reliability Engineering and System Safety journal homepage: www.elsevier.com/locate/ress A novel approach for modelling complex maintenance systems using discrete event simulation Abdullah Alrabghi a,n, Ashutosh Tiwari b a b Department of Industrial Engineering, University of Jeddah, Jeddah, Saudi Arabia Manufacturing Department, Cranfield University, Cranfield, United Kingdom art ic l e i nf o a b s t r a c t Article history: Received 7 August 2015 Received in revised form 2 April 2016 Accepted 5 June 2016 Available online 8 June 2016 Existing approaches for modelling maintenance rely on oversimplified assumptions which prevent them from reflecting the complexity found in industrial systems. In this paper, we propose a novel approach that enables the modelling of non-identical multi-unit systems without restrictive assumptions on the number of units or their maintenance characteristics. Modelling complex interactions between maintenance strategies and their effects on assets in the system is achieved by accessing event queues in Discrete Event Simulation (DES). The approach utilises the wide success DES has achieved in manufacturing by allowing integration with models that are closely related to maintenance such as production and spare parts systems. Additional advantages of using DES include rapid modelling and visual interactive simulation. The proposed approach is demonstrated in a simulation based optimisation study of a published case. The current research is one of the first to optimise maintenance strategies simultaneously with their parameters while considering production dynamics and spare parts management. The findings of this research provide insights for non-conflicting objectives in maintenance systems. In addition, the proposed approach can be used to facilitate the simulation and optimisation of industrial maintenance systems. & 2016 Elsevier Ltd. All rights reserved. Keywords: Simulation Maintenance Discrete Event Simulation Rapid modelling 1. 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]. Abbreviations: CBM, Condition Based Maintenance; CM, corrective maintenance; DES, Discrete Event Simulation; METBF, Mean Elapsed Time Between Failures; OM, opportunistic maintenance; PM, preventive maintenance; TTF, Time To Failure; SA, Simulated Annealing n Corresponding author. E-mail addresses: aalrabghi@uj.edu.sa (A. Alrabghi), a.tiwari@cranfield.ac.uk (A. Tiwari). http://dx.doi.org/10.1016/j.ress.2016.06.003 0951-8320/& 2016 Elsevier Ltd. All rights reserved. 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 [11–13]. 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 A. Alrabghi, A. Tiwari / Reliability Engineering and System Safety 154 (2016) 160–170 161 Fig. 1-1. An existing modelling approach used in simulation studies. Adapted from [11–13]. 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. 2. Methodology 2.1. 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 ensure maintenance intervention is conducted only when needed based on an analysis of the asset's condition. Predictive maintenance is seen as a part of CBM. The condition of assets is analysed to plan future maintenance actions. OM is closely related to both PM and CBM. Essentially, opportunities such as shutdowns are seized to preventively maintain an asset. A considerable amount of literature has discussed the details of modelling each maintenance strategy and its implications on assets in the system. This includes the modelling of assets degradation, the degree to which a maintenance action can successfully detect a failure and the degree to which a maintenance action can restore the asset to as good as new [19,20]. However, in this paper we are considering a holistic view. Table 1 illustrates how the actions of a given maintenance strategy might affect assets in the system in different ways assuming the probability of occurrence of all failure modes does not change. The proposed approach enables the modelling of interactions amongst various maintenance strategies and their effects on the assets in the system. Thanks to the flexibility of DES, the proposed approach enables the construction of various maintenance systems based on models that appear in the literature. Classic examples include perfect/imperfect maintenance, perfect/imperfect inspections, dependencies amongst assets, effect of maintenance on product quality, effect of maintenance on production speed, various approaches to modelling asset degradation and inclusion/ exclusion of maintenance resources such as maintenance equipment, spare parts and technicians. 2.2. Discrete Event Simulation The term DES refers to a modelling technique where only changes in system states are represented. Essentially, it creates a queue of events that affect the system state. These events are arranged based on their timings. The simulation then moves through these events and apply the changes on the system without modelling the time between any two events. Examples of such events in a typical manufacturing system include the arrival of a part, the start and finish of cycle times on machines and the occurrence of breakdowns. Therefore, it is a dynamic simulation technique where changes in the system are represented over time. The reader is referred to [21] for more details on DES. DES has been applied successfully in a wide range of business Table 1 Interactions amongst maintenance strategies. CM PM OM CBM Might affect other maintenance strategies on the same asset? Might affect other maintenance strategies on the other assets? No Yes No Yes No No Yes No 162 A. Alrabghi, A. Tiwari / Reliability Engineering and System Safety 154 (2016) 160–170 and manufacturing applications. In fact, it is the most popular technique to model manufacturing systems [22]. The main features of typical DES software include modelling variability in statistical or empirical distributions and rapid modelling by providing built-in modules that accelerate the modelling process. In addition, a typical DES software enables visual interactive simulation where changes in the system are animated and users can interact during the simulation. Benefits of visual interactive simulation include better understanding of the model by visualising, interactive experimentation, improved communications to all stakeholders and the facilitation of model verification [23]. 3. 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 ¼ 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 maintenance data. There are no restrictions on the number of assets in the manufacturing system or the number of maintenance strategies defined for each asset. The assets can be either identical or non-identical. Similarly, maintenance strategies can be the same for all machines or each asset can have its unique maintenance strategy. The approach consists of three steps as follows: 1. Develop the simulation model The approach begins with modelling the manufacturing system. For example this might include assets, buffers and rules governing machine cycle times and movement of parts within the system. The required maintenance strategies and policies are then identified for each asset. This includes defining parameters for statistical distributions required by each maintenance strategy to facilitate the modelling of variability in Maintenance Actions (MA) whenever they occur. For example, CM strategy requires the sampling from a statistical distribution to obtain Mean Elapsed Time Between Failures (METBF) each