Optimizing maintenance and repair policies via a combination of genetic algorithms and Monte Carlo simulation

Marseguerra, M.; Zio, Enrico

doi:10.1016/s0951-8320(00)00007-7

Cited by 183 publications

(83 citation statements)

References 24 publications

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“…In the literature there exists papers that considered uncertainties in the environment to improve sustainability of systems. In [12,13,14,15], genetic algorithms and Monte Carol simulation were combined to take into account the reliability of system components. In these studies Monte Carlo simulation was used to evaluate the fitness of chromosomes by estimating the reliability of the system and maintenance status of system components.…”

Section: Related Literaturementioning

confidence: 99%

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

Kavakeb

Nguyễn

Benmerikhi

et al. 2014

The 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)

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Abstract-In this paper, we propose an improved memetic algorithm by combining an evolutionary algorithm (EA) with Monte Carlo simulation (MCS) to identify the robust number of vehicles in environments with shuttle transport tasks (ESTTs). ESTTs are very common settings where vehicles shuttle between pickup and delivery points to transport goods. Examples of ESTTs are military bases, warehouses, manufacturing floors, and container terminals. In this study, MCS works as a local search to take into account risks of disruptions and guide the EA towards more reliable solutions. The disruptions arise from changes in the travel time of vehicles which can be caused by any breakdown, collision, accident and deadlock. Identifying the robust number of vehicles can improve sustainability and reliability of ESTTs against possible changes. This paper improves our previous attempts in which we studied a combination of an EA and MCS. Results of those studies showed the process of sampling in MCS is very time consuming. This prevents the EA from having an accurate estimation of the robust solutions within reasonable time. This paper improves the performance of the EA to make it possible to reach high quality solutions in reasonable time to make it practical for the realworld applications. Firstly, it proposes a new sampling technique to generate samples that reflect the worst-case scenarios better. This helps the EA to find better robust solutions using the fewer number of samples. Secondly, it proposes a new adaptive sampling technique to adjust the number of samples during evolution. To evaluate the algorithm we tested it in one of the most common ESTTs: real-world container terminals. Experimental results show that by such improvements the performance of the EA is improved significantly, making the proposed algorithm perfectly usable for its real-world case studies.

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Section: Related Literaturementioning

confidence: 99%

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

Kavakeb

Nguyễn

Benmerikhi

et al. 2014

The 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)

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“…They express each activities-combination solution by a string of variable integers, where each variable represent a component of the multi-component system and the allele number on that variable represents the PM activity of that component (no action, simple preventive maintenance, and preventive replacement). Marseguerra and Zio [88] utilize GA to optimize the components' maintenance periods and number of repair teams. The fitness function, which is a profit function which accounts for the safety and economic performance of the plant, is evaluated by Monte Carlo simulation.…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

“…The main focus of their study was the tuning of the genetic algorithm parameters. Marseguerra and Zio [88] present an approach to the optimal maintenance and repair strategies of an industrial plant, in the face of reliability and economic constraints, based on the coupling of GA and Monte Carlo techniques. GA is utilized to optimize the components' maintenance periods and number of repair teams.…”

Section: Simulation Based Optimizationmentioning

confidence: 99%

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Celen

Djurdjanović

2012

ASME 2012 International Manufacturing Science and Engineering Conference

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In highly flexible and integrated manufacturing systems such as those in semiconductor manufacturing, there exist strong dynamic interactions between the equipment condition, operations executed on the equipment and the resulting product quality. These interactions necessitate a methodology that integrates the decisions of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper we aim to devise an integrated decision making policy for maintenance scheduling and production sequencing with the objective of maximizing an adaptive profit function, while taking into account operation-dependent degradation models and a production target. In order to obtain the optimal decision policy, a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system is used. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

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“…또한 수많은 구성품으로 이루어진 복합 시스템의 경우 유전자 알고리즘을 통해 보다 빠른 시간 안에 해를 도출할 수 있으며 이는 군 무기체계 및 철도 산업과 같이 수많은 구성품으로 이루어진 시스템의 예방정비 주기를 산출하는데 있어서 효과적이다 (Wang et al, 1996;Marseguerra and Zio, 2000 NHPP)을 따르는 것으로 알려져 있다 (Nakagawa et al, 1983). 즉, 고장 발생 전과 후의 시스템 노후화 과정에는 변화가 없으 며 증가형 고장률 분포의 경우 시간이 지남에 따라 고장 발생 횟수는 증가하게 된다.…”

Section: 서 론unclassified

Optimal Preventive Maintenance Period in Complex Systems in Considering Components Reliability Characteristic

Lee¹,

Lee²,

Lee³

et al. 2011

Journal of Korean Institute of Industrial Engineers

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Generally, the life-cycle cost of complex systems composed of several sub systems or equipments such as train, aircraft, weapon systems is spent much more during operation and maintenance phase than development phase. The maintenance cost for maintaining the availability and extending the life span of systems comprise a large proportion of systems operation cost. The cycle of preventive maintenance affects operation and maintenance cost a lot. In this study, we introduce a way minimizing life-cycle cost of systems by calculating more reliable preventive maintenance period than the results of previous study, using systems reliability data considered the reliability and failure effect ratio of sub-systems or components. We can solve the preventive maintenance period problem, known as NP-Hard, as quick as possible by using modified genetic algorithm than using other models introduced in previous study. †

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Optimizing maintenance and repair policies via a combination of genetic algorithms and Monte Carlo simulation

Cited by 183 publications

References 24 publications

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Optimal Preventive Maintenance Period in Complex Systems in Considering Components Reliability Characteristic

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