Operations Research and Statistics
The leading operations research and statistics faculty and students at MIT are studying how new optimization models and solution methods can be used to improve the efficiency and safety of transportation systems. Applications range from long-term planning to real-time operations. Recent research includes simulation-based optimization, robust optimization and stochastic optimization with applications for ridesharing route design, transit scheduling and airline pricing.
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.
Robert N. Noyce Career Development Associate Professor
Control of Infrastructure Networks, Security of Cyber-Physical Systems, Applied Game Theory and Information Economics
Professor of Aeronautics and Astronautics and Engineering Systems
Aerospace Systems, Engineering Systems, Technology Development, Multidisciplinary Design Optimization
Abraham J. Siegel Professor of Management and Professor of Operations Management
Design and Planning of Manufacturing Systems and Supply Chains, Supply-Chain Optimization
Dugald C. Jackson Professor in EECS, Co-Director of the Operations Research Center
Online Optimization and Learning, Machine Learning, Decision Making Under Uncertainty
Director of the MIT Megacity Logistics Lab; Director of the MIT CAVE Lab
Multi-tier Distribution Network Design, Urban Logistics, Last-Mile Delivery, Urban Freight Policy, Data Analytics and Visualization
Professor of Management and Operations Research, Associate Dean of Business Analytics
Optimization, Stochastic Systems, Machine Learning, Robust Optimization, Transportation and Finance
Assistant Professor, Operations Research and Statistics
Stochastic optimization, data-driven decision-making, analytics, vehicle routing, transportation scheduling
William F. Pounds Professor of Management, EMBA Faculty Director, Operations Research Center Co-Director
Operations Management, Management Science, Pricing, Revenue Management, Supply Chains, Machine Learning, Optimization
Professor of Aeronautics and Astronautics
Design, Analysis, and Implementation of Control and Optimization Algorithms for Large-Scale Cyber-Physical Infrastructures
Leonardo Career Development Associate Professor, Department of Aeronautics and Astronautics
Nonlinear Estimation, Numerical and Distributed Optimization, and Probabilistic Inference
Theresa Seley Professor in Management Science at the Sloan School of Management at MIT
Continuous Optimization, Computational Complexity, Convexity, Computational Science, Mathematical Systems
Distinguished Professor and Department Head, EECS; Deputy Dean of Academics, Schwarzman College of Computing
Nonlinear and Convex Optimization: Theory and Algorithms; Game Theory;
Social and Economic Networks
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.
Data Science Lab
The Data Science Lab develops analytic techniques and tools for improving decision making in environments that involve uncertainty and require statistical learning. They achieve this vision by exploring theoretical foundations of operational problems and applying them in the development of algorithms that integrate machine learning and stochastic or deterministic optimization techniques. Their methods have been implemented by a large number of companies across a variety of industries such as Airlines, Insurance, Manufacturing and Retail.
Engineering Systems Laboratory
A part of the MIT Department of Aeronautics and Astronautics, the Engineering Systems Laboratory (ESL) studies the underlying principles and methods for designing complex socio-technical systems that involve a mix of architecture, technologies, organizations, policy issues and complex networked operations. Their focus is on aerospace and other systems critical to society such as product development, manufacturing and large scale infrastructures.
International Center for Air Transportation
The mission of the MIT International Center for Air Transportation is to undertake research and educational programs which discover and disseminate the knowledge and tools underlying a global air transportation industry driven by new technologies. Airline management, airport security, air transportation economics, fleet scheduling, traffic flow management and airport facilities development represent areas of great interest to the MIT faculty and are of vital importance to international air transportation. ICAT is a physical and intellectual home for these activities. The ICAT, and its predecessors, the Aeronautical Systems Laboratory (ASL) and Flight Transportation Laboratory (FTL), have pioneered several concepts in air traffic management and flight deck automation and displays that are now in common use.
