Optimization of Opportunistic Maintenance Policies in a Global Service

Lupo T; Passannanti A

Risultato della ricerca: Paper

Abstract

The recent developments of innovative approaches and methodologies in maintenance process management have defined new market opportunities, not only for the users of the maintenance services, but also for the companies that specialize in maintenance process management. Nowadays, these companies are increasingly offering their maintenance services (maintenance global services), allowing both the users and providers of the services to focus on their core skills and resources. The engineering of maintenance processes and the implementation of effective maintenance strategies improve quality of services, allow the efficient management of human resources and reduce the size of the spare parts supply, thereby allowing significant economies of scale. However, with increasing system complexity, such objectives can be carried out only using heuristic or meta‐heuristics solution techniques. In this paper, we examine the real case of an opportunistic maintenance policy, in which an unscheduled system outage is used to concurrently execute also other preventive maintenance in the system. The proposed methodology, applied in the maintenance global service field, is based on a genetic algorithm that aims to minimize total maintenance costs.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2008

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Preventive maintenance
Outages
Industry
Quality of service
Genetic algorithms
Personnel
Costs

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Optimization of Opportunistic Maintenance Policies in a Global Service. / Lupo T; Passannanti A.

2008.

Risultato della ricerca: Paper

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abstract = "The recent developments of innovative approaches and methodologies in maintenance process management have defined new market opportunities, not only for the users of the maintenance services, but also for the companies that specialize in maintenance process management. Nowadays, these companies are increasingly offering their maintenance services (maintenance global services), allowing both the users and providers of the services to focus on their core skills and resources. The engineering of maintenance processes and the implementation of effective maintenance strategies improve quality of services, allow the efficient management of human resources and reduce the size of the spare parts supply, thereby allowing significant economies of scale. However, with increasing system complexity, such objectives can be carried out only using heuristic or meta‐heuristics solution techniques. In this paper, we examine the real case of an opportunistic maintenance policy, in which an unscheduled system outage is used to concurrently execute also other preventive maintenance in the system. The proposed methodology, applied in the maintenance global service field, is based on a genetic algorithm that aims to minimize total maintenance costs.",
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AB - The recent developments of innovative approaches and methodologies in maintenance process management have defined new market opportunities, not only for the users of the maintenance services, but also for the companies that specialize in maintenance process management. Nowadays, these companies are increasingly offering their maintenance services (maintenance global services), allowing both the users and providers of the services to focus on their core skills and resources. The engineering of maintenance processes and the implementation of effective maintenance strategies improve quality of services, allow the efficient management of human resources and reduce the size of the spare parts supply, thereby allowing significant economies of scale. However, with increasing system complexity, such objectives can be carried out only using heuristic or meta‐heuristics solution techniques. In this paper, we examine the real case of an opportunistic maintenance policy, in which an unscheduled system outage is used to concurrently execute also other preventive maintenance in the system. The proposed methodology, applied in the maintenance global service field, is based on a genetic algorithm that aims to minimize total maintenance costs.

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