time the asset fails. In addition, a sampling from a statistical distribution is required to obtain the repair time. Other variables can be defined if required such as the cost of conducting each MA. Other maintenance characteristics and assumptions can be modelled to reflect the required behaviour in the maintenance system. Examples include failure detection, effect of failures on production, administrative delays, safety rules and periodic tests. In addition, other aspects can be modelled as well such as spare parts, work shifts, repair teams and maintenance equipment. When the simulation is run, the simulation clock is advanced to the next scheduled event. If a MA is due on one of the assets in the system, the effects on the asset is managed in the next step. 2. Manage the effects of Maintenance Actions on the same asset Whenever a MA is due on asset i in the system, a check is conducted to confirm that the criteria is met for the MA to be executed. For instance, CBM requires the current relevant condition indicator to exceed a specific threshold in order for the MA to be conducted. Likewise, some PM policies will be skipped if the asset was broken down when the MA is due. Other criteria can be added depending on the maintenance system and its assumptions. Some examples include: availability of repair teams, availability of repair tools and availability of spare parts. If the criteria is not met, the current MA will be skipped, costs will be updated if required and the next MA of that maintenance strategy for asset i will be scheduled. However, if the criterion of conducting the MA is met, a check will be conducted to determine if the current MA was initiated by a maintenance strategy that affects other maintenance actions on the same asset. As illustrated in Table 1, maintenance strategies such as PM and CBM affect CM actions. The interactions between maintenance strategies can be implemented by accessing the event queue for asset i and altering the timing of Fig. 3-1. A generic approach for modelling maintenance strategies. A. Alrabghi, A. Tiwari / Reliability Engineering and System Safety 154 (2016) 160–170 the relevant SMA. The effects of the current MA on other assets in the system are managed in the next step. The current MA will be conducted on asset i after scheduling the next MA. Whenever a MA is conducted, costs are updated and samples are taken from the relevant distributions to schedule the new timing of an activity or define the repair time for a MA. 3. Manage the effects of Maintenance Actions on other assets The current MA might affect SMA on other assets in the system. In that case, a check is conducted to confirm the criteria are met for the effects to take place. The event queue for these assets is accessed in order to apply the required changes. Steps 2 and 3 are repeated during the simulation every time a MA is due on any asset in the system. The next section presents detailed approaches for modelling common maintenance strategies namely Time-Based PM, OM and CBM with periodic inspections. These detailed approaches are special cases from the generic approach described in this section. 3.1. Common cases 3.1.1. Time-based preventive maintenance In time-based PM, the asset is maintained periodically to minimise unexpected breakdowns. Fig 3-2 illustrates the approach for modelling a manufacturing system where time-based PM is applied. 1. Develop the simulation model As assets can still breakdown unpredictably, both CM and PM are defined as possible maintenance strategies for each asset. Variables related to CM include METBF, repair times and CM costs whereas variables related to PM include PM frequency, repair times and PM costs. As the simulation clock advances, two maintenance strategies are possible on each asset, either CM or PM. 2. Manage the effects of Maintenance Actions on the same asset When machines have an unscheduled breakdown, a CM duration is sampled to set the CM repair time, CM cost is added for asset i, and METBF is sampled to schedule the next CM. In addition, CM will be conducted on asset i which means it will not be available for production. However, when PM is due on asset i, PM duration is sampled to set the PM repair time and PM cost is added for asset i. 163 Additionally, a sample from the METBF distribution will be drawn and the next CM breakdown will be changed to reflect the fact that PM has occurred. Finally, PM will be conducted on asset i making it unavailable for use in the production system. Nonetheless, if the time of PM coincidentally occurred when asset i is broken down, the current PM will be skipped and the next PM will run as scheduled. In this case, the third step of the approach is not required since none of the maintenance strategies considered for asset i might have an effect on other assets in the system. 3.1.2. Opportunistic maintenance As a strategy, OM utilises the breakdown of as asset to maintain another asset. The approach for modelling OM is illustrated in Fig. 3-3. 1. Develop the simulation model CM and OM are identified as maintenance strategies for each asset. Variables related to CM include METBF, repair times and CM costs whereas variables related to OM include repair times and OM costs. When the simulation starts, the clock will advance running the simulation model until a CM becomes due to an asset in the system. The effects of CM on the same asset are managed in the next step. 2. Manage the effects of Maintenance Actions on the same asset The asset subjected to CM will be made unavailable to conduct the required maintenance activities. Additionally, CM costs will be incurred and the next breakdown will be scheduled. 3. Manage the effects of Maintenance Actions on other assets All other machines on OM strategy in the system will be stopped for OM during which an OM cost will be incurred and a sampling for OM duration will take place. In addition, the next breakdown will be rescheduled according to the METBF sampling. If OM coincidentally occurs while the asset has broken down it will be skipped without incurring any costs. 3.1.3. Condition Based Maintenance with Periodic Inspections CBM strategy aims to further enhance the overall performance of assets by ensuring maintenance interventions are conducted only when needed. This is achieved by monitoring the condition of the asset and intervening when the condition exceeds a pre-set threshold. Fig. 3-4 shows a modelling approach for CBM where the condition of assets is monitored by periodic inspections. Fig. 3-2. An approach for modelling time-based PM. 164 A. Alrabghi, A. Tiwari / Reliability Engineering and System Safety 154 (2016) 160–170 Fig. 3-3. An approach for modelling OM. 1. Develop the simulation model Both CM and CBM are defined as maintenance strategies for each asset. CM variables include METBF, repair times and CM costs whereas CBM variables include inspection frequencies, inspection costs, CBM thresholds, CBM repair times and CBM costs. CM and CBM effects are managed in the next step. 2. Manage the effects of Maintenance Actions on the same asset The path of CM is similar to the one discussed above in timebased PM. However, in this case, the degradation level for asset i is set to the normal operation level after each CM. 4. Case study application Notation MSi Maintenance strategy for machine i PMfreqi Preventive maintenance frequency for machine i Qi Order quantit ...
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Attached.

