A Hybrid Simulation Study to Determine an Optimal Maintenance Strategy

Mulyana, Ig. Jaka; Gunawan, Ivan; Angelia, Yunia Vera; Trihastuti, Dian

doi:10.25077/josi.v19.n2.p91-100.2020

Cited by 3 publications

(3 citation statements)

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“…The time steps are larger influenced by the failure processes we seek to model and reducing computational cost. Another example of a hybrid simulation model for maintenance planning is Mulyana et al (2020), who apply a combination of Monte Carlo simulation and Systems Dynamics simulation for periodic preventative maintenance for the packing department of a flour mill.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation for Evaluating Long-term Maintenance Plans for Complex Systems

Rhodes-Leader¹,

Worthington²,

Griffiths³

et al. 2023

Proceedings of SW21 the OR Society Simulation Workshop

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The optimisation of maintenance plans for complex systems involving many components is not an easy problem. Analytical and mathematical models are possible, but often need to make significant assumptions and are unable to look at the distribution of costs and failures. This paper discusses a project in which a discrete-time simulation model was added onto an existing optimisation model in order to go beyond just estimating the mean performance and give a better picture of the risk and variability involved with potential maintenance plans.

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Section: Literature Reviewmentioning

confidence: 99%

Simulation for Evaluating Long-term Maintenance Plans for Complex Systems

Rhodes-Leader¹,

Worthington²,

Griffiths³

et al. 2023

Proceedings of SW21 the OR Society Simulation Workshop

View full text Add to dashboard Cite

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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Simulation, and now digital twins, excel in estimating parameters associated with complex and timedependent stochastic processes, e.g., large airport, highway, and warehouse operations. Within such contexts, we consider statistical inference, whereby one seeks to quantify the error in the obtained simulation estimate. Historically, statistical inference in simulation has been considered challenging because parameters needing estimation are often complicated, and simulation output often autocorrelated and non-normal. However, we argue that the remarkably simple idea of batching can be used as an "omnibus" inference device in simulation settings. Batching for inference works in three simple steps: (i) divide simulation output data into overlapping batches; (ii) construct parameter estimates from each batch; and (iii) use the batch estimates, after accounting for dependence, to perform statistical inference. As we describe in this paper, the resulting procedures are usually trivial to implement in software, and are provably correct and efficient. Batching ideas originated in the 1950s, and have enjoyed steady development in the simulation community since the 1970s mostly within the problem of variance estimation. Our thesis is that batching ideas have much wider utility, and that batching should be considered alongside other resampling ideas such as the bootstrap or the jackknife in modern statistics.

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“…Industri proses merupakan suatu sistem kompleks yang saling terkait satu sama lain. Kegagalan satu proses dapat mengakibatkan gangguan seluruh sistem (Mulyana et al, 2020). Industri proses sangat tergantung pada kesehatan mesin-mesin produksi.…”

Section: Pendahuluanunclassified