Our Research
Research Overview
The MIT Center for Transportation & Logistics founded Sustainable Supply Chains Lab in order to connect research outcomes to practical settings, enabling companies and stakeholders to leverage supply chains as a beneficial force to reaching global sustainable development goals. Our research seeks to improve visibility of supply chain impacts and develop strategies to help reduce them, so companies can better address consumer, political, and shareholder concerns.
Ongoing Projects
Electrification of the Supply Chain
Prepared by Dr. Josué C. Velázquez Martínez from MIT Center for Transportation & Logistics, Dr. Laura Palacios-Argüello, and Prof. Joachim Arts from Luxembourg Centre for Logistics and Supply Chain Management.
Our electrification of the supply chain initiative in Southern California aims to revolutionize transportation by overcoming EV range limitations. By integrating heavy electric trucks into first- and middle-mile operations, we’re shaping a future of eco-friendly logistics. Using mathematical models, we strategically place Electric Vehicle Charging Stations (EVCS) to optimize resilience and reduce costs, as evidenced by our promising results near the Port of Long Beach. Join us in reshaping transportation’s future.
Our groundbreaking initiative in Southern California is redefining transportation by integrating heavy electric trucks into first- and middle-mile operations. Through strategic placement of Electric Vehicle Charging Stations (EVCS) based on mathematical models, we’re reducing costs and optimizing resilience, as shown by promising results near the Port of Long Beach. Join us as we shape the future of eco-friendly logistics.
Sustainable Transportation
Poulad Moradi, Joachim Arts from University of Luxembourg Centre for Logistics and Supply Chain Management, and Dr. Josué C. Velázquez Martínez from MIT Center for Transportation & Logistics.
Is distance minimization good enough to reduce CO2 emissions?
This research focuses on leveraging a refined Comprehensive Modal Emission Model (CMEM) and transition from static to dynamic optimization, adapting to terrain elevations. This model reveals that the greenest path converges to an asymptotic state with increasing payload, achieved remarkably with a finite load.
In green logistics, the shorter distance does not necessarily mean less cost.
Consumer-Faced Logistics Sustainability
Andrés Muñoz-Villamizar, Josue C. Velazquez-Martínez, and Sergio Caballero-Caballero from MIT Center for Transportation & Logistics.
With the rapid growth of e-commerce and customer demand for fast shipping, last mile transportation has undergone for a heavy transformation, particularly for home deliveries. In this context, urges to find novel strategies to optimize vehicle utilization while maintaining timely delivery. Even when some retailers are incentivizing customers to embrace delayed home deliveries by offering economic incentives, current transportation systems fall short in accommodating orders with extended delivery windows. In this regard, we introduce a new consolidation-based delivery methodology that addresses these challenges.
Circular Supply Chains
Dr. Eva Ponce from the MIT Center for Transportation & Logistics.
MIT’s Circular Supply Chain Initiative focuses on leveraging circularity in supply chains to enhance sustainability, particularly regarding waste management. We assist companies in identifying and evaluating circular innovations, with a focus on omni-channel networks in the retail sector. By exploring models for recyclables, reusable packaging, and products, we aim to close the loop in supply chains, reducing single-use packaging waste and engaging customers. Join us in tackling the challenge of circular flows and reshaping supply chain sustainability.
Learn more about out previous projects
“Green Button Project” Consumer Preference for Green Last Mile Home Delivery
In the era of e-commerce and climate change, sustainability in last-mile delivery operations has a pivotal role. The boost of online shopping and increasing expectations of fast shipping means more vehicles on the road with lower utilization, higher frequency of...
Innovations in environmental training for the mining industry
The Sustainable Supply Chains team collaborated with the MIT Environmental Solutions Initiative and multinational mining company Vale to bring sustainability education to young engineering professionals in Brazil. Learn more about the program below and on MIT News....
State of Supply Chain Sustainability
The MIT Center for Transportation & Logistics (MIT CTL) and the Council of Supply Chain Management Professionals have teamed up to launch the annual State of Sustainable Supply Chains Report that will answer these questions — and help companies gain a better understanding of the importance of supply chain sustainability to their enterprises, industries, and the planet.
Circular Supply Chains
The Circular Supply Chain Initiative in MIT’s Center for Transportation & Logistics envisions and explores how companies can leverage supply chain circularity to contribute to their sustainability goals, with a special focus on waste. We help identify and assess...
Communicating Supply Chain Sustainability
A key question companies face is how to communicate their supply chain sustainability achievements externally. In the past decades, many companies have invested in enhancing the environmental sustainability and social responsibility of their supply chain. This process...
Supply Chain Traceability
In the wake of various global scandals in supply chains including contamination, labor scandals, and environmental impacts, motivations for supply chain traceability and transparency are growing.
Supply Chain Carbon Emissions
Carbon emissions are the de facto currency of climate change, yet many companies struggle to quantify carbon emissions, especially in the supply chain. Our researchers work with industry and stakeholders to develop and implement new methods to track and reduce carbon...
Last Mile Green Vehicle Assignment
Green vehicle assignment using Geospatial Analysis and Machine Learning algorithms