課程資訊
課程名稱
作業研究應用與實作
Operations Research Applications and Implementation 
開課學期
109-2 
授課對象
管理學院  資訊管理學系  
授課教師
李家岩 
課號
IM5059 
課程識別碼
725EU3690 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期二6,7,8(13:20~16:20) 
上課地點
管一204 
備註
本課程以英語授課。建議先修過作業研究、統計學。
限學士班四年級以上
總人數上限:24人
外系人數限制:2人 
 
課程簡介影片
 
核心能力關聯
本課程尚未建立核心能力關連
課程大綱
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課程概述

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. 

課程目標
- Know the advanced techniques of operations research
- Create theoretical model to solve the problem in real setting
- System development and implementation 
課程要求
Prerequisites
- Operations Research: ”Operations Research” in the IM department or equivalent.
- Statistics: ”Statistics I” and “Statistics II” in the IM department or equivalent.
 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
 
參考書目
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. 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
2/23  Review of Linear Programming and Markov Chain (線性規劃與馬可夫鏈) 
第2週
3/02  SP: Stochastic Programming with Two-stage Recourse Problem (隨機規劃) 
第3週
3/09  SP: The Value of Information and the Stochastic Solution (資訊價值) 
第4週
3/16  SP: Approximation and Sampling Methods (漸進與抽樣隨機規劃) 
第5週
3/23  Capacity Planning and Stochastic Scheduling Optimization (產能規畫與隨機排程) 
第6週
3/30  Dynamic Supply Chain Optimization and Nonlinear Cost Modelling (動態供應鏈與非線性成本) 
第7週
4/06  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) 
第8週
4/13  Multi-Objective Decision Analysis (多準則決策分析) 
第9週
4/20  Specialist Lecture (專家演講與教學: 作業研究與實證) 
第10週
4/27  Portfolio Optimization, Vendor Selection and Order Allocation (投資組合、廠商評選與訂單配置最佳化) 
第11週
5/04  DEA: Data Envelopment Analysis (數據包絡分析法) 
第12週
5/11  DEA: Data Envelopment Analysis (數據包絡分析法) 
第13週
5/18  Stochastic Dynamic Programming (隨機動態規劃) 
第14週
5/25  MDP: Markov Decision Processes (馬可夫決策過程) 
第15週
6/01  RL: Reinforcement Learning (強化學習) 
第16週
6/08  Team Project Discussion (分組實作討論) 
第17週
6/15  Team Project Discussion (分組實作討論)