A risk-averse simulation-based approach for a joint optimization of workforce capacity, spare part stocks and scheduling priorities in maintenance planning. (December 2020)

• Record Type: Journal Article
• Authors: Turan, Hasan Hüseyin; Atmis, Mahir; Kosanoglu, Fuat; Elsawah, Sondoss; Ryan, Michael J.
• Is Part Of: Reliability engineering & system safety. Volume 204(2020)
• Publication Date: 2020-12
• Subjects: Maintenance planning -- Spare parts inventories -- Repair priority -- Risk-aversion -- Simulation-based optimization -- Variable neighborhood search; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.107199
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A joint maintenance planning problem is modelled and solved under uncertainty. The decision maker's risk attitude is incorporated into the objective function. A risk-averse simulation-based optimization approach is developed. The approach achieves an average optimality gap of 0.45 . A risk-averse decision maker tends to use more workforce capacity. Abstract: 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.