Freight and Logistics
MIT's Center for Transportation and Logistics is just one of many research groups who study the dynamics of multinational corporations and global supply chains. The rise of online shopping has created a huge need for creative problem solving in last-mile delivery and distribution network design. MIT offers a number of programs for graduate and professional students related to freight and logistics, including an innovative online MicroMasters credential.
The research labs and faculty working in this area are shown below. You can see a full listing of the people and labs involved with the MIT Mobility Initiative by navigating to the people page and the labs page.
Center for Transportation and Logistics
For more than four decades, the MIT Center for Transportation & Logistics (MIT CTL) has been a world leader in supply chain management education and research. MIT CTL has made significant contributions to supply chain and logistics and has helped numerous companies gain competitive advantage from its cutting-edge research. Launched in 1973, the MIT Center for Transportation & Logistics (CTL) is a dynamic solutions-oriented environment where students, faculty, and industry leaders pool their knowledge and experience to advance supply chain education and research.
Computational and Visual Education (CAVE) Lab
The CAVE lab provides students, researchers, and decision makers with a more intuitive understanding of and access to quantitative methods to support strategic design, tactical planning and operational decision problems in the supply chain and logistics domain and related fields. Based on a newly created physical lab space at MIT CTL equipped with state-of-the-art visualization technology, the lab is developing interactive visual interfaces to data and analytical tools, addressing complex supply chain and logistics problems.
The lab enables research advances in three major domains:
Development, improvement and application of traditional quantitative methods in supply chain, logistics, and transportation decision making (network design, distribution systems, inventory management, risk management, etc.)
Adaptation and application of advanced data science methods (machine learning, network science, etc.) to large and diverse datasets to characterize, understand, predict, and improve the performance of complex supply networks, transportation and logistics systems
Behavioral analysis of human decision making in supply chain management, transportation and logistics in light of interactive visualization being used as a tool to communicate, analyze, and manipulate context- and problem-related information
Future Urban Mobility at SMART
The Future Urban Mobility IRG's grand challenge is to develop innovative mobility solutions that simultaneously tackle two opposing objectives: To improve the safety, comfort and time associated with transportation, getting individuals and good where they need to be, and when they need to be there; and to reverse the alarming, unsustainable energy and environmental trends associated with transportation, and devise transportation systems that materially enhance sustainability and societal well-being.
Humanitarian Supply Chain Lab
The mission of the MIT Humanitarian Supply Chain Lab is to understand and improve the supply chain systems behind public services and private markets to meet human needs. Based within the MIT Center for Transportation and Logistics, the Lab combines expertise in engineering, management, information technology, social science, economics, and other disciplines to drive practical innovation for humanitarian interventions. The lab has a diverse portfolio of projects to improve emergency response during crisis and to enable market development that improves resilience. Our theoretical and applied research is driven by active engagement with the private sector, government agencies, humanitarian, international development, and community organizations on several continents.
Intelligent Transportation Systems Lab
The MIT Intelligent Transportation Systems (ITS) Lab was established in 1990 by Professor Moshe Ben-Akiva. Since its inception, the ITS Lab has conducted numerous studies of transportation systems and developed network modeling and simulation tools. The lab's areas of research include discrete choice and demand modeling techniques, activity-based models, freight transport modeling, and data collection methods for behavioral modeling. Today, lab members are located at MIT's Cambridge campus and its first research center outside of Cambridge: the Singapore-MIT Alliance for Research and Technology (SMART) Centre.
Lincoln Laboratory - Transportation
The Transportation mission area at Lincoln Lab develops technology to enhance transportation safety and efficiency, supporting government sponsors in several domains, including flight safety and collision avoidance, unmanned aircraft systems, advanced air mobility, air transportation simulation, air traffic control and air traffic management, environmental impact of air traffic, weather sensing for air traffic control, aviation cyber security, and military logistics.
MIT Digital Supply Chain Transformation
Digital transformation is now a keystone of operational, organizational, and technological structures for companies and supply chains who desire to be competitive in the vision of the future business environment. Our work aims to support organizationally adaptable, technologically compatible, and economically viable transformation for improving performance.
The primary research examines new collaborative paradigms that arise while implementing different new digital technologies in supply chains. Our research domains are digital platforms, multidimensional collaboration, digital capabilities and Artificial Intelligence (AI) in supply chains. Our research fosters more visible, efficient, flexible and resilient networks. We apply quantitative research methodologies in order to assess how data-driven ecosystems create value.
The MIT FreightLab mission is to drive innovation into the freight transportation industry in order to reduce cost, minimize risk, and increase the level of service. Freight transportation is subject to highly volatile demand and costs that are typically outside of a firm’s ability to control or even influence. This is compounded by a dominant design in terms of how freight is historically procured and managed. FreightLab research focuses on working with companies to develop and implement real-world solutions to these challenges.
FreightLab objectives are to develop innovations in freight transportation planning and operations and drive them into practice. Recently, we have developed methods for forecasting both short term spot-market rates and longer-term contract rates. We are exploring alternative contract forms between shippers and carriers that increase the level of trust in the relationship and yield better results for both parties. Working with a wide range of shippers, carriers, and third-party providers, the freight lab team develops and delivers better ways to design, procure, and manage large-scale freight transportation systems.
