Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions

Martinod, Ronald; Bistorin, Olivier; Castañeda, Leonel; Rezg, Nidhal

doi:10.1016/j.cie.2018.07.019

Cited by 58 publications

(34 citation statements)

References 37 publications

(60 reference statements)

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“…Recently, the advance in computational algorithm and computer science have helped to solve and optimize the complicated problem [76][77], but the number of parameters is finite and simulation time isn't acceptable for some of the real-time systems. Industry 4.0 context provides big data and on the other hands, dynamic and resilience structures need the real-time response; thus optimization algorithms must be improved.…”

Section: Fig 4 Generic Architecture With H-cpps Control Loop [69]mentioning

confidence: 99%

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Farsi

Zio

2019

Int J Reliabl Risk Safe Theory App

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According to the development of Industry 4.0 and increase the integration of digital, physical and human worlds, reliability engineering must evolve for addressing the existing and future challenges about that. In this paper, the principle of Industry 4.0 is presented and some of these challenges and opportunities for reliability engineering are discussed. New directions for research in system modeling, big data analysis, health management, cyber-physical system, human-machine interaction, uncertainty, jointly optimization, communication, and interfaces are proposed. Each topic can be investigated individually, but this paper summarizes them and prepared a vision about reliability engineering for consideration and discussion by the interested scientific community.

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Section: Fig 4 Generic Architecture With H-cpps Control Loop [69]mentioning

confidence: 99%

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Farsi

Zio

2019

Int J Reliabl Risk Safe Theory App

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“…In other words Cmax j represents the IJQRM cost of the required resources to get the highest quality maintenance and to restore the reliability function of the component to its nominal value, R j ¼ 1. As a consequence, when the budget of a maintenance action for the jth component is equivalent to Cmax j , the executed maintenance action consists of replacing the component with a new one; hence, the value of the age reduction is restored, α ¼ 0 (Martinod et al, 2018).…”

Section: Development Formulation Of the Service Problemmentioning

confidence: 99%

“…In this paper, the proposed work analyses the demand for transport, for an optimal service-oriented maintenance plan; thus, the maintenance policy represents a set of parameters to qualitatively evaluate the proposed solution. Other work (Martinod et al , 2018) proposes a stochastic optimisation model in order to reduce the long-term total maintenance cost of complex systems considering several maintenance policies, but does not take into account the service-operational policy. The lack of studies evaluating service performance, as impacted by maintenance requirements, provided a motivation for the research.…”

Section: Introductionmentioning

confidence: 99%

Joint optimisation of operation and maintenance policies in an urban ropeway transport systems context

Martinod

Bistorin²,

Castañeda

et al. 2019

IJQRM

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Purpose The purpose of this paper is to propose a stochastic optimisation model for integrating service and maintenance policies in order to solve the queuing problem and the cost of maintenance activities for public transport services, with a particular focus on urban ropeway system. Design/methodology/approach The authors adopt the following approaches: a discrete-event model that uses a set of interrelated queues for the formulation of the service problem using a cost-based expression; and a maintenance model consisting of preventive and corrective maintenance actions, which considers two different maintenance policies (periodic block-type and age-based). Findings The work shows that neither periodic block-type maintenance nor an age-based maintenance is necessarily the best maintenance strategy over a long system lifecycle; the optimal strategy must consider both policies. Practical implications The maintenance policies are then evaluated for their impact on the service and operation of the transport system. The authors conclude by applying the proposed optimisation model using an example concerning ropeway systems. Originality/value This is the first study to simultaneously consider maintenance policy and operational policy in an urban aerial ropeway system, taking up the problem of queuing with particular attention to the unique requirements public transport services.

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“…Recently, there has been a growing interest in the maintenance modeling of multi-unit systems (Eruguz, Tan, & van Houtum, 2017;Martinod, Bistorin, Castañeda, & Rezg, 2018;Poppe, Boute, & Lambrecht, 2018;Zhang & Zeng, 2015). A review of the CBM policies for systems with multiple components can be found in Keizer, Flapper, and Teunter (2017).…”

Section: Introductionmentioning

confidence: 99%

Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems

Salari

Makiš

2020

Naval Research Logistics

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In this paper, a condition‐based maintenance model for a multi‐unit production system is proposed and analyzed using Markov renewal theory. The units of the system are subject to gradual deterioration, and the gradual deterioration process of each unit is described by a three‐state continuous time homogeneous Markov chain with two working states and a failure state. The production rate of the system is influenced by the deterioration process and the demand is constant. The states of the units are observable through regular inspections and the decision to perform maintenance depends on the number of units in each state. The objective is to obtain the steady‐state characteristics and the formula for the long‐run average cost for the controlled system. The optimal policy is obtained using a dynamic programming algorithm. The result is validated using a semi‐Markov decision process formulation and the policy iteration algorithm. Moreover, an analytical expression is obtained for the calculation of the mean time to initiate maintenance using the first passage time theory.

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Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions

Cited by 58 publications

References 37 publications

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Joint optimisation of operation and maintenance policies in an urban ropeway transport systems context

Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems

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