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16.888 Multidisciplinary System Design Optimization (MIT) 16.888 Multidisciplinary System Design Optimization (MIT)

Description

This course is mainly focused on the quantitative aspects of design and presents a unifying framework called "Multidisciplinary System Design Optimization" (MSDO). The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context, focusing on three aspects of the problem: (i) The multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. There is a version of this course (16.60s) offered through the MIT Professional Institute, targeted at professional engineers. This course is mainly focused on the quantitative aspects of design and presents a unifying framework called "Multidisciplinary System Design Optimization" (MSDO). The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context, focusing on three aspects of the problem: (i) The multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. There is a version of this course (16.60s) offered through the MIT Professional Institute, targeted at professional engineers.

Subjects

optimization | optimization | multidisciplinary design optimization | multidisciplinary design optimization | MDO | MDO | subsystem identification | subsystem identification | interface design | interface design | linear constrained optimization fomulation | linear constrained optimization fomulation | non-linear constrained optimization formulation | non-linear constrained optimization formulation | scalar optimization | scalar optimization | vector optimization | vector optimization | systems engineering | systems engineering | complex systems | complex systems | heuristic search methods | heuristic search methods | tabu search | tabu search | simulated annealing | simulated annealing | genertic algorithms | genertic algorithms | sensitivity | sensitivity | tradeoff analysis | tradeoff analysis | goal programming | goal programming | isoperformance | isoperformance | pareto optimality | pareto optimality | flowchart | flowchart | design vector | design vector | simulation model | simulation model | objective vector | objective vector | input | input | discipline | discipline | output | output | coupling | coupling | multiobjective optimization | multiobjective optimization | optimization algorithms | optimization algorithms | tradespace exploration | tradespace exploration | numerical techniques | numerical techniques | direct methods | direct methods | penalty methods | penalty methods | heuristic techniques | heuristic techniques | SA | SA | GA | GA | approximation methods | approximation methods | sensitivity analysis | sensitivity analysis | isoperformace | isoperformace | output evaluation | output evaluation | MSDO framework | MSDO framework

License

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STS.011 American Science: Ethical Conflicts and Political Choices (MIT) STS.011 American Science: Ethical Conflicts and Political Choices (MIT)

Description

Includes audio/video content: AV special element video. We will explore the changing political choices and ethical dilemmas of American scientists from the atomic scientists of World War II to biologists in the present wrestling with the questions raised by cloning and other biotechnologies. As well as asking how we would behave if confronted with the same choices, we will try to understand the choices scientists have made by seeing them in their historical and political contexts. Some of the topics covered include: the original development of nuclear weapons and the bombing of Hiroshima and Nagasaki; the effects of the Cold War on American science; the space shuttle disasters; debates on the use of nuclear power, wind power, and biofuels; abuse of human subjects in psychological and othe Includes audio/video content: AV special element video. We will explore the changing political choices and ethical dilemmas of American scientists from the atomic scientists of World War II to biologists in the present wrestling with the questions raised by cloning and other biotechnologies. As well as asking how we would behave if confronted with the same choices, we will try to understand the choices scientists have made by seeing them in their historical and political contexts. Some of the topics covered include: the original development of nuclear weapons and the bombing of Hiroshima and Nagasaki; the effects of the Cold War on American science; the space shuttle disasters; debates on the use of nuclear power, wind power, and biofuels; abuse of human subjects in psychological and othe

Subjects

risk | risk | science | science | society | society | ethics | ethics | politics | politics | technology | technology | history | history | controversy | controversy | atomic | atomic | whistleblowing | whistleblowing | GMO | GMO | genetic engineering | genetic engineering | nuclear | nuclear | space exploration | space exploration | energy | energy | policy | policy | debate | debate | museum | museum | archeology | archeology | war | war | terrorism | terrorism | tradeoff | tradeoff | decision making | decision making | medicine | medicine | health care policy | health care policy | biotechnology | biotechnology | climate change | climate change | global warming | global warming | human subjects | human subjects

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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ESD.34 System Architecture (MIT) ESD.34 System Architecture (MIT)

