NTU Course
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Operations Research Applications and Implementation

Offered in 112-1
  • Serial Number

    44122

  • Course Number

    IM5059

  • Course Identifier

    725 U3690

  • No Class

  • 3 Credits
  • Elective

    DEPARTMENT OF INFORMATION MANAGEMENT / GRADUATE INSTITUTE OF INFORMATION MANAGEMENT

      Elective
    • DEPARTMENT OF INFORMATION MANAGEMENT

    • GRADUATE INSTITUTE OF INFORMATION MANAGEMENT

  • CHIA-YEN LEE
  • 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
  • Notes
    The course is conducted in English。
  • NTU Enrollment Status

    Enrolled
    0/40
    Other Depts
    0/0
    Remaining
    0
    Registered
    0
  • Course Description
    This 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 Requirement
    Prerequisites - 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 Reading
    Lecture Notes
  • References
    Birge, 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 MethodDescription
    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 1Review of Linear Programming and Markov Chain (線性規劃與馬可夫鏈)
    9/14Week 2SP: Stochastic Programming with Two-stage Recourse Problem (隨機規劃)
    9/21Week 3SP: The Value of Information and the Stochastic Solution (資訊價值)
    9/28Week 4SP: Approximation and Sampling Methods (漸進與抽樣隨機規劃)
    10/05Week 5Capacity Planning and Stochastic Scheduling Optimization (產能規畫與隨機排程)
    10/12Week 6Dynamic Supply Chain Optimization and Nonlinear Cost Modelling (動態供應鏈與非線性成本)
    10/19Week 7Bin-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 8Multi-Objective Decision Analysis (多準則決策分析)
    11/02Week 9Specialist Lecture (專家演講與教學: 作業研究與實證)
    11/09Week 10Portfolio Optimization, Vendor Selection and Order Allocation (投資組合、廠商評選與訂單配置最佳化)
    11/16Week 11DEA: Data Envelopment Analysis (數據包絡分析法)
    11/23Week 12DEA: Data Envelopment Analysis (數據包絡分析法)
    11/30Week 13Stochastic Dynamic Programming (隨機動態規劃)
    12/07Week 14MDP: Markov Decision Processes (馬可夫決策過程)
    12/14Week 15RL: Reinforcement Learning (強化學習)
    12/21Week 16Team Project Discussion (分組實作討論)
    12/28Week 17No Class
    1/04Week 18No Class