Managing component degradation in series systems for balancing degradation through reallocation and maintenance

Abstract

We rewrite the stochastic response surface method proposed in Regis and Shoemaker (2007) in the context of our problem in Algorithm S.1. To emphasis that the long-run average operational cost C is a function of the reallocation and replacement thresholds (L, M ) and it is evaluated from simulations, we write it in Algorithm S.1 as Ĉ(L, M ). Recall that for a fixed combination of (L, M ), the simulation is replicated for m times. The estimated cost is then computed by Equation ( 12) (known degradation rate) or (13) (unknown degradation rate) in the main text. If the stochastic kriging model is used as the response surface, we use the two-stage estimation method in Ankenman et al. ( 2010) for fitting the nm simulation outputs, where we assume a Gaussian correlation structure as with Zhang et al. (2016). If the radial basis function (RBF) is used as the response surface, we use the method in Regis and Shoemaker (2007) for the data fitting.At step 4 of Algorithm S.1, we need to give a score to each candidate point in Ω n to select which points being further evaluated from simulations. In this study, we follow the procedure in Regis and Shoemaker (2007) to evaluate a candidate point from two criteria. First, we compute s max

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