JTL Urban Mobility Lab
The JTL Urban Mobility Lab at MIT brings behavioral science and transportation technology together to shape travel behavior, design mobility systems, and improve transportation policies. They apply this framework to managing automobile ownership and usage, optimizing public transit planning and operation, promoting active modes of walking and cycling, governing autonomous vehicles and shared mobility services, and designing multimodal urban transportation systems.
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.
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.
Quest for Intelligence
MIT Quest addresses two fundamental questions: How does human intelligence work, in engineering terms? And how can we use our understanding of human intelligence to build smarter machines for the benefit of society? As part of our mission, we are developing customized AI tools for non-AI researchers, which could accelerate progress in many fields. We see an opportunity to achieve a deeper understanding of intelligence through the kind of basic research that leads to unexpected breakthroughs. We aspire for our new knowledge and newly built tools to serve the public good, in our nation and around the world.
Resilient Infrastructure Systems Lab
The Resilient Infrastructure Systems Lab seeks to improve the robustness and security of critical infrastructure systems by developing tools to detect and respond to incidents, both random and adversarial and by designing incentive mechanisms for efficient infrastructure management. They are working on the problems of cyber-physical security, failure diagnostics and incident response, network monitoring and control, and demand management in real-world infrastructures. They mainly focus on cyber-physical infrastructure systems for electric power, transportation, and urban water and natural gas networks.
Engineering Systems Analysis for Design
Practical-oriented subject that builds upon theory and methods and culminates in extended application. Covers methods to identify, value, and implement flexibility in design (real options). Topics include definition of uncertainties, simulation of performance for scenarios, screening models to identify desirable flexibility, decision analysis, and multidimensional economic evaluation. Students demonstrate proficiency through an extended application to a system design of their choice. Complements research or thesis projects. Meets with IDS.333 first half of term. Enrollment limited.
Transportation Systems Modeling
Introduces basic concepts of transportation systems modeling, data analysis and visualization techniques. Covers fundamental analytical and simulation-based methodologies. Topics include time-space diagrams, cumulative plots, queueing theory, network science, data analysis, and their applications. Provides students with an understanding of the current challenges and opportunities in different areas of transportation.
Transportation Systems Analysis: Performance & Optimization
Problem-motivated introduction to methods, models and tools for the analysis and design of transportation networks including their planning, operations and control. Capacity of critical elements of transportation networks. Traffic flows and deterministic and probabilistic delay models. Formulation of optimization models for planning and scheduling of freight, transit and airline systems, and their solution using software packages. User- and system-optimal traffic assignment. Control of traffic flows on highways, urban grids, and airspace.
Resilient Infrastructure Networks
Control algorithms and game-theoretic tools to enable resilient operation of large-scale infrastructure networks. Dynamical network flow models, stability analysis, robust predictive control, fault and attack diagnostic tools. Strategic network design, routing games, congestion pricing, demand response, and incentive regulation. Design of operations management strategies for different reliability and security scenarios. Applications to transportation, logistics, electric-power, and water distribution networks.
Air Transportation Operations Research
Presents a unified view of advanced quantitative analysis and optimization techniques applied to the air transportation sector. Considers the problem of operating and managing the aviation sector from the perspectives of the system operators (e.g., the FAA), the airlines, and the resultant impacts on the end-users (the passengers). Explores models and optimization approaches to system-level problems, airline schedule planning problems, and airline management challenges.
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.
Public Transportation Analytics and Planning
Students will gain experience processing, visualizing, and analyzing urban mobility data, with special emphasis on models and performance metrics tailored to scheduled, fixed-route transit services. The evolution of urban public transportation modes and services, as well as interaction with emerging on-demand services, will be covered. Instructors and guest lecturers from industry will discuss both methods for data collection and analysis, as well as organizational, policy, and governance constraints on transit planning. In assignments, students will practice using spatial database, data visualization, network analysis, and other software to shape recommendations for transit that effectively meets the future needs of cities.
