multi_modal_carbon_optimizer.py — Multi-Modal Sustainable Transport Optimizer
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2025
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| author | B, Britt |
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| contents | <pre><code>multi_modal_carbon_optimizer.py v1.0 — Multi-Modal Sustainable Transport Optimizer Features • Zero extra setup core — single file (numpy + scipy + matplotlib for visualization) • Realistic global hub distances via Haversine formula • Optimizes mode choice (truck/rail/air/sea) to minimize CO₂e emissions • Respects maximum delivery time windows per shipment • Optional cost-carbon trade-off parameter for Pareto exploration • Clear reporting of total emissions + avoided carbon vs truck-only baseline • Professional bar chart showing modal contribution and savings highlight Dependencies • Requires numpy>=1.21 • Requires scipy>=1.8 — milp solver • Requires matplotlib>=3.5 — only for --plot Intended for green logistics teams, sustainability officers, and quantum researchers studying constrained discrete choice problems with strong potential quantum advantage. Real usage: python multi_modal_carbon_optimizer.py python multi_modal_carbon_optimizer.py -p 35 --cost-weight 0.05 --plot python multi_modal_carbon_optimizer.py --pairs 50 --plot Made by Britt (2025) — MIT License</code></pre> |
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| id | zenodo_https___doi_org_10_5281_zenodo_18030258 |
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| publishDate | 2025 |
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| spellingShingle | multi_modal_carbon_optimizer.py — Multi-Modal Sustainable Transport Optimizer B, Britt carbon footprint optimization multi-modal transport sustainable logistics green routing co2 minimization transport mode selection emissions modeling quantum routing baseline supply chain sustainability MILP optimization ESG logistics single-file script quantum constrained optimization python cli tool <pre><code>multi_modal_carbon_optimizer.py v1.0 — Multi-Modal Sustainable Transport Optimizer Features • Zero extra setup core — single file (numpy + scipy + matplotlib for visualization) • Realistic global hub distances via Haversine formula • Optimizes mode choice (truck/rail/air/sea) to minimize CO₂e emissions • Respects maximum delivery time windows per shipment • Optional cost-carbon trade-off parameter for Pareto exploration • Clear reporting of total emissions + avoided carbon vs truck-only baseline • Professional bar chart showing modal contribution and savings highlight Dependencies • Requires numpy>=1.21 • Requires scipy>=1.8 — milp solver • Requires matplotlib>=3.5 — only for --plot Intended for green logistics teams, sustainability officers, and quantum researchers studying constrained discrete choice problems with strong potential quantum advantage. Real usage: python multi_modal_carbon_optimizer.py python multi_modal_carbon_optimizer.py -p 35 --cost-weight 0.05 --plot python multi_modal_carbon_optimizer.py --pairs 50 --plot Made by Britt (2025) — MIT License</code></pre> |
| title | multi_modal_carbon_optimizer.py — Multi-Modal Sustainable Transport Optimizer |
| topic | carbon footprint optimization multi-modal transport sustainable logistics green routing co2 minimization transport mode selection emissions modeling quantum routing baseline supply chain sustainability MILP optimization ESG logistics single-file script quantum constrained optimization python cli tool |
| url | https://doi.org/10.5281/zenodo.18030258 |