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2019　Problem Solving and Decision Making

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Academic unit or major
Undergraduate major in Computer Science
Instructor(s)
Deguchi Hiroshi  Ono Isao  Komiya Ken 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Tue7-8(W611)  Fri7-8(W611)  
Group
-
Course number
CSC.T342
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/12/26
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course focuses on models and methodologies for optimization and decision making needed for system analysis and design. This course consists of two parts. The topics of the first part include the linear programming model, linear programming, the nonlinear programming model, nonlinear programming, multiobjective optimization and stochastic optimization. In the second part, students will learn criteria for decision making, decision making and information, and social dilemma through gaming, and then learn models and methodologies for decision making such as alternatives and selection criteria, game theory, institution and social simulation through lectures.
The aims of this course is to enable students 1) to acquire knowledge on optimization and decision making for system analysis and design, and 2) to apply the knowledge to solve real-world problems.

Student learning outcomes

By the end of this course, students will be able to:
1) Explain optimization problems and formulate real-world problems as optimization problems.
2) Explain the linear/nonlinear programming problems and linear/nonlinear programming, and solve linear/nonlinear programming problems.
3) Explain multiobjective optimization, typical multiobjective optimization methods, and typical stochastic optimization methods.
4) Learn the complexity of decision making by attending gaming simulation.
5) Learn the importance of institutional boundary condition in decision making process.
6) Learn the importance of information and model sharing in decision making process.

Keywords

Linear/nonlinear programming, multiobjective optimization, stochastic optimization, decision making, gaming simulation

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

In classes 2 to 7, exercise problems or homework are assigned. Their aim is to facilitate students’ understanding. In class 8, the level of understanding is assessed with exercise problems covering the contents of classes 2 to 7.
In classes 9 to 15, you have to attend the gaming simulation as players and learn about decison making through the gaming simulation.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction Understand the background and the aim of the course.
Class 2 Linear programming (1): the linear programming problem and the simplex method. Understand the linear programming problem and the simplex method.
Class 3 Linear programming (2): the two-phase method. Understand the two-phase method.
Class 4 Nonlinear programming (1): unconstrained nonlinear programming problems, optimality conditions and iterative methods. Understand the unconstrained nonlinear programming problem, optimality conditions and typical iterative methods.
Class 5 Nonlinear programming (2): global convergence and convergence rate, conjugate gradient method and quasi-Newton method. Understand global convergence, convergence rate, conjugate gradient method and quasi-Newton method.
Class 6 Nonlinear programming (3): constrained nonlinear programming problems, optimality conditions and iterative methods. Understand the constrained nonlinear programming problem, optimality conditions and typical iterative methods.
Class 7 Multiobjective optimization Understand the multiobjective optimization problem and typical multiobjective optimization methods.
Class 8 Test level of understanding with exercise problems covering the contents of classes 2 to 7. Assess level of understanding of the contents of classes 2 to 7.
Class 9 Decision Making through Gaming Simulation 1 A general introduction for decision making
Class 10 Decision Making through Gaming Simulation 2 Mars Message Playing the gaming called Lost in Space
Class 11 Decision Making through Gaming Simulation 3 Debriefing Discussion in the debriefing process of the gaming
Class 12 Decision Making through Gaming Simulation 4 Mars Message Playing the gaming called Mars Message
Class 13 Decision Making through Gaming Simulation 5 Debriefing Discussion in the debriefing process of the gaming
Class 14 Decision Making through Gaming Simulation 6 Environment Management Game Playing the gaming called Environment Management Game
Class 15 Decision Making through Gaming Simulation 7 Debriefing Discussion in the debriefing process of the gaming

Textbook(s)

None required. All materials used in class can be found on WEB site.

Reference books, course materials, etc.

Not Specified.

Assessment criteria and methods

Students’ course scores are based on the first part (50%) and the second one (50%). In the first part, students’ scores are based on exercise problems for assessing students’ level of understanding in class 8. In the last part, the evaluation is done by the reports about gaming.

Related courses

  • CSC.T362 ： Numerical Analysis
  • CSC.T351 ： System Analysis
  • CSC.T373 ： Dynamical Systems
  • CSC.T374 ： Control Systems

Prerequisites (i.e., required knowledge, skills, courses, etc.)

No prerequisites.

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