Description

This course covers principles and methods for technical System Architecture. It presents a synthetic view including: the resolution of ambiguity to identify system goals and boundaries; the creative process of mapping form to function; and the analysis of complexity and methods of decomposition and re-integration. Industrial speakers and faculty present examples from various industries. Heuristic and formal methods are presented. Restricted to SDM (System Design and Management) students. This course covers principles and methods for technical System Architecture. It presents a synthetic view including: the resolution of ambiguity to identify system goals and boundaries; the creative process of mapping form to function; and the analysis of complexity and methods of decomposition and re-integration. Industrial speakers and faculty present examples from various industries. Heuristic and formal methods are presented. Restricted to SDM (System Design and Management) students.

Subjects

systems | systems | Product Development Process (PDP) | Product Development Process (PDP) | architect | architect | tradeoff | tradeoff | function | function | use case | use case | scenario | scenario | creativity | creativity | complexity | complexity | interface | interface | form | form | feature | feature | requirements | requirements | design | design | optimization | optimization | risk | risk

License

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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ESD.10 Introduction to Technology and Policy (MIT) ESD.10 Introduction to Technology and Policy (MIT)

Description

This course explores perspectives in the policy process - agenda setting, problem definition, framing the terms of debate, formulation and analysis of options, implementation and evaluation of policy outcomes using frameworks including economics and markets, law, and business and management. Methods include cost/benefit analysis, probabilistic risk assessment, and system dynamics. Exercises include developing skills to work on the interface between technology and societal issues; simulation exercises; case studies; and group projects that illustrate issues involving multiple stakeholders with different value structures, high levels of uncertainty, multiple levels of complexity; and value trade-offs that are characteristic of engineering systems. Emphasis on negotiation, team building and g This course explores perspectives in the policy process - agenda setting, problem definition, framing the terms of debate, formulation and analysis of options, implementation and evaluation of policy outcomes using frameworks including economics and markets, law, and business and management. Methods include cost/benefit analysis, probabilistic risk assessment, and system dynamics. Exercises include developing skills to work on the interface between technology and societal issues; simulation exercises; case studies; and group projects that illustrate issues involving multiple stakeholders with different value structures, high levels of uncertainty, multiple levels of complexity; and value trade-offs that are characteristic of engineering systems. Emphasis on negotiation, team building and g

Subjects

Politics | Politics | decision making | decision making | negotiation | negotiation | planning | planning | wedge game | wedge game | climate change | climate change | global warming | global warming | NRC | NRC | nuclear power | nuclear power | nuclear energy | nuclear energy | nuclear proliferation | nuclear proliferation | government | government | public policy | public policy | globalization | globalization | science | science | EPA | EPA | NSF | NSF | transportation | transportation | urban planning | urban planning | standards | standards | risk | risk | risk assessment | risk assessment | engineering | engineering | energy | energy | internet | internet | network neutrality | network neutrality | regulation | regulation | security | security | 9/11 | 9/11 | September 11 | September 11 | terrorism | terrorism | defense | defense | tradeoff | tradeoff

License

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15.763J Manufacturing System and Supply Chain Design (MIT) 15.763J Manufacturing System and Supply Chain Design (MIT)

Description

15.763J focuses on decision making for system design, as it arises in manufacturing systems and supply chains. Students are exposed to frameworks and models for structuring the key issues and trade-offs. The class presents and discusses new opportunities, issues and concepts introduced by the internet and e-commerce. It also introduces various models, methods and software tools for logistics network design, capacity planning and flexibility, make-buy, and integration with product development. Industry applications and cases illustrate concepts and challenges. The class is recommended for anyone concentrating in Operations Management, and is a second half-term subject. 15.763J focuses on decision making for system design, as it arises in manufacturing systems and supply chains. Students are exposed to frameworks and models for structuring the key issues and trade-offs. The class presents and discusses new opportunities, issues and concepts introduced by the internet and e-commerce. It also introduces various models, methods and software tools for logistics network design, capacity planning and flexibility, make-buy, and integration with product development. Industry applications and cases illustrate concepts and challenges. The class is recommended for anyone concentrating in Operations Management, and is a second half-term subject.

