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Multi-objective optimisation of spare parts allocation and level of repair analysis in performance-based logistics

Published online by Cambridge University Press:  16 February 2022

C. Malyemez Open the ORCID record for C. Malyemez [Opens in a new window]  and
Ö.F. Baykoç
C. Malyemez*
Affiliation:
Turkish Air Force, Ankara, Turkey
Ö.F. Baykoç
Affiliation:
Industrial Engineering Department, Gazi University, Ankara, Turkey
*
Email: [email protected]
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Abstract

In recent years, Performance Based Logistics (PBL) has been increasingly used to reduce the product lifecycle cost. As a result, the existing logistics analysis methods need to be reassessed due to the difference in PBL from the classical approach. This study considers the Spare Parts Allocation (SPA) and Level of Repair Analysis (LORA), which are the most commonly used problems within PBL, and are the subject of analysis. A comprehensive, multi-objective simulation-optimisation model is developed for a military aircraft operations case study, with the objectives of minimising total cost and maximising total flight hours. The Design of Experiment (DOE) method is used for determining simulation-optimisation parameters. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for the first time in order to select the best design points. Simulated Annealing (SA) was used for multi-objective optimisation with the SPA model being solved first. Then the LORA problem was added to this model as a decision variable and the effects of the integrated solution (SPA+LORA) were examined. The results show that the integrated solution yields remarkably better results in terms of flight hours and cost when compared to the SPA optimisation approach alone. Moreover, the proposed model provides a profit-centric approach and can be efficiently used as a decision support tool for both customers and suppliers for difficult and complex logistics support activities.

Keywords

Spare parts allocationlevel of repair analysisperformance-based logisticsmulti-objective optimisationTOPSIS
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1301 , July 2022 , pp. 1210 - 1221
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Kirk, R.L. and Depalma, T.J. Performance-based logistics contracts: A basic overview, CNA Corporation, 2005. Online: accessed 01 August 2021, https://www.cna.org/CNA_files/PDF/D0012881.A2.pdf Google Scholar
US DoD. Quadrennial defense review report, 2001. Online: accessed 01 August 2021, https://history.defense.gov/Portals/70/Documents/quadrennial/QDR2001.pdf?ver=AFts7axkH2zWUHncRd8yUg%3d%3d Google Scholar
Randall, W.S., Nowicki, D.R. and Hawkins, T.G. Explaining the effectiveness of performance-based logistics: A quantitative examination, Int. J. Logist. Manag., 2011, 22, (3), pp 324–348.CrossRefGoogle Scholar
Sols, A., Nowicki, D.R. and Verma, D. Defining the fundamental framework of an effective performance-based logistics (PBL) contract, Eng. Manag. J., 2007, 19, (2), pp 40–50.Google Scholar
Nowicki, D.R. Optimization models in support of performance based logistics (PBL) contracts, PhD Dissertation, Industrial Engineering, University of Wisconsin, 2008.Google Scholar
Hypko, P., Tilebein, M. and Gleich, R. Clarifying the concept of performance-based contracting in manufacturing industries: A research synthesis, J. Serv. Manag., 2010, 21, (5), pp 625–655.Google Scholar
Randall, W.S., Pohlen, T.L. and Hanna, J.B. Evolving a theory of performance-based logistics using insights from service dominant logic, J. Bus. Logist., 2010, 31, (2), pp 35–61.CrossRefGoogle Scholar
Mirzahosseinian, H. and Piplani, R. A study of repairable parts inventory system operating under performance-based contract, Eur. J. Oper. Res., 2011, 214, (2), pp 256–261.CrossRefGoogle Scholar
Sols, A., Romero, J. and Cloutier, R. Performance-based logistics and technology refreshment programs: Bridging the operational-life performance capability gap in the Spanish F-100 frigates, Syst. Eng., 2012, 15, (4), pp 422–432.Google Scholar
Selviaridis, K. and Wynstra, F. Performance-based contracting: a literature review and future research directions, Int. J. Prod. Res., 2014, 53, (12), pp 3505–3540.Google Scholar
Glas, A.H. and Kleemann, F.C. Performance-based contracting: Contextual factors and the degree of buyer supplier integration, J. Bus. Indus. Market., 2017, 32, (5), pp 677–692.Google Scholar
Hur, M., Keskin, B.B. and Schmidt, C.P. End-of-life inventory control of aircraft spare parts under performance based logistics, Int. J. Prod. Econ., 2018, 204, (C), pp 186–203.Google Scholar
Alfredsson, P. Optimization of multi-echelon repairable item inventory systems with simultaneous location of repair facilities, Eur. J. Oper. Res., 1997, 99, (3), pp 584–595.Google Scholar
Kusters, R.A.M. The design of a logistic support system, Master Thesis, Operations Management and Logistics, Eindhoven University of Technology, 2011.Google Scholar
Basten, R.J.I., Van der heijden, M.C. and Schutten, J.M.J. Joint optimization of level of repair analysis and spare parts stocks, Eur. J. Oper. Res., 2012, 222, (3), pp 474–483.Google Scholar
Basten, R.J.I., Van der heijden, M.C., Schutten, J.M.J. and Kutanoglu, E. An approximate approach for the joint problem of level of repair analysis and spare parts stocking, Ann. Oper. Res., 2012, 224, (1), pp 121–145.CrossRefGoogle Scholar
Bouachera, T. Whole life costing optimisation with integrated logistics support considerations, PhD Dissertation, Robert Gordon University, 2012.Google Scholar
Fan, J., Guo, L., Yang, Y. and Kang, R. EBO optimization considering the joint of LORA and spare stocks, Chem. Eng. Trans., 2013, 33, pp 631–636.Google Scholar
Triki, C., Alalawin, A. and Ghiani, G. Optimizing the performance of complex maintenance systems, 5th International Conference on Modeling, Simulation and Applied Optimization, Hammamet, 2013.Google Scholar
Alalawin, A., Ghiani, G., Manni, E. and Triki, C. Design of the logistics support of complex engineering systems, Maintenance Performance Measurement and Management, Lappeenranta, Finland, 2013.Google Scholar
Ghaddar, B., Sakr, N. and Asiedu, Y. Spare parts stocking analysis using genetic programming, Eur. J. Oper. Res., 2016, 252, (1), pp 136–144.Google Scholar
Liu, Y., Feng, Y., Xue, X. and Lu, C. Joint optimization of level of repair analysis and civil aircraft inventory system based on PSO algorithm, IOP Conf. Series: Materials Science and Engineering, 2019, 538. doi: 10.1088/1757-899X/538/1/012061 Google Scholar
Liu, W., Liu, K. and Deng, T. Modelling analysis and improvement of an integrated chance-constrained model for level of repair analysis and spare parts supply control, Int. J. Prod. Res., 2020, 58, (10), pp 3090–3109.CrossRefGoogle Scholar
Wang, R., Chen, G., Wu, J., Zhou, W. and Huang, Z. Joint optimization method of spare parts stocks and level of repair analysis considering the multiple failure modes, Appl. Sci., 2021, 11, 7254.Google Scholar
Cakir, B., Altiparmak, F. and Dengiz, B. Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm, Comput. Indus. Eng., 2011, 60, (3), pp 376–384.Google Scholar
Design expert 10, Stat-Ease Company, 2018.Google Scholar