Sustainable Multi-Modal Transportation and Routing focusing on Costs and Carbon Emissions Reduction

TL;DR

This paper introduces an ILP-based multi-modal transportation routing model that balances cost efficiency with carbon emission constraints, demonstrating trade-offs and practical implications for sustainable supply chain logistics.

Contribution

It presents a novel optimization model incorporating emission constraints into multi-modal routing, optimizing vehicle usage for sustainability and cost-effectiveness.

Findings

Tight emission constraints increase costs by about 8%.

Over 95% of vehicles used are rented.

The model effectively balances cost and environmental goals.

Abstract

Transportation plays a critical role in supply chain networks, directly impacting cost efficiency, delivery reliability, and environmental sustainability. This study provides an enhanced optimization model for transportation planning, emphasizing environmental sustainability and cost-efficiency. An Integer Linear Programming (ILP) model was developed to minimize the total transportation costs by considering organizational and third-party vehicles' operational and rental costs while incorporating constraints on carbon emissions. The model incorporates multi-modal transportation routing and emission caps to select the optimized number of organizational and rental vehicles of different modes in each route to ensure adherence to sustainability goals. Key innovations include adding carbon emission constraints and optimizing route selection to reduce overall emissions. The model was implemented using the Gurobi solver, and numerical analysis reveals a trade-off between cost minimization and carbon footprint reduction. The results indicate that adopting tight environmental policies increases the costs by around 8% on average while more than 95% of the vehicles utilized will be rented. These insights provide actionable guidance for industries aiming to enhance both economic performance and environmental responsibility.