Multi-Aspect Probability Model of Expected Profit Subject to Uncertainty for Managerial Decision-Making in Local Transport Problems

• Published in: Logistics Vol. 9; no. 1; p. 39
• Main Authors: Holubčík, Martin, Falát, Lukáš, Soviar, Jakub, Dubovec, Juraj
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 13.03.2025
• Subjects: Combinatorial probabilities; Cost control; cost–benefit analysis; Decision making; Economic models; expected profit; Fatalities; Geometric probabilities; Infrastructure; Linear programming; Management; managerial decision-making; multi-criteria model; Neural networks; Probabilities; Project evaluation; Public transportation; Roads & highways; Traffic; transport problems; Transportation; Transportation planning; Trends; uncertainty; Vehicles; Slovakia
• DOI: 10.3390/logistics9010039
• ISSN: 2305-6290

Background: Governments face critical decisions regarding road remediation projects, requiring careful economic evaluation, especially in countries like Slovakia where road infrastructure is crucial for attracting foreign investment. These decisions are complex, involving short-term and long-term costs and revenues, along with inherent uncertainty about future outcomes. Traditional economic assessments often fail to capture the full scope of these factors, potentially leading to suboptimal choices. Methods: This study proposes four probability-based models: the Short-term Model (SM), Long-term-Short-term Model (LSM), Social Long-term-Short-term Model (SLSM), and Long-term-Short-term Model with a Time Aspect (TLSM). These models incorporate probabilistic functions to calculate expected costs and profits, considering various factors such as reparation costs, financial compensations, social costs, and time-related costs, as well as long-term benefits like increased investment and lives saved. Results: The proposed models were partially validated through an ex post analysis of a past road remediation project on road 1/18 (E50) under the Strecno castle cliff in Slovakia. The analysis demonstrated the models’ utility for multi-criteria decision-making in transportation problems, highlighting their ability to capture the complex interplay of economic and societal factors. Conclusions: The models enable governments to maximize societal benefit while mitigating potential risks, contributing to a more sustainable and efficient transportation sector. Future research could focus on refining the models and adapting them to other sectors beyond transportation.