Searching for heuristics : 25 results found | RSS Feed for this search

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2.75 Precision Machine Design (MIT) 2.75 Precision Machine Design (MIT)

Description

Intensive coverage of precision engineering theory, heuristics, and applications pertaining to the design of systems ranging from consumer products to machine tools. Topics covered include: economics, project management, and design philosophy; principles of accuracy, repeatability, and resolution; error budgeting; sensors; sensor mounting; systems design; bearings; actuators and transmissions; system integration driven by functional requirements, and operating physics. Emphasis on developing creative designs, which are optimized by analytical techniques applied via spreadsheets. This is a projects course with lectures consisting of design teams presenting their work and the class helping to develop solutions; thereby everyone learning from everyone's projects. Intensive coverage of precision engineering theory, heuristics, and applications pertaining to the design of systems ranging from consumer products to machine tools. Topics covered include: economics, project management, and design philosophy; principles of accuracy, repeatability, and resolution; error budgeting; sensors; sensor mounting; systems design; bearings; actuators and transmissions; system integration driven by functional requirements, and operating physics. Emphasis on developing creative designs, which are optimized by analytical techniques applied via spreadsheets. This is a projects course with lectures consisting of design teams presenting their work and the class helping to develop solutions; thereby everyone learning from everyone's projects.

Subjects

precision engineering theory | precision engineering theory | heuristics | heuristics | systems design | systems design | economics | economics | project management | project management | design philosophy | design philosophy | error budgeting | error budgeting | bearings | bearings | actuators | actuators | transmissions | transmissions | system integration | system integration | functional requirements | functional requirements | operating physics | operating physics

License

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6.370 The Battlecode Programming Competition (MIT) 6.370 The Battlecode Programming Competition (MIT)

Description

Includes audio/video content: AV lectures. This course is conducted as an artificial intelligence programming contest in Java. Students work in teams to program virtual robots to play Battlecode, a real-time strategy game. Optional lectures are provided on topics and programming practices relevant to the game, and students learn and improve their programming skills experientially. The competition culminates in a live Battlecode tournament. 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. Includes audio/video content: AV lectures. This course is conducted as an artificial intelligence programming contest in Java. Students work in teams to program virtual robots to play Battlecode, a real-time strategy game. Optional lectures are provided on topics and programming practices relevant to the game, and students learn and improve their programming skills experientially. The competition culminates in a live Battlecode tournament. 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

Battlecode | Battlecode | programming | programming | artificial intelligence | artificial intelligence | distributed algorithm | distributed algorithm | network communication | network communication | robot | robot | team | team | code | code | build | build | strategy | strategy | player | player | game | game | pathing | pathing | search | search | navigation | navigation | computation | computation | data | data | structure | structure | debugging | debugging | bytecode | bytecode | method | method | cost | cost | Git | Git | repository | repository | swarm | swarm | spawn time | spawn time | heuristics | heuristics

License

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15.053 Introduction to Optimization (MIT) 15.053 Introduction to Optimization (MIT)

Description

15.053 is an undergraduate subject in the theory and practice of optimization. We will consider optimization models with applications to transportation, logistics, manufacturing, computer science, E-business, project management, finance as well as several other domains. This subject will survey some of the applications of optimization as well as heuristics, and we will present algorithms and theory for linear programming, dynamic programming, integer programming, and non-linear programming.One way of summarizing a subject is a lecture by lecture description of the subject, or a description of the methodologies presented in the subject. We do list a lecture by lecture description, but first we describe several cross cutting themes. 15.053 is an undergraduate subject in the theory and practice of optimization. We will consider optimization models with applications to transportation, logistics, manufacturing, computer science, E-business, project management, finance as well as several other domains. This subject will survey some of the applications of optimization as well as heuristics, and we will present algorithms and theory for linear programming, dynamic programming, integer programming, and non-linear programming.One way of summarizing a subject is a lecture by lecture description of the subject, or a description of the methodologies presented in the subject. We do list a lecture by lecture description, but first we describe several cross cutting themes.

Subjects

finance | finance | project management | project management | E-commerce | E-commerce | heuristics | heuristics | non-linear programming | non-linear programming | integer programming | integer programming | dynamic programming | dynamic programming | network optimization | network optimization | linear programming | linear programming

License

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14.127 Behavioral Economics and Finance (MIT) 14.127 Behavioral Economics and Finance (MIT)

Description

This course surveys research which incorporates psychological evidence into economics. Topics include: prospect theory, biases in probabilistic judgment, self-control and mental accounting with implications for consumption and savings, fairness, altruism, and public goods contributions, financial market anomalies and theories, impact of markets, learning, and incentives, and memory, attention, categorization, and the thinking process. This course surveys research which incorporates psychological evidence into economics. Topics include: prospect theory, biases in probabilistic judgment, self-control and mental accounting with implications for consumption and savings, fairness, altruism, and public goods contributions, financial market anomalies and theories, impact of markets, learning, and incentives, and memory, attention, categorization, and the thinking process.

