STOCHASTIC EFFICIENCY MODELING OF MULTIMODAL TRANSPORT SYSTEMS USING AN INTEGRATED PERFORMANCE INDICATOR AND MONTE CARLO SIMULATION
https://doi.org/10.33815/2313-4763.2026.1.32.174-186
Abstract
The growing complexity and uncertainty of multimodal transport systems require the development of advanced methodological approaches for performance evaluation. This paper presents a structured analysis of contemporary research on multimodal transportation efficiency, focusing on deterministic, multi-criteria, and stochastic modeling approaches. The review reveals that deterministic models, although widely used, fail to adequately capture the inherent variability of logistics processes, leading to overly optimistic performance estimates. Multi-criteria approaches improve the comprehensiveness of evaluation but often remain limited by their deterministic nature and subjective weighting schemes. This study proposes a novel stochastic framework for assessing the performance of multimodal transport systems based on an Integrated Performance Indicator (IPE) that simultaneously accounts for delivery time, transportation cost, and reliability. The proposed approach formalizes the transport process as a stochastic dynamic system, where key parameters such as travel time, delays, and operational costs are modeled as random variables. To capture the inherent uncertainty, a Monte Carlo simulation method is employed, allowing the estimation of the probability distribution of the IPE rather than relying on deterministic point estimates. Additionally, a nonlinear extension is considered to account for the increased sensitivity of system performance to risk factors.Simulation results demonstrate that deterministic approaches tend to overestimate system efficiency, while stochastic modeling reveals a significant reduction in expected performance due to variability and risks. The findings confirm that incorporating stochastic modeling and integrated performance metrics provides a more realistic and robust basis for decision-making in multimodal logistics. The proposed framework can be applied to route selection, risk management, and strategic planning of transport operations, particularly in environments characterized by high uncertainty and dynamic disruptions.
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