Subjects

15.763 | 15.763 | 1.274 | 1.274 | ESD.268 | ESD.268 | supply chain strategies | supply chain strategies | companies | companies | supply chain components | supply chain components | concepts and models | concepts and models | key tradeoffs and phenomena | key tradeoffs and phenomena | risk pooling and inventory placement | risk pooling and inventory placement | integrated planning and collaboration | integrated planning and collaboration | information sharing | information sharing | supply chain analysis and optimization | supply chain analysis and optimization

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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15.762J Supply Chain Planning (SMA 6305) (MIT) 15.762J Supply Chain Planning (SMA 6305) (MIT)

Description

15.762J focuses on effective supply chain strategies for companies that operate globally with emphasis on how to plan and integrate supply chain components into a coordinated system. Students are exposed to concepts and models important in supply chain planning with emphasis on key tradeoffs and phenomena. The course introduces and utilizes key tactics such as risk pooling and inventory placement, integrated planning and collaboration, and information sharing. Lectures, computer exercises, and case discussions introduce various models and methods for supply chain analysis and optimization. The class is recommended for Operations Management concentrators and is a first half-term subject. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 15.762J focuses on effective supply chain strategies for companies that operate globally with emphasis on how to plan and integrate supply chain components into a coordinated system. Students are exposed to concepts and models important in supply chain planning with emphasis on key tradeoffs and phenomena. The course introduces and utilizes key tactics such as risk pooling and inventory placement, integrated planning and collaboration, and information sharing. Lectures, computer exercises, and case discussions introduce various models and methods for supply chain analysis and optimization. The class is recommended for Operations Management concentrators and is a first half-term subject. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA

Subjects

supply chain strategies | supply chain strategies | companies | companies | supply chain components | supply chain components | concepts and models | concepts and models | key tradeoffs and phenomena | key tradeoffs and phenomena | risk pooling and inventory placement | risk pooling and inventory placement | integrated planning and collaboration | integrated planning and collaboration | and information sharing | and information sharing | supply chain analysis and optimization | supply chain analysis and optimization | information sharing | information sharing | 15.762 | 15.762 | 1.273 | 1.273 | ESD.267 | ESD.267 | SMA 6305 | SMA 6305

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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2.994 MADM with Applications in Material Selection and Optimal Design (MIT) 2.994 MADM with Applications in Material Selection and Optimal Design (MIT)

Description

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 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.

Subjects

materials selection | materials selection | tradeoff | tradeoff | optimization | optimization | Taguchi | Taguchi | multiple attribute | multiple attribute | decision making | decision making | multiple attribute decision making | multiple attribute decision making | performance index | performance index | rank stability analysis | rank stability analysis | decision matrix | decision matrix | multi-criteria decision making | multi-criteria decision making | multiobjective optimization | multiobjective optimization | Pareto | Pareto | TOPSIS | TOPSIS | ELECTRE | ELECTRE

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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2.882 System Design and Analysis based on AD and Complexity Theories (MIT) 2.882 System Design and Analysis based on AD and Complexity Theories (MIT)

Description

This course studies what makes a good design and how one develops a good design. Students consider how the design of engineered systems (such as hardware, software, materials, and manufacturing systems) differ from the "design" of natural systems such as biological systems; discuss complexity and how one makes use of complexity theory to improve design; and discover how one uses axiomatic design theory (AD theory) in design of many different kinds of engineered systems. Questions are analyzed using Axiomatic Design Theory and Complexity Theory. Case studies are presented including the design of machines, tribological systems, materials, manufacturing systems, and recent inventions. Implications of AD and complexity theories on biological systems discussed. This course studies what makes a good design and how one develops a good design. Students consider how the design of engineered systems (such as hardware, software, materials, and manufacturing systems) differ from the "design" of natural systems such as biological systems; discuss complexity and how one makes use of complexity theory to improve design; and discover how one uses axiomatic design theory (AD theory) in design of many different kinds of engineered systems. Questions are analyzed using Axiomatic Design Theory and Complexity Theory. Case studies are presented including the design of machines, tribological systems, materials, manufacturing systems, and recent inventions. Implications of AD and complexity theories on biological systems discussed.

