The objective of this research is to assess the influence of effective supply chain management on the success of transport infrastructure projects, considering the moderating effects of building information modeling (BIM) and environmental factors. Data were collected through questionnaires from construction projects and subjected to analysis to gain insights into the contributions of various supply chain management strategies to the overall performance of transport infrastructure projects, as well as how the presence of BIM and environmental considerations affect this relationship. The data were subjected to analysis using partial least squares structural equation modeling (PLS-SEM). The results of this investigation revealed a significant impact of supply chain management practices in the construction industry on the performance of transport infrastructure projects, with BIM and environmental considerations acting as moderators in this association. This study holds both practical and theoretical significance, as it contributes to the existing body of knowledge by shedding light on the role of supply chain management in construction and its influence on the success of transport infrastructure projects while also exploring the moderating influence of BIM and environmental factors. The findings provide valuable perspectives for improving supply chain management practices in construction, thereby enhancing the outcomes of transport infrastructure projects.

Container congestion remains a persistent operational challenge in seaports because berth, yard, and gate processes are tightly coupled, demand is volatile, and control actions often operate under delayed feedback. Reinforcement learning (RL) is increasingly proposed for adaptive terminal decision support, yet the literature still says little about the mechanism through which RL may reduce congestion in practice. This study therefore develops a simulation-based mechanism framework in which RL improves congestion outcomes primarily by increasing Operational Learning Stability (OLStab), defined here as the consistency and governability of learning-enabled operational decisions under variability and disruption. A queueing-based, gate-focused terminal simulator is used as the data-generating process, with gate congestion treated as a reduced-form proxy for broader terminal congestion pressure. The statistical layer is interpreted cautiously as an internal mechanism consistency check within synthetic data rather than as empirical causal identification. Results show that RL is strongly associated with higher OLStab and that OLStab is the dominant pathway linking RL to lower congestion pressure in the simulated environment. Logistics Efficiency (LE) is directionally consistent with congestion reduction in bivariate analysis but adds limited incremental mediation once OLStab is jointly modeled. The theorized moderation by Decision Latency Sensitivity (DLS) is not robustly recovered within the examined latency range. Overall, the study contributes a more bounded explanation of how RL may reduce congestion in a designed gate-focused terminal control environment and highlights learning stability as a practical screening criterion for future digital twin and pilot deployment studies.

This study explains how reinforcement learning (RL) contributes to congestion mitigation in inland freight terminal operations by testing a serial process model in which RL strengthens operational learning stability (OLStab), OLStab improves logistics efficiency, and logistics efficiency lowers congestion. Rather than presenting RL as a stand-alone congestion-reduction instrument, the paper examines a distinct inland-terminal application in which congestion emerges from interacting gate, yard, transfer, and dispatch frictions. Using a simulation-based explanatory design calibrated to a realistic macro-logistics context, and interpreting the results as simulation-informed evidence rather than direct field proof, the study analyzes 500 episode-level observations representing complete terminal runs under varying control conditions. The results show that RL positively affects OLStab, OLStab positively affects logistics efficiency, and logistics efficiency negatively affects congestion. The serial indirect pathway from RL through OLStab and logistics efficiency to congestion is statistically significant, whereas the direct effect of RL on congestion becomes non-significant once the mediators are introduced. Decision latency sensitivity does not significantly moderate the RL-to-OLStab relationship, suggesting that latency-related boundary conditions are more context-specific than the main capability pathway. The article contributes by offering a cautious simulation-based and mechanism-centered explanation of RL-enabled congestion mitigation in inland terminals, by treating OLStab as a simulation-grounded intermediate operational stability index, and by showing that the empirical pattern is better explained by theory-ordered simulator-level mechanism than by a residual direct RL effect.