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Improving the computational efficiency of metric-based spares algorithms

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  • Nowicki, David R.
  • Randall, Wesley S.
  • Ramirez-Marquez, Jose Emmanuel

Abstract

We propose a new heuristic algorithm to improve the computational efficiency of the general class of Multi-Echelon Technique for Recoverable Item Control (METRIC) problems. The objective of a METRIC-based decision problem is to systematically determine the location and quantity of spares that either maximizes the operational availability of a system subject to a budget constraint or minimizes its cost subject to an operational availability target. This type of sparing analysis has proven essential when analyzing the sustainment policies of large-scale, complex repairable systems such as those prevalent in the defense and aerospace industries. Additionally, the frequency of these sparing studies has recently increased as the adoption of performance-based logistics (PBL) has increased. PBL represents a class of business strategies that converts the recurring cost associated with maintenance, repair, and overhaul (MRO) into cost avoidance streams. Central to a PBL contract is a requirement to perform a business case analysis (BCA) and central to a BCA is the frequent need to use METRIC-based approaches to evaluate how a supplier and customer will engage in a performance based logistics arrangement where spares decisions are critical. Due to the size and frequency of the problem there exists a need to improve the efficiency of the computationally intensive METRIC-based solutions. We develop and validate a practical algorithm for improving the computational efficiency of a METRIC-based approach. The accuracy and effectiveness of the proposed algorithm are analyzed through a numerical study. The algorithm shows a 94% improvement in computational efficiency while maintaining 99.9% accuracy.

Suggested Citation

  • Nowicki, David R. & Randall, Wesley S. & Ramirez-Marquez, Jose Emmanuel, 2012. "Improving the computational efficiency of metric-based spares algorithms," European Journal of Operational Research, Elsevier, vol. 219(2), pages 324-334.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:2:p:324-334
    DOI: 10.1016/j.ejor.2011.12.033
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    Cited by:

    1. Araceli Zavala & David Nowicki & Jose Emmanuel Ramirez-Marquez, 2019. "Quantitative metrics to analyze supply chain resilience and associated costs," Journal of Risk and Reliability, , vol. 233(2), pages 186-199, April.
    2. Riccardo Patriarca & Tianya Hu & Francesco Costantino & Giulio Di Gravio & Massimo Tronci, 2019. "A System-Approach for Recoverable Spare Parts Management Using the Discrete Weibull Distribution," Sustainability, MDPI, vol. 11(19), pages 1-15, September.
    3. Costantino, Francesco & Di Gravio, Giulio & Tronci, Massimo, 2013. "Multi-echelon, multi-indenture spare parts inventory control subject to system availability and budget constraints," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 95-101.
    4. Jan Block & Alireza Ahmadi & Xun Xiao & Uday Kumar, 2019. "Spares provisioning strategy for periodically replaced units within the fleet retirement period," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(3), pages 299-315, June.

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