Subjects

information content | information content | electrical connector | electrical connector | constraint | constraint | complexity | complexity | manufacturing | manufacturing | design | design | functional requirement | functional requirement | requirement | requirement | tradeoff | tradeoff | optimization | optimization | engineered systems | engineered systems | natural systems | natural systems | complexity theory | complexity theory | axiomatic design | axiomatic design | tribology | tribology | tribological systems | tribological systems | manufacturing systems | manufacturing systems | biological systems | biological systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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15.763J Manufacturing System and Supply Chain Design (MIT)

Description

15.763J focuses on decision making for system design, as it arises in manufacturing systems and supply chains. Students are exposed to frameworks and models for structuring the key issues and trade-offs. The class presents and discusses new opportunities, issues and concepts introduced by the internet and e-commerce. It also introduces various models, methods and software tools for logistics network design, capacity planning and flexibility, make-buy, and integration with product development. Industry applications and cases illustrate concepts and challenges. The class is recommended for anyone concentrating in Operations Management, and is a second half-term subject.

Subjects

15.763 | 1.274 | ESD.268 | supply chain strategies | companies | supply chain components | concepts and models | key tradeoffs and phenomena | risk pooling and inventory placement | integrated planning and collaboration | information sharing | supply chain analysis and optimization

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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2.994 MADM with Applications in Material Selection and Optimal Design (MIT)

Description

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.

Subjects

materials selection | tradeoff | optimization | Taguchi | multiple attribute | decision making | multiple attribute decision making | performance index | rank stability analysis | decision matrix | multi-criteria decision making | multiobjective optimization | Pareto | TOPSIS | ELECTRE

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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2.882 System Design and Analysis based on AD and Complexity Theories (MIT)

Description

This course studies what makes a good design and how one develops a good design. Students consider how the design of engineered systems (such as hardware, software, materials, and manufacturing systems) differ from the "design" of natural systems such as biological systems; discuss complexity and how one makes use of complexity theory to improve design; and discover how one uses axiomatic design theory (AD theory) in design of many different kinds of engineered systems. Questions are analyzed using Axiomatic Design Theory and Complexity Theory. Case studies are presented including the design of machines, tribological systems, materials, manufacturing systems, and recent inventions. Implications of AD and complexity theories on biological systems discussed.

Subjects

information content | electrical connector | constraint | complexity | manufacturing | design | functional requirement | requirement | tradeoff | optimization | engineered systems | natural systems | complexity theory | axiomatic design | tribology | tribological systems | manufacturing systems | biological systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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STS.011 American Science: Ethical Conflicts and Political Choices (MIT)

Description

We will explore the changing political choices and ethical dilemmas of American scientists from the atomic scientists of World War II to biologists in the present wrestling with the questions raised by cloning and other biotechnologies. As well as asking how we would behave if confronted with the same choices, we will try to understand the choices scientists have made by seeing them in their historical and political contexts. Some of the topics covered include: the original development of nuclear weapons and the bombing of Hiroshima and Nagasaki; the effects of the Cold War on American science; the space shuttle disasters; debates on the use of nuclear power, wind power, and biofuels; abuse of human subjects in psychological and other experiments; deliberations on genetically modified food

Subjects

risk | science | society | ethics | politics | technology | history | controversy | atomic | whistleblowing | GMO | genetic engineering | nuclear | space exploration | energy | policy | debate | museum | archeology | war | terrorism | tradeoff | decision making | medicine | health care policy | biotechnology | climate change | global warming | human subjects

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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15.762J Supply Chain Planning (SMA 6305) (MIT)

