Courses tagged with "Nutrition" (461)
Sustainability is a practice operating across a variety of scales and skills. We will explore the ways that decision makers use systems analysis and design thinking to confront the career-defining challenges facing the next generation of leaders. Networks of practice from across North America and around the globe will provide case material and guest lectures.
This course will cover the agricultural and urban water quality issues in Florida, their bases, land and nutrient management strategies, and the science and policy behind the best management practices (BMPs). Students will learn to evaluate BMP research and analyze its role in determining practices and policies that protect water quality.
By Dariush Rafinejad
Environmental sustainability has emerged as the imperative management undertaking for business sustainability in the face of rising global demand for natural resources and environment services and of environmental problems such as climate change. This course will examine how regulatory and...
15.874 and 15.871 provide an introduction to system dynamics modeling for the analysis of business policy and strategy. Students learn to visualize a business organization in terms of the structures and policies that create dynamics and regulate performance. The course uses role playing games, simulation models, and management flight simulators to develop principles for the successful management of complex strategies. Special emphasis will be placed on case studies of successful strategies using system dynamics.
15.874 is a full semester course and 15.871 is a half semester course. The two classes meet together and cover the same material for the first half of the term. In the second half of the semester, only 15.874 continues.
Continuation of 15.871, emphasizing tools and methods needed to apply systems thinking and simulation modeling successfully in complex real-world settings. Uses simulation models, management flight simulators, and case studies to deepen the conceptual and modeling skills introduced in 15.871. Through models and case studies of successful applications students learn how to use qualitative and quantitative data to formulate and test models, and how to work effectively with senior executives to implement change successfully.
Many books and thousands of papers cover the field of system dynamics. With all of these resources available, it can be difficult to know where to begin. The System Dynamics in Education Project at MIT put together these resources to help people sort through the vast library of books and papers on system dynamics. This course site includes a collection of papers and computer exercises entitled “Road Maps,” as well as a collection of assignments and solutions that were initially part of a guided study to system dynamics. Note that while the level of the course indicated in the upper right corner of the screen is "Undergraduate / Graduate," the material is suitable for people ranging from K-12 students to chief executives of corporations.
One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management.
Managers and engineers are constantly attempting to optimize, particularly in the design and operation of complex systems. This course is an application-oriented introduction to (systems) optimization. It seeks to:
- Motivate the use of optimization models to support managers and engineers in a wide variety of decision making situations;
- Show how several application domains (industries) use optimization;
- Introduce optimization modeling and solution techniques (including linear, non-linear, integer, and network optimization, and heuristic methods);
- Provide tools for interpreting and analyzing model-based solutions (sensitivity and post-optimality analysis, bounding techniques); and
- Develop the skills required to identify the opportunity and manage the implementation of an optimization-based decision support tool.
