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Fuel-System Reliability as a Missing Term in AI Route-Optimization Objective Functions for Hazmat and Emissions-Aware Logistics
Journal of Artificial Intelligence and Big Data
| Vol 6, Issue 1
Table 1. Numerical evaluation of candidate hazmatroutes across fuel-system degradation and conditioning regimes.
| Route Candidate | Metric | Nominal(η = 1.00) | Moderate Wear(η = 0.88) | Severe Fault(η = 0.76) | Dissolution(η = 1.08) |
| Route A (Highway) | Fuel Consumption (L) | 38.40 | 43.64 | 50.53 | 35.56 |
| CO2 Mass (kg) | 102.91 | 116.95 | 135.41 | 95.29 | |
| Risk / Cost ($) | 14.20 / $210 | 14.20 / $210 | 14.20 / $210 | 14.20 / $210 | |
| Route B (Arterial) | Fuel Consumption (L) | 34.20 | 38.86 | 45.00 | 31.67 |
| CO2 Mass (kg) | 91.66 | 104.15 | 120.60 | 84.87 | |
| Risk / Cost ($) | 28.50 / $185 | 28.50 / $185 | 28.50 / $185 | 28.50 / $185 | |
| Route C (Bypass) | Fuel Consumption (L) | 44.50 | 50.57 | 58.55 | 41.20 |
| CO2 Mass (kg) | 119.26 | 135.52 | 156.92 | 110.43 | |
| Risk / Cost ($) | 6.80 / $245 | 6.80 / $245 | 6.80 / $245 | 6.80 / $245 |