Description

15.762J focuses on effective supply chain strategies for companies that operate globally with emphasis on how to plan and integrate supply chain components into a coordinated system. Students are exposed to concepts and models important in supply chain planning with emphasis on key tradeoffs and phenomena. The course introduces and utilizes key tactics such as risk pooling and inventory placement, integrated planning and collaboration, and information sharing. Lectures, computer exercises, and case discussions introduce various models and methods for supply chain analysis and optimization. The class is recommended for Operations Management concentrators and is a first half-term subject. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA

Subjects

supply chain strategies | companies | supply chain components | concepts and models | key tradeoffs and phenomena | risk pooling and inventory placement | integrated planning and collaboration | and information sharing | supply chain analysis and optimization | information sharing | 15.762 | 1.273 | ESD.267 | SMA 6305

License

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16.888 Multidisciplinary System Design Optimization (MIT)

Description

This course is mainly focused on the quantitative aspects of design and presents a unifying framework called "Multidisciplinary System Design Optimization" (MSDO). The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context, focusing on three aspects of the problem: (i) The multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. There is a version of this course (16.60s) offered through the MIT Professional Institute, targeted at professional engineers.

Subjects

optimization | multidisciplinary design optimization | MDO | subsystem identification | interface design | linear constrained optimization fomulation | non-linear constrained optimization formulation | scalar optimization | vector optimization | systems engineering | complex systems | heuristic search methods | tabu search | simulated annealing | genertic algorithms | sensitivity | tradeoff analysis | goal programming | isoperformance | pareto optimality | flowchart | design vector | simulation model | objective vector | input | discipline | output | coupling | multiobjective optimization | optimization algorithms | tradespace exploration | numerical techniques | direct methods | penalty methods | heuristic techniques | SA | GA | approximation methods | sensitivity analysis | isoperformace | output evaluation | MSDO framework

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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15.763J Manufacturing System and Supply Chain Design (MIT)

Description

15.763J focuses on decision making for system design, as it arises in manufacturing systems and supply chains. Students are exposed to frameworks and models for structuring the key issues and trade-offs. The class presents and discusses new opportunities, issues and concepts introduced by the internet and e-commerce. It also introduces various models, methods and software tools for logistics network design, capacity planning and flexibility, make-buy, and integration with product development. Industry applications and cases illustrate concepts and challenges. The class is recommended for anyone concentrating in Operations Management, and is a second half-term subject.

Subjects

15.763 | 1.274 | ESD.268 | supply chain strategies | companies | supply chain components | concepts and models | key tradeoffs and phenomena | risk pooling and inventory placement | integrated planning and collaboration | information sharing | supply chain analysis and optimization

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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ESD.34 System Architecture (MIT)

Description

This course covers principles and methods for technical System Architecture. It presents a synthetic view including: the resolution of ambiguity to identify system goals and boundaries; the creative process of mapping form to function; and the analysis of complexity and methods of decomposition and re-integration. Industrial speakers and faculty present examples from various industries. Heuristic and formal methods are presented. Restricted to SDM (System Design and Management) students.

Subjects

systems | Product Development Process (PDP) | architect | tradeoff | function | use case | scenario | creativity | complexity | interface | form | feature | requirements | design | optimization | risk

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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ESD.10 Introduction to Technology and Policy (MIT)

Description

This course explores perspectives in the policy process - agenda setting, problem definition, framing the terms of debate, formulation and analysis of options, implementation and evaluation of policy outcomes using frameworks including economics and markets, law, and business and management. Methods include cost/benefit analysis, probabilistic risk assessment, and system dynamics. Exercises include developing skills to work on the interface between technology and societal issues; simulation exercises; case studies; and group projects that illustrate issues involving multiple stakeholders with different value structures, high levels of uncertainty, multiple levels of complexity; and value trade-offs that are characteristic of engineering systems. Emphasis on negotiation, team building and g

Subjects

Politics | decision making | negotiation | planning | wedge game | climate change | global warming | NRC | nuclear power | nuclear energy | nuclear proliferation | government | public policy | globalization | science | EPA | NSF | transportation | urban planning | standards | risk | risk assessment | engineering | energy | internet | network neutrality | regulation | security | 9/11 | September 11 | terrorism | defense | tradeoff

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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