In the context of Industry 4.0, predictive maintenance increasingly relies on machine learning models to anticipate equipment failures and reduce unplanned downtime. This makes the selection of the most suitable ML model a multidimensional and complex decision problem, since models with comparable predictive performance may differ substantially in terms of failure detection, false positives (FP), false negatives (FN), and computational requirements. This paper proposes a reproducible multicriteria decision-making framework that integrates objective Entropy-based weighting with the TOPSIS method for ML model selection in predictive maintenance applications. Logistic Regression (LR), Random Forest (RF), XGBoost, and LightGBM are evaluated using two predictive-maintenance datasets: the MetroPT dataset, representing real industrial time-series data from an air production unit in a metro system, and the AI4I 2020 Predictive Maintenance dataset from the UCI Machine Learning Repository. The experimental protocol incorporates leakage-aware temporal evaluation for MetroPT, stratified repeated evaluation for AI4I, controlled hyperparameter optimization, statistical significance analysis, and separate assessment of predictive and computational performance. The resulting model rankings are further examined using the VIKOR method and a systematic weight-sensitivity analysis covering 56 perturbation scenarios at ±20% and ±50%. The results show that XGBoost achieves the highest TOPSIS closeness coefficient on MetroPT (Ci = 0.6097), whereas LightGBM ranks first on AI4I (Ci = 0.9837). The VIKOR analysis produces the same ranking as TOPSIS on MetroPT (Spearman ρ = 1.000) and shows a strong but not perfect agreement on AI4I (ρ = 0.800). The sensitivity analysis indicates that the MetroPT ranking remains unchanged across all ±20% perturbation scenarios, while three ranking reversals occur under the ±50% perturbations. For AI4I, the top-ranked model remains unchanged across the tested perturbation scenarios, although the complete ranking can vary under changes in criterion weighting. These findings demonstrate that ML model selection in predictive maintenance should not rely solely on conventional predictive metrics, and that the proposed framework provides a structured decision-support approach for jointly considering predictive and operational performance.
Zouhair Marmoucha, Mohamed El Khaili, A. Soulhi et al.· Applied Sciences· 0 citations
Improving emergency vehicle mobility in congested urban environments is a critical challenge for transportation systems. Although roadway capacity expansions, such as widening roads, are often deployed to reduce congestion, their impact on emergency response performance is not always guaranteed, especially when delays concentrate at critical intersections. This study investigates how roadway capacity expansion affects emergency vehicle performance by using a microscopic traffic simulation framework. The study was applied to a real urban corridor in Mohammedia, Morocco, to provide a solid base for simulations with real-world conditions. A SUMO model was calibrated to represent two roadway configurations: a baseline two-lane layout and a three-lane post-widening scenario. Traffic volumes from 1056 to 3520 vehicles per hour were simulated, and performance was assessed using three emergency-specific indicators: Emergency Response Time (ERT), Delay Ratio (DR), and Priority Mobility Index (PMI). An initial single-run comparison suggested a substantial ERT reduction under moderate demand (343.40 s to 270.90 s, 21.11%); however, a 30-seed replication with paired Wilcoxon signed-rank tests shows that this and nearly all other widening effects are not statistically distinguishable from stochastic simulation noise. Only one of 12 emergency vehicle comparisons (Priority Mobility Index at 18:00) reached significance, and it favored the baseline configuration; none of 12 general traffic comparisons improved significantly, and general traffic was significantly slower under the widened configuration at 22:00 (p < 0.01). A supplementary sensitivity analysis (±20% emergency vehicle demand share) further shows that Delay Ratio conclusions are considerably more sensitive to this assumption (up to 34% relative change) than ERT or PMI (under 8%). These findings indicate that, in this network, roadway capacity expansion alone does not deliver a statistically robust improvement in either emergency vehicle or general mobility, and that a persistent signalized-intersection bottleneck remains the dominant constraint irrespective of lane geometry. The study provides a replicable, statistically validated simulation framework for assessing roadway capacity expansion effectiveness and cautions against single-run comparisons, which can substantially overstate the causal effect of infrastructure interventions in microscopic traffic simulation studies.
Imane Chakir, Mohamed El Khaili, Adil El Arfaoui et al.· Future Transportation· 0 citations
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