Operations-Research-Applications

內容概述

此網頁為國立臺灣大學資訊管理學系與國立成功大學製造資訊與系統研究所的「作業研究應用與實作」課程網頁。 以統計方法與最佳化理論為基礎，深入探討各種作業研究模型於實務應用問題，包含產能規劃、供應鏈管理、績效評估、背包問題、設施規劃、投資組合、保險醫療等。 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.

授課老師為李家岩老師

編輯群    

編輯者	暱稱	LinkedIn
李家岩	Chia-Yen Lee	http://polab.im.ntu.edu.tw/Bio.html

Fundamentals for Linear Programming

1. Python+Gurobi / Python+Pulp

Fundamentals for Robust Optimization

1. Python for Robust Optimization

隨機規劃 Stochastic Programming

1. 漸進抽樣法pdf(Sample Average Approximation)
2. 漸進抽樣法code(Sample Average Approximation)
3. 隨機預防保養排程(Preventive Maintenance Scheduling)
4. 隨機需求的人員排程(Staff Scheduling For Stochastic Demands)
5. 運算中心的綠色排程優化(Green-schedule-optimization-for-heterogeneous-computing-center)
6. 非接觸式物資遞送政策的救災物流最佳化(Disaster Relief Logistics with Contactless Delivery Policy)

模型與應用實例

1. 動態供應鏈(Dynamic Supply Chain Management)
2. 貨櫃裝載問題(3D-Bin Packing Problem)
3. 專案組合選擇最佳化(Project Portfolio Selection Problem (PPSP))
4. 生成對抗網路於文字摘要(GAN-based Method for Text Summarization)
5. 需求預測與動態定價於數位通路(Demand estimation and dynamic pricing in digital channels)
6. 逆向物流設施選址(Facility location for reverse logistics of returns)
7. 考慮易貨交換的報童問題(Newsvendor problem with barter exchange)
8. 迴歸與梯度下降法於摩擦力估計(Regression and Gradient Descent for Friction Model Estimation)
9. 集成學習參數最佳化(Parameters Optimization in Ensemble Learning)
10. 多媒體隨選數據路徑最佳化(Data Routing Optimization for Multimedia on Demand)
11. 社交距離座位安排最佳化(Seat Assignments with Social Distance and Minimum Volume in COVID)
12. 文字知識圖譜與作業研究(Textual Knowledge Graph and Operations Research)
13. 救災階段不確定性下的多商品分配 (Multi-Commodity Distribution Under Uncertainty in Disaster Response Phase)
14. COVID19運送服務下的救災物流 (Disaster-Relief-Logistics-Under-Covid-19-Delivery-Service)

多目標決策分析 Multi-Objective Decision Analysis

1. MODA: Prior Articulation of Preference- Compromise Programming
2. MODA: Progressive Articulation of Preferences- The Step Method(STEM)
3. 投資組合最佳化(Investment Portfolio Optimization)
4. 廠商評選與訂單配置(Vendor Selection and Order Allocation)
5. 多目標綠色供應鏈(Multi-Objective Green Supply Chain Optimization)
6. 貝式投資組合最佳化(Black-Litterman Portfolio Optimization)

數據包絡分析 Data Envelopment Analysis (DEA)

1. 數據包絡分析概論(Introduction to Data Envelopment Analysis)
2. 數據包絡分析(Data Envelopment Analysis)
3. 網路數據包絡分析(Network Data Envelopment Analysis)
4. 隨機無母數數據包絡(Stochastic Nonparametric Envelopment of Data, StoNED)
5. 汙染物邊際減排成本估計(Marginal Abatement Cost Estimation of CO2, SO2, and NOx)
6. 保險業效率分析-網路DEA(Efficiency Measure in Insurance Industry- Network DEA)
7. 網路數據包絡分析於供應鏈上下游績效評估(Supply Chain Performance Evaluation using network data envelopment analysis (NDEA))

強化學習 Reinforcement Learning

1. 馬可夫決策過程(Markov Decision Process)
2. 強化學習概論(Introduction to Reinforcement Learning)
3. 深度強化學習概論(Introduction to Deep Reinforcement Learning)
4. 多目標強化學習(Multi-Objective Reinforement Learning)
5. 強化學習於流線式生產排程1(Reinforcement Learning for Flow Shop Scheduling
6. 強化學習於半導體生產排程2(Reinforcement Learning for Semiconductor Scheduling)
7. 強化學習於零工式生產排程3(Reinforcement Learning for Job Shop Scheduling)
8. 強化學習於預防保養優化(Reinforcement Learning in Preventive Maintenance Policy)
9. 強化學習於動態定價(Reinforcement Learning for Dynamic Pricing)
10. 馬可夫決策過程於COVID-19預防策略(Markov Decision Process for COVID-19 Prevemtion Strategy)
11. 兩玩家多台吃角子老虎機策略(Strategy for Two Players Multi-Armed Bandit)
12. 強化學習於預防保養與生產排程(Reinforcement-Learning-in-Preventive-Maintenance-and-production-scheduling)
13. 強化學習於交通號誌控制(Deep-Reinforcement-Learning-for-Traffic-Signal-Control-Problem)

生產與服務排程 Production Scheduling

1. 數學規劃 Mathematical Programming for Job Shop Scheduling
2. 數學規劃 Mathematical Programming for Flexible Job Shop
3. 基因演算法於生產排程1(Genetic Algorithm for Job Shop Scheduling-1)
4. 基因演算法於生產排程2(Genetic Algorithm for Job Shop Scheduling-2)
5. Nondominated Sorting Genetic Algorithm II (NSGA-II for Job Shop Scheduling)
6. 改良NSGA-II與NSGA-III求解生產排程(Improved NSGA-II and NSGA-III for Job Shop Scheduling)
7. 考慮預防保養的生產排程1(Job Scheduling Problem considering Preventive Maintenance)
8. 海運航線排程(Shipping Scheduling)
9. 火車列車長排班排程(Train Captains Scheduling at the Taiwan Railways Administration)
10. 加工時間變異下的彈性生產排程(Flexible Job Shop Scheduling Problem with Variable Processing Time)

運輸規劃與排程 Transportation**

1. 強化學習於共享計程車(Reinforcement Learning in Carpooling problem)
2. 自駕車共享排程系統(Scheduling System for Autonomous Vehicle Sharing)
3. 循環經濟下的車輛共享系統(Circular Economy with Vehicle Sharing)

供應鏈管理 Supply Chain Management**

1. 權衡價格與品質的綠色供應鏈協作系統(Balancing Price and Green Quality in Green Supply Chain Coordination)

人工智慧/機器學習/數據科學 and 作業研究 AI/ML/DS and OR

1. 支持向量機於代價敏感特徵挑選(Cost-sensitive Feature Selection for Support Vector Machines)

聖誕老公公大數據競賽 Kaggle's Christmas Contest

1. 看片組合最佳化問題(Santa-2021-The Merry Movie Montage- Asymmetric traveling salesman problem)

啟發式演算法 Metaheuristic

1. 禁忌搜尋法(Tabu Search Algorithm)
2. 模擬退火法(Simulated Annealing Algorithm)
3. 基因演算法(Genetic Algorithm)
4. 混合蟻群最佳化與基因演算法求解旅行推銷員問題(Hybrid of ACO and GA for Traveling Salesman Problem, TSP)
5. 粒子群最佳化與天牛鬚搜尋於機械手臂運動規劃(PSO and BAS for Manipulator Motion Planning)

📌 Python

🚩 其他介紹