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Opportunistic maintenance considering non-homogenous opportunity arrivals and stochastic opportunity durations

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  • Truong Ba, H.
  • Cholette, M.E.
  • Borghesani, P.
  • Zhou, Y.
  • Ma, L.

Abstract

Many systems and manufacturing processes undergo intermittent operation due to external factors (e.g. weather, low market prices), offering opportunities to conduct maintenance with reduced production losses. Making use of appropriate opportunities can thus lead to significant reduction in the total cost of maintenance and improvement in productivity. In this paper, an opportunistic maintenance (OM) model is developed considering two critical properties of real world opportunities: (i) non-homogeneous opportunity arrivals and (ii) stochastic opportunity duration. The model enables exploiting downtime cost savings from “partial†opportunities (stops shorter than the required maintenance time) thus extending the potential benefit of OM. The criteria for accepting maintenance opportunities are found by minimizing the single-cycle total cost. A closed form expression of the single-cycle total cost is derived for a given PM/OM policy and then a Genetic Algorithm is used to solve the optimization problem. Numerical results are presented to assess the benefit of opportunistic maintenance and the marginal benefit of considering partial opportunities. Results indicate that significant savings can be achieved by considering OM. Moreover, it is shown that the novel consideration of partial opportunities significantly increase the benefit of OM.

Suggested Citation

  • Truong Ba, H. & Cholette, M.E. & Borghesani, P. & Zhou, Y. & Ma, L., 2017. "Opportunistic maintenance considering non-homogenous opportunity arrivals and stochastic opportunity durations," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 151-161.
    • Handle: RePEc:eee:reensy:v:160:y:2017:i:c:p:151-161
    DOI: 10.1016/j.ress.2016.12.011
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    7. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.

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