An approximate approach for the joint problem of level of repair analysis and spare parts stocking.

Research output: Working paper

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Abstract

For the spare parts stocking problem, generally metric type methods are used in the context of capital goods. Implicitly, a decision is assumed on which components to discard and which to repair upon failure, and where to perform repairs. In the military world, this decision is taken explicitly using the level of repair analysis (lora). Since the lora does not consider the availability of the installed base, solving the lora and spare parts stocking problems sequentially may lead to suboptimal solutions. We propose an iterative algorithm to solve the two problems. We compare its performance with that of the sequential approach and a recently proposed, so-called integrated algorithm. The latter finds optimal solutions for two-echelon, single-indenture problems. In our experiment, we use a set of such problems, and a set of multi-echelon, multi-indenture problems, for which we achieve a cost reduction of 3% on average (35% at maximum) compared with the sequential approach. Compared with the integrated algorithm, the gap is only 0.6% on average (5% at maximum), while the maximum computation time falls from 3 hours to 2.5 minutes. In a case study, we achieve a cost reduction of 10% compared with the sequential approach.
Original languageEnglish
Place of PublicationThe Netherlands
PublisherUniversity of Twente, Research School for Operations Management and Logistics (BETA)
ISBN (Print)9789038624969
Publication statusPublished - 2011

Publication series

NameBETA Working papers
PublisherUniversity of Twente, BETA Research School for Operations Management and Logistics
No.347

Keywords

  • Spare parts
  • IR-79706
  • METIS-281137
  • Inventories
  • Level of repair analysis
  • Service Logistics

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    Basten, R. J. I., van der Heijden, M. C., & Schutten, J. M. J. (2011). An approximate approach for the joint problem of level of repair analysis and spare parts stocking. (BETA Working papers; No. 347). The Netherlands: University of Twente, Research School for Operations Management and Logistics (BETA).