Advanced Analytics Edge
More advanced version of 15.071 introduces core methods of business analytics, their algorithmic implementations and their applications to various domains of management and public policy. Spans descriptive analytics (e.g., clustering, dimensionality reduction), predictive analytics (e.g., linear/logistic regression, classification and regression trees, random forests, boosting deep learning) and prescriptive analytics (e.g., optimization). Presents analytics algorithms, and their implementations in data science. Includes case studies in e-commerce, transportation, energy, healthcare, social media, sports, the internet, and beyond. Uses the R and Julia programming languages. Includes team projects. Preference to Sloan Master of Business Analytics students.
Integer Programming and Combinatorial Optimization
In-depth treatment of the modern theory of integer programming and combinatorial optimization, emphasizing geometry, duality, and algorithms. Topics include formulating problems in integer variables, enhancement of formulations, ideal formulations, integer programming duality, linear and semidefinite relaxations, lattices and their applications, the geometry of integer programming, primal methods, cutting plane methods, connections with algebraic geometry, computational complexity, approximation algorithms, heuristic and enumerative algorithms, mixed integer programming and solutions of large-scale problems.
Introduces the principal algorithms for linear, network, discrete, robust, nonlinear, and dynamic optimization. Emphasizes methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
Robust Modeling, Optimization, and Computation
Introduces modern robust optimization, including theory, applications, and computation. Presents formulations and their connection to probability, information and risk theory for conic optimization (linear, second-order, and semidefinite cones) and integer optimization. Application domains include analysis and optimization of stochastic networks, optimal mechanism design, network information theory, transportation, pattern classification, structural and engineering design, and financial engineering. Students formulate and solve a problem aligned with their interests in a final project.
The Airline Industry
Overview of the global airline industry, focusing on recent industry performance, current issues and challenges for the future. Fundamentals of airline industry structure, airline economics, operations planning, safety, labor relations, airports and air traffic control, marketing, and competitive strategies, with an emphasis on the interrelationships among major industry stakeholders. Recent research findings of the MIT Global Airline Industry Program are showcased, including the impacts of congestion and delays, evolution of information technologies, changing human resource management practices, and competitive effects of new entrant airlines. Taught by faculty participants of the Global Airline Industry Program.
Reinforcement Learning: Foundations and Methods
This subject counts as a Control concentration subject. Reinforcement learning (RL) as a methodology for approximately solving sequential decision-making under uncertainty, with foundations in optimal control and machine learning. Finite horizon and infinite horizon dynamic programming, focusing on discounted Markov decision processes. Value and policy iteration. Monte Carlo, temporal differences, Q-learning, and stochastic approximation. Approximate dynamic programming, including value-based methods and policy space methods. Special topics at the boundary of theory and practice in RL. Applications and examples drawn from diverse domains. While an analysis prerequisite is not required, mathematical maturity is necessary. Enrollment limited
Introduction to Mathematical Programming
Introduction to linear optimization and its extensions emphasizing both methodology and the underlying mathematical structures and geometrical ideas. Covers classical theory of linear programming as well as some recent advances in the field. Topics: simplex method; duality theory; sensitivity analysis; network flow problems; decomposition; integer programming; interior point algorithms for linear programming; and introduction to combinatorial optimization and NP-completeness.
Unified analytical and computational approach to nonlinear optimization problems. Unconstrained optimization methods include gradient, conjugate direction, Newton, sub-gradient and first-order methods. Constrained optimization methods include feasible directions, projection, interior point methods, and Lagrange multiplier methods. Convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. Comprehensive treatment of optimality conditions and Lagrange multipliers. Geometric approach to duality theory. Applications drawn from control, communications, machine learning, and resource allocation problems.
Discrete Probability and Stochastic Processes
Provides an introduction to tools used for probabilistic reasoning in the context of discrete systems and processes. Tools such as the probabilistic method, first and second moment method, martingales, concentration and correlation inequalities, theory of random graphs, weak convergence, random walks and Brownian motion, branching processes, Markov chains, Markov random fields, correlation decay method, isoperimetry, coupling, influences and other basic tools of modern research in probability will be presented. Algorithmic aspects and connections to statistics and machine learning will be emphasized.
Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.036 or other previous experience in machine learning.