Subjects

behavioral economics | behavioral economics | finance | finance | psychology | psychology | prospect theory | prospect theory | bias | bias | probabilistic judgment | probabilistic judgment | self-control | self-control | mental accounting | mental accounting | fairness | fairness | altruism | altruism | public goods | public goods | market anomalies | market anomalies | market theories | market theories | heuristics | heuristics | noise | noise | confusion | confusion | competition | competition | bounded rationality | bounded rationality | learning | learning | games | games | neuroeconomics | neuroeconomics | hyperbolic discounting | hyperbolic discounting | consumption | consumption | hyperbolics | hyperbolics | temptation | temptation | assets | assets | puzzles | puzzles | bubbles | bubbles | Gul-Pesendorfer | Gul-Pesendorfer

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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6.803 The Human Intelligence Enterprise (MIT) 6.803 The Human Intelligence Enterprise (MIT)

Description

6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course. 6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | Human Intelligence Enterprise | artificial intelligence | artificial intelligence | computational models | computational models | perception | perception | cognition | cognition | neuroscience | neuroscience | human behavior | human behavior | communication | communication | heuristics | heuristics | object tracking | object tracking | object recognition | object recognition | change representation | change representation | language evolution | language evolution | Turing | Turing | Minsky | Minsky

License

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1.203J Logistical and Transportation Planning Methods (MIT) 1.203J Logistical and Transportation Planning Methods (MIT)

Description

The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics. The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics.

Subjects

1.203 | 1.203 | 6.281 | 6.281 | 15.073 | 15.073 | 16.76 | 16.76 | ESD.216 | ESD.216 | logistics | logistics | transportation | transportation | hypercube models | hypercube models | barrier example | barrier example | operations research | operations research | spatial queues | spatial queues | queueing models | queueing models | network models | network models | TSP | TSP | heuristics | heuristics | geometrical probabilities | geometrical probabilities | Markov | Markov | quantitative techniques | quantitative techniques | transportation systems analysis | transportation systems analysis | urban service systems | urban service systems | emergency services | emergency services | random variables | random variables | multi-server queueing theory | multi-server queueing theory | spatial location theory | spatial location theory | network analysis | network analysis | graph theory | graph theory | simulation | simulation | urban OR | urban OR

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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1.203J Logistical and Transportation Planning Methods (MIT) 1.203J Logistical and Transportation Planning Methods (MIT)

Description

The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics. The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics.

Subjects

logistics | logistics | transportation | transportation | hypercube models | hypercube models | barrier example | barrier example | operations research | operations research | spatial queues | spatial queues | queueing models | queueing models | network models | network models | TSP | TSP | heuristics | heuristics | geometrical probablities | geometrical probablities | Markov | Markov | 1.203 | 1.203 | 6.281 | 6.281 | 15.073 | 15.073 | 16.76 | 16.76 | ESD.216 | ESD.216

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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15.066J System Optimization and Analysis for Manufacturing (MIT) 15.066J System Optimization and Analysis for Manufacturing (MIT)

Description

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. 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.

Subjects

modeling | modeling | optimization | optimization | simulation | simulation | manufacturing systems | manufacturing systems | decision making | decision making | decision support | decision support | probabilistic simulation | probabilistic simulation | designing manufacturing systems | designing manufacturing systems | operations management | operations management | linear programming | linear programming | sensitivity analysis | sensitivity analysis | network flow problems | network flow problems | non-linear programming | non-linear programming | Lagrange multipliers | Lagrange multipliers | integer programming | integer programming | discrete-event simulation | discrete-event simulation | heuristics | heuristics | algorithms | algorithms | 15.066 | 15.066 | 2.851 | 2.851 | 3.83 | 3.83

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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6.803 The Human Intelligence Enterprise (MIT) 6.803 The Human Intelligence Enterprise (MIT)

Description

This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course. This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | Human Intelligence Enterprise | artificial intelligence | artificial intelligence | computational models | computational models | perception | perception | cognition | cognition | neuroscience | neuroscience | human behavior | human behavior | communication | communication | heuristics | heuristics | object tracking | object tracking | object recognition | object recognition | change representation | change representation | language evolution | language evolution | Turing | Turing | Minsky | Minsky

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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16.892J Space System Architecture and Design (MIT) 16.892J Space System Architecture and Design (MIT)

Description

Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues. Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues.

