1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际课需要或在课程检讨之后产生变动。如对课程有任何疑问,
联系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
运筹与优化 Operational Research and Optimization
2.
授课院系
Originating Department
统计与数据科学系 Department of Statistics and Data Science
3.
课程编号
Course Code
STA201
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业基础课 Major Foundational Courses
6.
授课学期
Semester
秋季 Fall
7.
授课语言
Teaching Language
中英双语 English & Chinese
8.
他授课教师)
Instructor(s), Affiliation&
Contact
For team teaching, please list
all instructors
9.
验员/、所、联
方式
Tutor/TA(s), Contact
NA / To be announced / / Please list all
Tutor/TA(s)
(请保留相应选项 Please only keep the relevant information
10.
选课人数限额(可不)
Maximum Enrolment
Optional
2
11.
授课方式
Delivery Method
讲授
Lectures
实验/
Lab/Practical
其它(具体注明)
OtherPleasespecify
总学时
Total
学时数
Credit Hours
48
48
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
MA107 高等代数 I / MA107A 线性代数 A
MA107 Advanced Linear Algebra I /MA107ALinear Algebra A
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
14.
其它要求修读本课程的学系
Cross-listing Dept.
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
本课程通过理论与实例相结合的形式使得学生能充分掌握运筹与优化的基本工具、理论和方法, 尤其在数学和数据科学领
域。初步掌握模型化方法来描述、分析、建模和求解现实的运筹优化问题,进而支持决策。
By providing a balanced view of "theory" and "practice", the course should allow the student to understand, use, and
build practical analysis and planning of complex systems in the field of big data and data science. The goal of this
course is to teach students to formulate, analyze, and solve mathematical models that represent real-world optimization
problems for decision analysis.
16.
预达学习成果 Learning Outcomes
通过本课程的学习,学生预期可达到:
掌握运筹与优化分支(线性&动态规划、网络优化、运输与指派、排队论、数据建模等)的基本理论、方法
运用所学知识和方法对实际优化及数据问题进行量化分析、建模、检验及应用推广
独立或以小组的形式分析运筹优化的应用案例并给出最佳决策
应用课程提供的软件( Matlab, excel)解决实际优化问题
On successful completion of the course, students should be able to:
Understand the basic theoretical workings (including linear and dynamic programming, the transportation and
assignment problems; network optimization models, dynamic programming, queueing models, and
mathematical models in data science, etc. ) in operational research and optimization.
Formulate a real-world problem as a mathematical model and quickly explore, solve it if possible, check and
and apply it.
Build their own formulations independently or in groups, to expand existing formulations, to critically evaluate
the impact of model assumptions and to choose an appropriate solution technique for a given formulation.
Be able to implement practical cases by software( MATLAB, .etc) presented in class.
17.
课程内容及教学日历 (如授课语言以英文为主,则课程内容介绍可以用英文;如团队教学或模块教学,教学日历须注明
主讲人)
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)