The Benefit of Capacity Pooling for Repairable Spare Parts

• Main Author: Sahba, Pedram
• Other Authors: Balcioglu, Baris
• Language: en_ca
• Published: 2012
• Subjects: Queueing Theory; Stochastic Processes; Spare Parts Provisioning; Maintenance; Capacity Pooling; 0546
• Online Access: http://hdl.handle.net/1807/36297

Capacity pooling in production systems, in the form of production capacity or inventory pooling, has been extensively studied in the literature. While production capacity pooling has been proven to be beneficial, the impact of inventory pooling has been less significant. These results cannot be easily extended to repairable systems due to fundamental differences between repairable and production systems. For one thing, in repairable systems, the demand rate is a function of the number of operational machines, whereas it is exogenous and constant in production systems. In this Thesis, to serve different fleets of machines possibly at different locations, we study whether repair shop pooling is more cost effective than having dedicated on-site repair shops for each fleet. In the first model, we consider transportation delays and related costs, which have been traditionally ignored in the literature. We include on-site spare-part inventories that operate according to a continuous-review base-stock policy. Our numerical findings indicate that when transportation costs are reasonable, repair shop pooling is a better alternative. Next, we model a pooled repair shop that fixes failed components from different k-out-of-n:G systems. We permit a shared spare parts inventory serving all systems and/or reserved spare parts inventories for each system; we call this a hybrid model. The destination for a repaired component can be chosen either on a first-come-first-served basis or by following a static priority rule. Our findings show that both hybrid policies are more cost effective than having separate repair shops and inventories for each system. We propose implementing the multilevel rationing (MR) policy in systems with shared inventory. The MR policy prioritizes classes, and stops serving a class from inventory if the inventory level is below the inventory threshold identified for that class. When there is no inventory, the repaired component is sent to the highest priority class among those with down machines. To approximate the cost of the MR policy, we study an M/G/1//N queueing system serving multiple classes of customers with an unreliable server. Our numerical findings indicate that the MR policy performs as well as the epsilon-optimal policy and outperforms the hybrid policies.