Subjects

space system | space system | space system architecture | space system architecture | space architecting | space architecting | uncertainties | uncertainties | space policy | space policy | robustness | robustness | flexibility | flexibility | optimality | optimality | tradespace analysis | tradespace analysis | quality function deployment | quality function deployment | multi-attribute utility theory | multi-attribute utility theory | n-squared | n-squared | design structure matrix | design structure matrix | multi-attribution tradespace exploration | multi-attribution tradespace exploration | MATE | MATE | MATE-CON | MATE-CON | satellite | satellite | classes of space system | classes of space system | XTOS | XTOS | spacetug | spacetug | GINA | GINA | pareto fronts | pareto fronts | engineering design process | engineering design process | optimization methods | optimization methods | genetic algorithms | genetic algorithms | simulated annealing | simulated annealing | MMDOSA | MMDOSA | distributed space systems design optimization | distributed space systems design optimization | clarity test | clarity test | taxonomy of uncertainty | taxonomy of uncertainty | treatment of uncertainty | treatment of uncertainty | irreducible uncertainty | irreducible uncertainty | portfolio theory | portfolio theory | portfolio applications | portfolio applications | taxonomy of flexibility | taxonomy of flexibility | on-orbit servicing | on-orbit servicing | US national space policy | US national space policy | space policy heuristics | space policy heuristics | policy architectures | policy architectures | 16.892 | 16.892 | ESD.353 | ESD.353

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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Artificial Intelligence

Description

This course includes materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics, which will introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. The material is introductory; the readings cite many resources outside those assigned in this course, and students are encouraged to explore these resources to pursue topics of interest. This free course may be completed online at any time. See course site for detailed overview and learning outcomes. (Computer Science 405)

Subjects

ai | logic | machine learning | heuristics | computing | natural language | semantics | robotics | Computer science | I100

License

Attribution 2.0 UK: England & Wales Attribution 2.0 UK: England & Wales http://creativecommons.org/licenses/by/2.0/uk/ http://creativecommons.org/licenses/by/2.0/uk/

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14.127 Behavioral Economics and Finance (MIT)

Description

This course surveys research which incorporates psychological evidence into economics. Topics include: prospect theory, biases in probabilistic judgment, self-control and mental accounting with implications for consumption and savings, fairness, altruism, and public goods contributions, financial market anomalies and theories, impact of markets, learning, and incentives, and memory, attention, categorization, and the thinking process.

Subjects

behavioral economics | finance | psychology | prospect theory | bias | probabilistic judgment | self-control | mental accounting | fairness | altruism | public goods | market anomalies | market theories | heuristics | noise | confusion | competition | bounded rationality | learning | games | neuroeconomics | hyperbolic discounting | consumption | hyperbolics | temptation | assets | puzzles | bubbles | Gul-Pesendorfer

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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1.203J Logistical and Transportation Planning Methods (MIT)

Description

The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics.

Subjects

1.203 | 6.281 | 15.073 | 16.76 | ESD.216 | logistics | transportation | hypercube models | barrier example | operations research | spatial queues | queueing models | network models | TSP | heuristics | geometrical probabilities | Markov | quantitative techniques | transportation systems analysis | urban service systems | emergency services | random variables | multi-server queueing theory | spatial location theory | network analysis | graph theory | simulation | urban OR

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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14.127 Behavioral Economics and Finance (MIT)

Description

This course surveys research which incorporates psychological evidence into economics. Topics include: prospect theory, biases in probabilistic judgment, self-control and mental accounting with implications for consumption and savings, fairness, altruism, and public goods contributions, financial market anomalies and theories, impact of markets, learning, and incentives, and memory, attention, categorization, and the thinking process.

Subjects

behavioral economics | finance | psychology | prospect theory | bias | probabilistic judgment | self-control | mental accounting | fairness | altruism | public goods | market anomalies | market theories | heuristics | noise | confusion | competition | bounded rationality | learning | games | neuroeconomics | hyperbolic discounting | consumption | hyperbolics | temptation | assets | puzzles | bubbles | Gul-Pesendorfer

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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2.75 Precision Machine Design (MIT)

Description

Intensive coverage of precision engineering theory, heuristics, and applications pertaining to the design of systems ranging from consumer products to machine tools. Topics covered include: economics, project management, and design philosophy; principles of accuracy, repeatability, and resolution; error budgeting; sensors; sensor mounting; systems design; bearings; actuators and transmissions; system integration driven by functional requirements, and operating physics. Emphasis on developing creative designs, which are optimized by analytical techniques applied via spreadsheets. This is a projects course with lectures consisting of design teams presenting their work and the class helping to develop solutions; thereby everyone learning from everyone's projects.

