Explores a variety of models and optimization techniques for the solution of airline schedule planning and operations problems. Schedule design, fleet assignment, aircraft maintenance routing, crew scheduling, passenger mix, and other topics are covered. Recent models and algorithms addressing issues of model integration, robustness, and operations recovery are introduced. Modeling and solution techniques designed specifically for large-scale problems, and state-of-the-art applications of these techniques to airline problems are detailed.

Animation is a compelling and effective form of expression; it engages viewers and makes difficult concepts easier to grasp. Today’s animation industry creates films, special effects, and games with stunning visual detail and quality. This graduate class will investigate the algorithms that make these animations possible: keyframing, inverse kinematics, physical simulation, optimization, optimal control, motion capture, and data-driven methods. Our study will also reveal the shortcomings of these sophisticated tools. The students will propose improvements and explore new methods for computer animation in semester-long research projects. The course should appeal to both students with general interest in computer graphics and students interested in new applications of machine learning, robotics, biomechanics, physics, applied mathematics and scientific computing.

Students learn about linear programming (also called linear optimization) to solve engineering design problems. As they work through a word problem as a class, they learn about the ideas of constraints, feasibility and optimization related to graphing linear equalities. Then they apply this information to solve two practice engineering design problems related to optimizing materials and cost by graphing inequalities, determining coordinates and equations from their graphs, and solving their equations. It is suggested that students conduct the associated activity, Optimizing Pencils in a Tray, before this lesson, although either order is acceptable.

As currently taught in the United States, introductory courses in analytical chemistryemphasize quantitative (and sometimes qualitative) methods of analysis along with a heavydose of equilibrium chemistry. Analytical chemistry, however, is much more than a collection ofanalytical methods and an understanding of equilibrium chemistry; it is an approach to solvingchemical problems. Although equilibrium chemistry and analytical methods are important, theircoverage should not come at the expense of other equally important topics.

The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum forexploring topics such as experimental design, sampling, calibration strategies, standardization,optimization, statistics, and the validation of experimental results. Analytical methods comeand go, but best practices for designing and validating analytical methods are universal. Becausechemistry is an experimental science it is essential that all chemistry students understand theimportance of making good measurements.

My goal in preparing this textbook is to find a more appropriate balance between theoryand practice, between “classical” and “modern” analytical methods, between analyzing samplesand collecting samples and preparing them for analysis, and between analytical methods anddata analysis. There is more material here than anyone can cover in one semester; it is myhope that the diversity of topics will meet the needs of different instructors, while, perhaps,suggesting some new topics to cover.

This course presents real-world examples in which quantitative methods provide a significant competitive edge that has led to a first order impact on some of today’s most important companies. We outline the competitive landscape and present the key quantitative methods that created the edge (data-mining, dynamic optimization, simulation), and discuss their impact.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is transforming our way of life. Able to spot patterns invisible to the human eye, algorithms are learning how to make our lives easier, safer, and more fun. That power is not lost on materials researchers. During the next decade, artificial intelligence or AI-driven research could fundamentally transform how new and better materials are developed. What’s more, it might even revamp how materials research itself is carried out, enabling promising new materials and processes to be developed more quickly. Machine learning methods come in a variety of flavors, with some requiring more guidance, or “supervision,” from researchers. But, generally, a machine-learning algorithm designed to discover and understand the behavior of materials looks for patterns connecting the composition, structure, and properties of known materials..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

In this unit students will analyze and manipulate functions in order to apply them to real-life optimization (minimum/maximum) situations.

Carrier systems involve the design, operation and management of transportation networks, assets, personnel, freight and passengers. In this course, we will present models and tools for analyzing, optimizing, planning, managing and controlling carrier systems.

This course explores the reciprocal relationships among design, science, and technology by covering a wide range of topics including industrial design, architecture, visualization and perception, design computation, material ecology, and environmental design and sustainability. Students will examine how transformations in science and technology have influenced design thinking and vice versa, as well as develop methodologies for design research and collaborate on design solutions to interdisciplinary problems.

This course covers the design, construction, and testing of field robotic systems, through team projects with each student responsible for a specific subsystem. Projects focus on electronics, instrumentation, and machine elements. Design for operation in uncertain conditions is a focus point, with ocean waves and marine structures as a central theme. Topics include basic statistics, linear systems, Fourier transforms, random processes, spectra, ethics in engineering practice, and extreme events with applications in design.

