Joint maintenance and just-in-time spare parts provisioning policy for a multi-unit production system

• Published in: Annals of operations research Vol. 287; no. 1; pp. 351 - 377
• Main Authors: Salari, Nooshin, Makis, Viliam
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2020; Springer; Springer Nature B.V
• Subjects: Business and Management; Combinatorics; Cost analysis; Inspection; Maintenance; Markov analysis; Markov processes; Methods; Operations research; Operations Research/Decision Theory; Optimality criteria; Optimization; Original Research; Policies; Provisioning; Sensitivity analysis; Spare parts; Theory of Computation; Canada; Production system; Multi-unit system; Spare part provisioning; Semi-Markov decision process; Condition-based maintenance
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-019-03371-3

In this paper, two new joint maintenance and spare parts provisioning policies for a multi-unit production system are proposed. The production system consists of N identical, independent units, each subject to gradual deterioration. Production rates of the units depend on their operating states. Every maintenance action incurs a high set-up cost which includes the cost of sending a crew to the field, and it is therefore cost effective to maintain several units at the same time. The paper’s main contribution is an analytical modeling of a multi-unit production system and the development of effective joint maintenance and spare parts ordering policies for such system. The states of the units are observable through regular inspections and the maintenance and spare part ordering decisions depend on the number of the failed units and the number of available spare parts. The process is formulated as a semi-Markov decision process with the optimality criterion being the minimization of the total long-run expected average cost per unit time. The objective is to determine the optimal levels of the number of failed units to place an order for the spare parts or to initiate group maintenance, and to find the optimal inspection interval. Numerical examples are provided to illustrate the proposed optimization model and to compare the two maintenance policies. Sensitivity analysis is conducted to analyze the effect of several cost components on the optimal levels and on the long run expected average cost rate.