The Development of a Concurrent Spare-Parts Optimization Model for Weapon Systems in the South Korean Military Forces

References

• Aronis K-P, Magou I, Dekker R, Tagaras G (2004) Inventory control of spare parts using a Bayesian approach: A case study. Eur. J. Oper. Res. 154(3):730–739.Crossref, Google Scholar
• Baluja S, Caruana R (1995) Removing the genetics from the standard genetic algorithm. Accessed June 9, 2016, http://www.esprockets.com/papers/CMU-CS-95-141.ps.pdf.Google Scholar
• Denson W (2006) Handbook of 217PLUS Reliability Prediction Models (The Reliability Information Analysis Center, New York).Google Scholar
• Efron B, Morris C (1977) Stein’s paradox in statistics. Sci. Amer. 236(5):119–127.Crossref, Google Scholar
• Graves SC (1985) A multi-echelon inventory model for a repairable item with one-for-one replenishment. Management Sci. 31(10): 1247–1256.Link, Google Scholar
• Healy J (2011) Reliability prediction procedure for electronic equipment. Telcordia Technologies Special Report SR-332, Telecordia Technologies, Inc., Bellcore, NJ.Google Scholar
• Hwang MH (2006) Modeling and simulation using DEVS#. Accessed April 4, 2015, http://xsy-csharp.sourceforge.net/DEVSsharp/DEVSsharpSim.pdf.Google Scholar
• Juan X, Hongfu Z, Lei S (2014) A multi-item, multi-echelon inventory allocation model for aircraft spare parts based on VARI-METRIC. Inform. Tech. J. 13(13):2218–2223.Crossref, Google Scholar
• Kaplan AJ (1980) Mathematics for SESAME model. Accessed June 9, 2016, http://www.dtic.mil/dtic/tr/fulltext/u2/a081931.pdf.Google Scholar
• Kotkin M (1993) SESAME Match Tutorial (U.S. Army Material Systems Analysis Activity, Aberdeen, MD).Google Scholar
• Lipowski A, Lipowska D (2011) Roulette-wheel selection via stochastic acceptance. Accessed June 9, 2016, https://arxiv.org/pdf/1109.3627.pdf.Google Scholar
• Menhert CF (1983) An evaluation of the United States Army SESAME and Swedish OPUS VII provisioning models. Master’s thesis, Naval Postgraduate School, Monterey, CA.Google Scholar
• Moon S, Hicks C, Simpson A (2012) The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study. Internat. J. Production Econom. 140(2):794–802.Crossref, Google Scholar
• Parametric Technology Corporation (PTC) (2015) Windchill. Accessed April 23, 2105, http://www.ptc.com/searchresults?q=windchill.Google Scholar
• Sherbrooke CC (1986) VARI-METRIC: Improved approximations for multi-indenture, multi-echelon availability models. Oper. Res. 34(2):311–319.Link, Google Scholar
• Sherbrooke CC (2004) Optimal Inventory Modeling of Systems: Multi-Echelon Techniques (Springer, New York).Google Scholar
• Smith JG (2003) Operational Availability Handbook, A Practical Guide for Military Systems, Sub-Systems, and Equipment (U.S. Department of the Navy, Washington, DC).Google Scholar
• Syswerda G (1989) Uniform crossover in genetic algorithms. Schaffer DJ, ed. Proc. Third Internat. Conf. Genetic Algorithms (Morgan Kaufmann Publishers, San Francisco), 2–9.Google Scholar
• Teter MD, Newman AM, Weiss M (2016) Consistent notation for presenting complex optimization models in technical writing. Surveys Oper. Res. Management Sci. 21(1):1–17.Crossref, Google Scholar
• Toledo T, Koutsopoulos H, Davol A, Ben-Akiva M, Burghout W, Andréasson I, Johansson T, Lundin C (2003) Calibration and validation of microscopic traffic simulation tools: Stockholm case study. Transportation Res. Record 1831(03–2189):65–75.Crossref, Google Scholar
• U.S. Department of Defense (1995) MIL-HDBK-217F N2 (U.S. Department of Defense, Washington, DC).Google Scholar
• Yoon H, Lee S (2011) The effect analysis of the improved VARI-METRIC in multi-echelon inventory model. Korean Management Sci. Rev. 28(1):117–127.Google Scholar
• Yoon KB, Sohn SY (2007) Finding the optimal CSP inventory level for multi-echelon system in Air Force using random effects regression model. Eur. J. Oper. Res. 80(3):1076–1085.Crossref, Google Scholar