Methodology for determining the optimal ratio of own and leased fleet of construction equipment in the implementation of infrastructure projects

Abstract

The article proposes a comprehensive methodology for determining the optimal ratio of owned and rented construction equipment fleets in the implementation of infrastructure projects, aimed at minimizing total discounted costs while simultaneously controlling downtime risks and macroeconomic volatility. The study is based on an empirical dataset from 68 projects implemented in 2017-2023, with more than 500,000 records obtained from project and estimate documentation, financial statements, machine operation logs (ESM-1/2/3), lease and rental contracts, and service center data. The methodological design includes a systems approach and a techno-economic decomposition of TCO, economic-mathematical modeling, regression and correlation analysis, Monte Carlo simulation of key stochastic parameters (rental and borrowing rates, fuel costs, failure intensity), scenario analysis, and the development of an integrated efficiency coefficient (IEC) that combines costs, downtime risks, and sensitivity to external shocks. The results show that for short-term and most medium-term projects, minimum discounted costs are achieved when rental predominates, whereas maximum effectiveness by IEC is attained with a hybrid structure of 40% owned and 60% rented units; for long-term projects with high utilization, a shift toward ownership (up to 60-70%) is rational. A comparison of downtimes revealed a 5-6-fold reduction in downtime and a 6,5-7,5-fold decrease in related losses for rented equipment due to replacement pools and mature MRO systems. Sensitivity analysis confirmed high elasticity of the optimum to the interest rate (a 20% decrease in the borrowing rate raises the ownership share to approximately 55%), moderate elasticity to fuel prices, and the decisive role of project duration and reliability in the ultimate economic effect. Verification on completed cases showed a 12-18% reduction in total costs and in the probability of schedule slippage, confirming the applicability of the methodology for DSS and for the operational calibration of fleet structure over the project life cycle.