Serial Number
44122
Course Number
IM5059
Course Identifier
725 U3690
No Class
- 3 Credits
Elective
DEPARTMENT OF INFORMATION MANAGEMENT / GRADUATE INSTITUTE OF INFORMATION MANAGEMENT
DEPARTMENT OF INFORMATION MANAGEMENT
GRADUATE INSTITUTE OF INFORMATION MANAGEMENT
Elective- CHIA-YEN LEE
- View Courses Offered by Instructor
COLLEGE OF MANAGEMENT DEPARTMENT OF INFORMATION MANAGEMENT
- Thu 2, 3, 4
管一405
Type 2
40 Student Quota
NTU 40
No Specialization Program
- English
- NTU COOL
- Core Capabilities and Curriculum Planning
- NotesThe course is conducted in English。
NTU Enrollment Status
Enrolled0/40Other Depts0/0Remaining0Registered0- Course DescriptionThis course will provide students to learn the methodologies of operations research and its applications to the real problem. The models include deterministic models (such as linear programming, multi-criteria decision analysis, data envelopment analysis, etc.) and stochastic models (such as Bayesian decision analysis, stochastic programming, Markov decision process, etc.). The course integrates the knowledge domains of the management and engineering, applied in capacity planning, facility layout, supply chain, manufacturing scheduling, performance evaluation, vendor selection and order allocation, Bin-packing, financial investment, etc. We develop the implementation capability of the information system in practice. Finally we should know how to solve the real problem systematically using optimization or statistical methods.
- Course Objective- Know the advanced techniques of operations research - Create theoretical model to solve the problem in real setting - System development and implementation
- Course RequirementPrerequisites - Operations Research: ”Operations Research” in the IM department or equivalent. - Statistics: ”Statistics I” and “Statistics II” in the IM department or equivalent.
- Expected weekly study hours after class
- Office Hour
TBD
- Designated ReadingLecture Notes
- ReferencesBirge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming (2nd ed.). New York: Springer Verlag. Morse, P. M. and G. E. Kimball (1951, 2012). Methods of Operations Research. Dover Publications. Puterman, M. L. (2005). Markov Decision Processes: Discrete Stochastic Dynamic Programming. 2nd edition, Wiley-InterScience.
- Grading
50% Homework
50% Project & System Implementation
- Adjustment methods for students
Adjustment Method Description Teaching methods Assisted by recording
Assisted by video
Assignment submission methods Group report replace Personal report
Exam methods Written (oral) reports replace exams
- Course Schedule
9/07Week 1 9/07 Review of Linear Programming and Markov Chain (線性規劃與馬可夫鏈) 9/14Week 2 9/14 SP: Stochastic Programming with Two-stage Recourse Problem (隨機規劃) 9/21Week 3 9/21 SP: The Value of Information and the Stochastic Solution (資訊價值) 9/28Week 4 9/28 SP: Approximation and Sampling Methods (漸進與抽樣隨機規劃) 10/05Week 5 10/05 Capacity Planning and Stochastic Scheduling Optimization (產能規畫與隨機排程) 10/12Week 6 10/12 Dynamic Supply Chain Optimization and Nonlinear Cost Modelling (動態供應鏈與非線性成本) 10/19Week 7 10/19 Bin-packing Problem (Three-dimensional Knapsack Problem) and Piece-wise Linearization (貨櫃裝載三維度背包問題與分段線性化) 10/26Week 8 10/26 Multi-Objective Decision Analysis (多準則決策分析) 11/02Week 9 11/02 Specialist Lecture (專家演講與教學: 作業研究與實證) 11/09Week 10 11/09 Portfolio Optimization, Vendor Selection and Order Allocation (投資組合、廠商評選與訂單配置最佳化) 11/16Week 11 11/16 DEA: Data Envelopment Analysis (數據包絡分析法) 11/23Week 12 11/23 DEA: Data Envelopment Analysis (數據包絡分析法) 11/30Week 13 11/30 Stochastic Dynamic Programming (隨機動態規劃) 12/07Week 14 12/07 MDP: Markov Decision Processes (馬可夫決策過程) 12/14Week 15 12/14 RL: Reinforcement Learning (強化學習) 12/21Week 16 12/21 Team Project Discussion (分組實作討論) 12/28Week 17 12/28 No Class 1/04Week 18 1/04 No Class