OUTLINE FOR MODELLING COMPLEX MAINTENANCE SYSTEMS USING
DISCRETE EVENT SIMULATION
The attached paper contains the following;
✓ Introduction
✓ The objective of the research
✓ The contribution of the research
✓ Methodology
✓ Modelling maintenance strategies
✓ Discrete Event Simulation
✓ Problem
✓ Key Results
✓ Conclusion
✓ References


1
Running header: MODELLING COMPLEX MAINTENANCE SYSTEMS USING DISCRETE
EVENT SIMULATION

MODELLING COMPLEX MAINTENANCE SYSTEMS USING DISCRETE EVENT
SIMULATION

Student’s Name:
Institution Affiliation:
Date

2
DISCRETE EVENT SIMULATION
MODELLING COMPLEX MAINTENANCE SYSTEMS USING DISCRETE EVENT
SIMULATION

a) Introduction
The currently used approaches for the modelling of complex maintenance systems are
overly over simplified making them not to reflect the complexity that exists in industrial systems.
This paper looks to propose a novel approach that can be used to help create models that help in
the creation of complex maintenance systems without oversimplifying assumptions. The system
will make use of the success experienced in the Discrete Event Simulation (DES). The findings of
the paper will give an insight into the non-conflicting objectives in the maintenance of systems.

i)

The objective of the research
The objective of this research is to fill the gap in the exist...

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