A generic conceptual simulation model for maintenance systems

Duffuaa, Salih O.; Ben‐Daya, Mohamed; Al‐Sultan, Khaled S.; Andijani, A.

doi:10.1108/13552510110404512

Cited by 98 publications

(67 citation statements)

References 12 publications

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“…Simulation has traditionally been used as a tool to understand and perform experiments with a system. However, combining the simulation model with the optimization model ensures better and faster results [2,11,12,18].…”

Section: Refer To Equipment Bmentioning

confidence: 99%

Random preventive maintenance policy based on inspection for a multicomponent system using simulation

Marsaro

Cavalcante

2017

EiN

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Section: Refer To Equipment Bmentioning

confidence: 99%

Random preventive maintenance policy based on inspection for a multicomponent system using simulation

Marsaro

Cavalcante

2017

EiN

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“…In order to improve these functions, different models have been developed by researchers. Some of these models are simulation (Ntuen and Park 1999;Duffuaa et al 2001), integer programming (Alfares 1999), stochastic programming (Duffuaa and AlSultan 1997) and mixed-integer programming (Judice et al 2005) models. For instance, Barnett and Blundel (1981) investigated trade demarcation of a workforce at optimal level using simulation model.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mixed-integer Multi-objective Maintenance and Production Workforce Planning Model

Ighravwe

Oke

2017

Process Integr Optim Sustain

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This study tackles the dilemma of producing a cooperative workforce planning involving maintenance and production departments. Its focus is on instituting workforce equilibrium between maintenance and production departments. This is necessary in order to ensure the optimal utilisation of manufacturing resources (time, equipment and funds). This maintenance-production problem was modelled using a mixed-integer multi-objective approach. The model minimises the workforce, rework and scrap, inventory holding cost and machine usage costs while maximising the average achieved machine availability. The model accounts for expected products' demand, the amount of expected defective products, workforce size, rest period and finished good inventory. Due to the nonlinear relationships among the maintenanceproduction variables, a big bang-big crunch (BB-BC) algorithm and genetic algorithm (GA) were selected as solution methods for the model. The proposed model performance was validated in a household utensils manufacturing plant under the conditions of workers' rest and without rest periods considerations. Based on the results obtained, the BB-BC algorithm performed better than the GA in terms of fitness function and computational time. The model performance with workers' rest period consideration generated higher achieved machine availability value than when workers' rest period was not considered. The optimised maintenance-production variables results confirmed the model's capability to generate acceptable solutions for the case study.

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“…Duffuaa et al [9] provide valuable insight into the field of maintenance systems. They list all the activities performed by a typical maintenance department, and concede that simulation modeling in this area of study is lacking.…”

Section: Simulationmentioning

confidence: 99%

A Simulation Technique for Optimising Maintenance Teams for a Service Company

Visser

Howes

2012

The South African Journal of Industrial Engineering

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The maintenance of some sophisticated and complex equipment -for example, diagnostic equipment used in hospitals -is frequently outsourced to the original equipment supplier or another maintenance service provider. Companies that own such assets can either enter into a service agreement, or they can use service providers on a call-out basis. A fundamental problem for the provider of maintenance services is to determine the optimum number of maintenance technicians or artisans. An investigation was done for a service company that provides planned and breakdown maintenance services to hospitals and clinics that use specialised medical imaging equipment. Data on the duration of maintenance tasks and traveling costs were used to obtain an input distribution for a simulation model. The model that was developed can now be used to determine the number of technicians that would optimise the service company's profit. This paper discusses the features of the simulation model, as well as some results obtained with the basic simulation model. OPSOMMINGDie instandhouding van sommige gesofistikeerde en komplekse toerusting, soos byvoorbeeld diagnostiese toerusting wat in hospitale gebruik word, word dikwels uitgekontrakteer na die oorspronklike vervaardigers van die toerusting. Ondernemings wat sulke toerusting besit kan dan 'n diensleweringskontrak met die diensverskaffer aangaan, of gebruik maak van 'n uitroepstelsel. 'n Fundamentale probleem vir die diensverskaffer is om die optimum aantal tegnici te bepaal. 'n Ondersoek is gedoen vir 'n tipiese diensverskaffer wat beplande sowel as onbeplande instandhouding doen vir hospitale en klinieke wat gesofistikeerde optiese toerusting bedryf. Data vir die tydsduur van take sowel as reistyd is gebruik om insetverdelings te verkry vir die ontwikkeling van 'n simulasiemodel. Die model wat ontwikkel is kan gebruik word om die aantal tegnici te bepaal wat die wins vir die diensmaatskappy sal optimeer. Hierdie artikel bespreek die eienskappe van die simulasiemodel wat ontwikkel is, sowel as van die resultate wat met die basiese simulasiemodel verkry is.

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A generic conceptual simulation model for maintenance systems

Cited by 98 publications

References 12 publications

Random preventive maintenance policy based on inspection for a multicomponent system using simulation

Random preventive maintenance policy based on inspection for a multicomponent system using simulation

A Mixed-integer Multi-objective Maintenance and Production Workforce Planning Model

A Simulation Technique for Optimising Maintenance Teams for a Service Company

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