Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

Abstract

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.

Replacement and inventory control for a multi-customer product service system with decreasing replacement costs

“…Although, showed that the ( s, S ) inventory is not optimal even when cooperating with the optimal replacement decision. Cai, Yin, Zhang, and Chen (2017) proposed an appointment policy which reserves a spare to a specific unit when the deterioration reaches a particular threshold. In their method, the inventory level would be larger than the determined level under some conditions.…”

“…The MDP model, first developed by Bellman (1957) , is first proposed as an approach because it is useful for a broad range of problems where the payoff is partly based on a dynamic environment and partly based on decision maker's behaviors. The essential elements of an MDP model are the states, actions, transition probability, and the reward function, all of which are explained in detail below.…”

“…Existing literature on maintenance policies for large equipment or products considers replacement as an option to enhance the reliability ( Ahmad & Kamaruddin, 2012;Dekker, Wildeman, & Van der Duyn Schouten, 1997;Pan & Thomas, 2010 ). In the area of maintenance decision making, the use of condition-based maintenance relies on the continuous or periodic monitoring of an in-use product ( Grall, Bérenguer, & Dieulle, 2002;Chen & Wu, 2007 ;& Alaswad & Xiang, 2017 ). Some researchers have extended the new maintenance paradigm by monitoring multiple in-use products without inventory considerations ( Hong, Zhou, Zhang, & Ye, 2014;Keizer, Teunter, & Veldman, 2016;Shafiee, Finkelstein, & Bérenguer, 2015;Tian & Liao, 2011 ;& Verbert, Schutter, & Babuška, 2017 ).…”

Integrated Model Development of Spare Part Inventory and Maintenance

“…Many research of joint optimization of spare parts inventory and maintenance also presented not only in literature but also models have been proposed. Research proposed by [4] discusses about joint optimization of inventory and maintenance for critical and non critical spare part in multi echelon inventory. Another research proposed by (Sheng , 2016) that discussed about joint optimization of spare parts inventory and maintenance in the aviation field.…”

Joint maintenance-inventory optimisation of parallel production systems

“…Finally, turning to solution methodology, the use of simulation to analyse joint policies is common for realistic settings [33,37,[44][45][46]. Simulation has the flexibility to address the increasingly complex and dynamic nature of maintenance optimisation, and inventory optimisation adds to this complexity.…”