Reliability optimization for series systems under uncertain component failure rates in the design phase

Abstract

. (2018). Reliability optimization for series systems under uncertain component failure rates in the design phase. International Journal of Production Economics, 196(2), 163-175. DOI: 10.1016163-175. DOI: 10. /j.ijpe.2017 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. AbstractWe develop an optimization model to determine the reliability design of critical components in a serial system. The system is under a service contract, and a penalty cost has to be paid by the OEM when the total system down time exceeds a predetermined level, which complicates the evaluation of the expected cost under a given reliability design. Furthermore, in the design phase for each critical component, all possible designs are subject to uncertain component failure rates. Considering the computational intractability of evaluating the system performance, we develop approximate evaluation methods which take the system uncertainty into account. Numerical results show that the method which includes randomness in the number of failures, failure rates and repair times leads to efficient and accurate evaluations and to close-to-optimal design decisions when used in an enumeration procedure for the optimization problem. We also show that ignoring these three types of uncertainty may result in bad design decisions.

The value of installed base information for spare part inventory control

“…The scaled cost performance is calculated by the ratio of the total cost over the test set horizon and the total demand observed in this test set, and scaled by subtracting the unit order cost c. We report cost performance averaged over 100 simulated sample paths. In addition to the average performance, we also report 95% error bars of the 100 sample paths in Figure 2 (as in e.g., Ge et al, 2018). Analysis of the results leads to the following observations:…”

Spare parts inventory control under a fixed-term contract with a long-down constraint

“…They show that when planning for the steady-state situation, there is a high risk of not meeting the availability target within the contract execution period. Ge et al (2018) extend their work by explicitly taking into account the uncertainty in the component failure rates, the stochastic nature of the number of failures during a service contract period, and the stochastic repair times. To allow for fast approximate evaluation, they develop the fulluncertainty method and show the benefits of taking the variation occurring during the contract period into account.…”

Optimal redesign decisions through failure rate estimates

“…Jin & Wang, 2012 propose a model to minimize LCC under PBC in the presence of uncertain system usage. Ge, Peng, van Houtum, and Adan (2018) propose a model to choose the optimal component design for a serially connected component with uncertain failure rates. These studies consider the initial design decision only, without taking into account the option to redesign the system after deployment.…”