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

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.

Automated summary

This study examines the commonly used problems of Spare Parts Allocation (SPA) and Level of Repair Analysis (LORA) within Performance Based Logistics (PBL). A comprehensive multi-objective simulation-optimization model is developed for a military aircraft operations case study, resulting in significantly better results in terms of cost and flight hours compared to the SPA optimization approach alone. The proposed model offers a profit-centric approach and serves as an efficient decision support tool for both customers and suppliers in complex logistics support activities.