This course provides students with an opportunity to conceive, design and implement a product, using rapid prototyping methods and computer-aid tools. The first of two phases challenges each student team to meet a set of design requirements and constraints for a structural component. A course of iteration, fabrication, and validation completes this manual design cycle. During the second phase, each team conducts design optimization using structural analysis software, with their phase one prototype as a baseline.
Acknowledgements
This course is made possible thanks to a grant by the alumni sponsored Teaching and Education Enhancement Program (Class of ‘51 Fund for Excellence in Education, Class of ‘55 Fund for Excellence in Teaching, Class of ‘72 Fund for Educational Innovation). The instructors gratefully acknowledge the financial support.
The course was approved by the Undergraduate Committee of the MIT Department of Aeronautics and Astronautics in 2003. The instructors thank Prof. Manuel Martinez-Sanchez and the committee members for their support and suggestions.

This course provides students with an opportunity to conceive, design and implement a product, using rapid prototyping methods and computer-aid tools. The first of two phases challenges each student team to meet a set of design requirements and constraints for a structural component. A course of iteration, fabrication, and validation completes this manual design cycle. During the second phase, each team conducts design optimization using structural analysis software, with their phase one prototype as a baseline.
Acknowledgements
This course is made possible thanks to a grant by the alumni sponsored Teaching and Education Enhancement Program (Class of ‘51 Fund for Excellence in Education, Class of ‘55 Fund for Excellence in Teaching, Class of ‘72 Fund for Educational Innovation). The instructors gratefully acknowledge the financial support. The course was approved by the Undergraduate Committee of the MIT Department of Aeronautics and Astronautics in 2003. The instructors thank Prof. Manuel Martinez-Sanchez and the committee members for their support and suggestions.

In this activity, students will model a noisy set of bacterial cell count data using both exponential and logistic growth models. For each model the students will plot the data (or a linear transformation of the data) and apply the method of least squares to fit the model's parameters. Activities include both theoretical and conceptual work, exploring the properties of the differential equation models, as well as hands-on computational work, using spreadsheets to quickly process large amounts of data. This activity is meant as a capstone to the differential calculus portion of a typical undergraduate Calculus I course. It explores a biological application of a variety of differential calculus concepts, including: differential equations, numerical differentiation, optimization, and limits. Additional topics explored include semi-log plots and linear regression.

Sustainability denotes one of the main future challenges of societies and the global community. Issues of sustainability range from energy and natural resources to biodiversity loss and global climate change. Properly dealing with these issues will be crucial to future societal and economic development. This course provides the theoretical background for the discussion and analysis of sustainability issues. Students will recognize specific sustainability issues, such as sustainable energy, as part of a more complex challenge of developing sustainable societies and systems, and against the background of the general meaning and implications of the conception of sustainability.

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

This course begins with a comparative review of conventional and advanced multiple attribute decision making (MADM) models in engineering practice. Next, a new application of particular MADM models in reliable material selection of sensitive structural components as well as a multi-criteria Taguchi optimization method is discussed. Other specific topics include dealing with uncertainties in material properties, incommensurability in decision-makers opinions for the same design, objective ways of weighting performance indices, rank stability analysis, compensations and non-compensations.
This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

This course covers the following topics: models of manufacturing systems, including transfer lines and flexible manufacturing systems; calculation of performance measures, including throughput, in-process inventory, and meeting production commitments; real-time control of scheduling; effects of machine failure, set-ups, and other disruptions on system performance.

Students play the role of engineers as they test, design and build Mentos(TM) fountains a dramatic example of how potential energy (stored energy) can be converted to kinetic energy (motion). They are challenged to work together as a class to optimize the design of the basic soda/candy geyser made by the teacher. To do this, three research teams each investigate how a different variable nozzle shape, soda temperature, number of candies affects fountain height. They devise and run experimental tests to determine the best variable values. Then they combine their results to design the highest fountain to compete head-to-head with the teacher's geyser design.

Modelling is about understanding the nature: our world, ourselves and our work. Everything that we observe has a cause (typically several) and has the effect thereof. The heart of modelling lies in identifying, understanding and quantifying these cause-and-effect relationships.

A model can be treated as a (selective) representation of a system. We create the model by defining a mapping from the system space to the model space, thus we can map system state and behaviour to model state and behaviour. By defining the inverse mapping, we may map results from the study of the model back to the system. In this course, using an overarching modelling paradigm, students will become familiar with several instances of modelling, e.g., mechanics, thermal dynamics, fluid mechanics, etc.

This course uses computer-aided design methodologies for synthesis of multivariable feedback control systems. Topics covered include: performance and robustness trade-offs; model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; and nonlinear effects. The assignments for the course comprise of computer-aided (MATLAB®) design problems.