MIT Sustainable Supply Chains
The MIT Center for Transportation & Logistics launched Sustainable Supply Chains in 2018 as an umbrella program that brings together our sustainability research, education, and outreach. Our goal is 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. We seek to improve visibility of supply chain impacts and develop strategies to help reduce them, so companies can better address consumer, political, and shareholder concerns. The lab has a wide portfolio of research projects including supply chain transparency, sustainable logistics, sustainable procurement, consumer purchasing behavior, and on. The research is inclusive of issues across the supply chain and spans social, environmental, and economic impact areas.
Megacity Logistics Lab
The Megacity Logistics Lab brings together business, logistics, and urban planning perspectives to develop appropriate technologies, infrastructures, and policies for sustainable urban logistics operations. Their work aims to promote new urban delivery models, from unattended home delivery solutions to smart locker systems, to click & collect services, to drone delivery. They are pushing the limits of existing logistics network designs as future city logistics networks need to support omni-channel retail models, smaller store formats, increased intensity of deliveries, coordinate multiple transshipment points, engage a wider range of vehicle technologies - including electric and autonomous vehicles - and support complex inventory balancing and deployment strategies.
Mobility Systems Center
The Mobility Systems Center, an MIT Energy Initiative Low-Carbon Energy Center, brings together MIT's extensive expertise in mobility research to understand current and future trends in global passenger and freight mobility. Approaching mobility from a socio-technical perspective, we identify key challenges, understand potential trends, and analyze the societal and environmental impact of new mobility solutions. Through developing, maintaining, and applying a set of state-of-the-art scientific tools for the mobility sector, the Center aims to assess future mobility transformations from a technological, economic, environmental, and socio-political perspective. Executive Director: Randall Field
Operations Research Center
The mission of the Operations Research Center is to impact the world by educating students in the fields of Operations Research and Analytics who will become leaders in either academia or industry, generate new knowledge via research that will be used in educating future generations of students around the world and impact society via research by solving some of the world's most significant problems.
Sociotechnical Systems Research Center
The MIT Sociotechnical Systems Research Center (SSRC) is an interdisciplinary research center that focuses on the study of high-impact, complex, sociotechnical systems that shape our world. SSRC brings together faculty, researchers, students and staff from across MIT to study and seek solutions to complex societal challenges that span healthcare, energy, infrastructure networks, environment and international development. Their mission is to develop collaborative, holistic and systems-based approaches that combine knowledge and expertise from engineering and social sciences.
Urban Last-Mile Logistics
Explores specific challenges of urban last-mile B2C and B2B distribution in both industrialized and emerging economies. Develops an in-depth understanding of the perspectives, roles, and decisions of all relevant stakeholder groups, from consumers, to private sector decision makers, to public policy makers. Discussion of the most relevant traditional and the most promising innovating operating models for urban last-mile distribution. Introduces applications of the essential quantitative methods for the strategic design and tactical planning of urban last-mile distribution systems, including optimization and simulation. Covers basic facility location problems, network design problems, single- and multi-echelon vehicle routing problems, as well as associated approximation techniques.
D-Lab: Supply Chains
Introduces concepts of supply chain design and planning with a focus on supply chains for products destined to improve quality of life in developing countries. Topics include demand estimation, process analysis and improvement, facility location and capacity planning, inventory management, and supply chain coordination. Also covers issues specific to emerging markets, such as sustainable supply chains, choice of distribution channels, and how to account for the value-adding role of a supply chain. Students conduct D-Lab-based projects on supply chain design or improvement. Students taking graduate version will complete additional assignments.
Data Science and Machine Learning for Supply Chain Management
Introduces data science and machine learning topics in both theory and application. Data science topics include database and API connections, data preparation and manipulation, and data structures. Machine learning topics include model fitting, tuning and prediction, end-to-end problem solving, feature engineering and feature selection, overfitting, generalization, classification, regression, neural networks, dimensionality reduction and clustering. Covers software packages for statistical analysis, data visualization and machine learning. Introduces best practices related to source control, system architecture, cloud computing frameworks and modules, security, emerging financial technologies and software process. Applies teaching examples to logistics, transportation, and supply chain problems.
Provides an introduction to supply chain management from both analytical and practical perspectives. Taking a unified approach, students develop a framework for making intelligent decisions within the supply chain. Covers key logistics functions, such as demand planning, procurement, inventory theory and control, transportation planning and execution, reverse logistics, and flexible contracting. Explores concepts such as postponement, portfolio management, and dual sourcing. Emphasizes skills necessary to recognize and manage risk, analyze various tradeoffs, and model logistics systems.
Provides an in-depth introduction to the fundamental concepts and techniques related to the design, procurement, and management of freight transportation. Examines freight transportation as a bridging function for a firm, considering the physical flow of raw materials and finished goods as well as connections to suppliers and customers. Also covers how freight transportation insulates a firm's core operations from external disruptions and variability of supply and demand.
Logistics Systems Topics
Provides an introduction to supply chain management from both analytical and practical perspectives. Taking a unified approach, students develop a framework for making intelligent decisions within the supply chain. Covers key logistics functions, such as demand planning, procurement, inventory theory and control, transportation planning and execution, reverse logistics, and flexible contracting. Explores concepts such as postponement, portfolio management, and dual sourcing. Emphasizes skills necessary to recognize and manage risk, analyze various tradeoffs, and model logistics systems. SCM.271 meets with SCM.260 but requires fewer assignments and lectures.