Subjects

precision engineering theory | heuristics | systems design | economics | project management | design philosophy | error budgeting | bearings | actuators | transmissions | system integration | functional requirements | operating physics

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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6.370 The Battlecode Programming Competition (MIT)

Description

This course is conducted as an artificial intelligence programming contest in Java. Students work in teams to program virtual robots to play Battlecode, a real-time strategy game. Optional lectures are provided on topics and programming practices relevant to the game, and students learn and improve their programming skills experientially. The competition culminates in a live Battlecode tournament. 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

Battlecode | programming | artificial intelligence | distributed algorithm | network communication | robot | team | code | build | strategy | player | game | pathing | search | navigation | computation | data | structure | debugging | bytecode | method | cost | Git | repository | swarm | spawn time | heuristics

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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1.203J Logistical and Transportation Planning Methods (MIT)

Description

The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics.

Subjects

logistics | transportation | hypercube models | barrier example | operations research | spatial queues | queueing models | network models | TSP | heuristics | geometrical probablities | Markov | 1.203 | 6.281 | 15.073 | 16.76 | ESD.216

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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1.203J Logistical and Transportation Planning Methods (MIT)

Description

The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics.

Subjects

logistics | transportation | hypercube models | barrier example | operations research | spatial queues | queueing models | network models | TSP | heuristics | geometrical probablities | Markov | 1.203 | 6.281 | 15.073 | 16.76 | ESD.216

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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15.053 Introduction to Optimization (MIT)

Description

15.053 is an undergraduate subject in the theory and practice of optimization. We will consider optimization models with applications to transportation, logistics, manufacturing, computer science, E-business, project management, finance as well as several other domains. This subject will survey some of the applications of optimization as well as heuristics, and we will present algorithms and theory for linear programming, dynamic programming, integer programming, and non-linear programming.One way of summarizing a subject is a lecture by lecture description of the subject, or a description of the methodologies presented in the subject. We do list a lecture by lecture description, but first we describe several cross cutting themes.

Subjects

finance | project management | E-commerce | heuristics | non-linear programming | integer programming | dynamic programming | network optimization | linear programming

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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15.066J System Optimization and Analysis for Manufacturing (MIT)

Description

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.

Subjects

modeling | optimization | simulation | manufacturing systems | decision making | decision support | probabilistic simulation | designing manufacturing systems | operations management | linear programming | sensitivity analysis | network flow problems | non-linear programming | Lagrange multipliers | integer programming | discrete-event simulation | heuristics | algorithms | 15.066 | 2.851 | 3.83

License

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1.203J Logistical and Transportation Planning Methods (MIT)

Description

The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics.

Subjects

1.203 | 6.281 | 15.073 | 16.76 | ESD.216 | logistics | transportation | hypercube models | barrier example | operations research | spatial queues | queueing models | network models | TSP | heuristics | geometrical probabilities | Markov | quantitative techniques | transportation systems analysis | urban service systems | emergency services | random variables | multi-server queueing theory | spatial location theory | network analysis | graph theory | simulation | urban OR

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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1.203J Logistical and Transportation Planning Methods (MIT)

Description

The class will cover quantitative techniques of Operations Research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, emergency repair services). It presents a unified study of functions of random variables, geometrical probability, multi-server queueing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. There will be discussion focused on the difficulty of implementation, among other topics.

Subjects

1.203 | 6.281 | 15.073 | 16.76 | ESD.216 | logistics | transportation | hypercube models | barrier example | operations research | spatial queues | queueing models | network models | TSP | heuristics | geometrical probabilities | Markov | quantitative techniques | transportation systems analysis | urban service systems | emergency services | random variables | multi-server queueing theory | spatial location theory | network analysis | graph theory | simulation | urban OR

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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6.803 The Human Intelligence Enterprise (MIT)

Description

6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | artificial intelligence | computational models | perception | cognition | neuroscience | human behavior | communication | heuristics | object tracking | object recognition | change representation | language evolution | Turing | Minsky

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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6.803 The Human Intelligence Enterprise (MIT)

Description

This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | artificial intelligence | computational models | perception | cognition | neuroscience | human behavior | communication | heuristics | object tracking | object recognition | change representation | language evolution | Turing | Minsky

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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16.892J Space System Architecture and Design (MIT)

Description

Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues.

Subjects

space system | space system architecture | space architecting | uncertainties | space policy | robustness | flexibility | optimality | tradespace analysis | quality function deployment | multi-attribute utility theory | n-squared | design structure matrix | multi-attribution tradespace exploration | MATE | MATE-CON | satellite | classes of space system | XTOS | spacetug | GINA | pareto fronts | engineering design process | optimization methods | genetic algorithms | simulated annealing | MMDOSA | distributed space systems design optimization | clarity test | taxonomy of uncertainty | treatment of uncertainty | irreducible uncertainty | portfolio theory | portfolio applications | taxonomy of flexibility | on-orbit servicing | US national space policy | space policy heuristics | policy architectures | 16.892 | ESD.353

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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