A risk-averse simulation-based approach for a joint optimization of workforce capacity, spare part stocks and scheduling priorities in maintenance planning

We model a maintenance system consisting of one repair facility, where repairables are kept on inventory to serve assets to prevent downtime and increase availability. We seek optimal values of the repairable spare parts stocks and workforce capacity in the repair facility. Further, we simultaneously search for the best repair scheduling rule that minimizes total inventory holding and backorder costs associated with the downtime of assets. The joint optimization problem under study brings about two additional challenges: (i) the difficulty of analyzing such systems due to the lack of analytical (i.e., queuing) models, and (ii) the difficulty in incorporating the decision maker's risk attitude regarding uncertainties. We develop a risk-averse simulation-based optimization approach, in which the decision maker's risk attitude is modeled as a trade-off between the expected and the worst-case costs in the objective function. In the developed approach, the repairable spare part supply system is analyzed with a discrete-event simulation (DES) model. The DES model is coupled with an improved reduced variable neighborhood search (IRVNS) meta-heuristic that seeks the optimal values of decision variables. We compare the performance of the proposed risk-averse simulation-based optimization approach with several plausible benchmark methods commonly used in practice and with well-known meta-heuristic algorithms.