References for graduate students and my laboratory members

Knowledge to master for a better foundation (and future)

Tools and general: 

•  Adi Ignatius, “We Want to Make Ourselves Better”: The HBR Interview with Bob Sternfels, Harvard Business Review, January–February 2026.  (new)

服務和計畫 (Service and Projects)

政府計畫 (Government Project)

• 國科會，高維度受限迴歸模型與稀疏特徵選取：快速座標下降演算法和應用 (High-Dimensional Constrained Regression Models and Sparse Feature Selection: Fast Coordinate Descent Algorithms and Applications)，主持人 (PI)，115 年度 (2026/8 - 2027/7)
• 國科會，公平最佳決策樹和其應用 (Fair and Optimal Decision Tree and Its Applications)，主持人 (PI)，114 年度 (2025/8 - 2026/7)

產學合作 (Industry-Academia Cooperation)

• 金屬工業研究發展中心 (Metal Industries Research & Development Centre)，最佳化決策於工站排程應用之研究 (Optimal Decision-making in Work Station Scheduling) (3)，主持人 (PI)，2024

作業研究競賽題目

Please cite this webpage if you use the material/method in this blog. Thank you. (如果您使用本部落格中的資料/方法，請註明此來源網頁。謝謝。)

• Some papers/information for idea generation or implementation

作業研究和機器學習

課程

• 作業研究 (上): 3.8 迴歸，11.2 迴歸，12.3.1 類神經
• Google Scholar: generative ai optimization
• 作業研究 (下): 2 Robust Optimization (穩健最佳化)，3 適應穩健最佳化，5 資料驅動的報童模型
• Applications in generative AI: diffusion probabilistic models 
• Dmytro Kuzmenko, Denoising diffusion probabilistic models
• Lilian Weng, What are Diffusion Models?
• Dimitris Bertsimas and Georgios Margaritis, Robust and Adaptive Optimization under a Large Language Model Lens, arXiv:2501.00568.
• 最佳化和機器學習：

  機器學習和作業研究的奇妙結合

Unforeseen Consequences of “Tabu” Choices

Fred W. Glover, Unforeseen Consequences of “Tabu” Choices—A Retrospective, INFORMS Journal on Computing, 2022, Volume 34, Issue 3, Pages 1306-1308.

This retrospective is written upon receiving The IJOC Test of Time Award for the papers “Tabu Search—Part 1 and —Part 2,” a delightful capstone to an unanticipated (and exceptionally fun) adventure.

2023 Franz Edelman Award

2023 Edelman Competition (video)

Prakhar Mehrotra et al., (2024) Optimizing Walmart’s Supply Chain from Strategy to Execution. INFORMS Journal on Applied Analytics 54(1):5-19. (2023 Franz Edelman Award) (Keywords: supply chain optimization, network design,  simulation,  truck routing and loading, mixed-integer programming,  metaheuristics)

An Exact Solution to Wordle

 Dimitris Bertsimas, Alex Paskov (2024) An Exact Solution to Wordle. Operations Research.

Interesting, Important, and Impactful Operations Management

Gerard P. Cachon, Karan Girotra, Serguei Netessine (2020) Interesting, Important, and Impactful Operations Management. Manufacturing & Service Operations Management 22(1):214-222. 

Optimization in Online Content Recommendation Services

Omar Besbes, Yonatan Gur, Assaf Zeevi, Optimization in Online Content Recommendation Services: Beyond Click-Through Rates, 18(1), pp. 15–33, Manufacturing & Service Operations Management, Volume 18, Issue 1, Winter 2016. 

A new class of online services allows Internet media sites to direct users from articles they are currently reading to other content they may be interested in. This process creates a “browsing path” along which there is potential for repeated interaction between the user and the provider, giving rise to a dynamic optimization problem. A key metric that often underlies this recommendation process is the click-through rate (CTR) of candidate articles. Whereas CTR is a measure of instantaneous click likelihood, we analyze the performance improvement that one may achieve by some lookahead that accounts for the potential future path of users. To that end, by using some data of user path history at major media sites, we introduce and derive a representation of content along two key dimensions: clickability, the likelihood to click to an article when it is recommended; and engageability, the likelihood to click from an article when it hosts a recommendation. We then propose a class of heuristics that leverage both clickability and engageability, and provide theoretical support for favoring such path-focused heuristics over myopic heuristics that focus only on clickability (no lookahead). We conduct a live pilot experiment that measures the performance of a practical proxy of our proposed class, when integrated into the operating system of a worldwide leading provider of content recommendations, allowing us to estimate the aggregate improvement in clicks per visit relative to the CTR-driven current practice. The documented improvement highlights the importance and the practicality of efficiently incorporating the future path of users in real time.

學習動力與方向 (2/2)

如同如何準備研究所中所言，不一定要念研究所。在網路時代，知識的取得已經非常方便，例如 edX 或 Coursera，所以大學部的基礎知識和自學的動力很重要 (1)。以 The Analytics Edge 而言，是 MIT 商業分析 (Business Analytics) 碩士的必修課，該碩士學費一年七萬美金，所以此線上免費課程價值一萬美金。

作業研究的持續改善

下學年要教我喜歡的作業研究。依照往例，再一次更新之前的版本。

第一學期，考慮確定的環境和有限資源下，使用最佳化 (optimization)，以最大化 (例如利潤) 或最小化目標  (例如成本)。第二學期，準備教不確定性環境下，如何分析、最佳化和做決策 (控制)。

為什麼工 (資) 管的課程看起來很雜？

(2022) 因為橫跨兩個學院 (工和商)，工業系龐雜的內容，和資管系一樣，所以修改一下標題。其實，我的部落格如何選填大學志願，就是以四個專業和李國鼎的話 (第 17 頁)，說明這兩個系的重要性。

(2014) 常常聽到學生有這樣的疑惑，我試著以營收管理說明之；針對固定 (如旅館房間、網頁上廣告空間) 且易過時 (如機位、時裝) 的容量或庫存供給下，如何有效地分配庫存 (屬於生管) 和 (動態) 定價 (屬於行銷)，以最大化企業之營收；詳細的內容可以參考我的課程。

Garrett van Ryzin talks about optimization

以前教營收管理的時候，讀了不少哥倫比亞商學院 van Ryzin 教授的文章，其中一篇說，他們的研究是在解決10年後的問題。各位注意喔，是商學院！

最近在讀一本書，某教授寫的序，開頭四個字，就是學用落差。

Machine Learning into Metaheuristics: A Survey and Taxonomy

El-Ghazali Talbi, Machine Learning into Metaheuristics: A Survey and Taxonomy, ACM Computing Surveys, Volume 54, Issue 6, July 2022, Article No.: 129, pp 1–32, https://doi.org/10.1145/3459664.

 During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.

YouTube video streaming now using A.I. that mastered chess and Go

JEREMY KAHN, YouTube video streaming now using A.I. that mastered chess and Go, Fortune, February 11, 2022.

The artificial intelligence algorithm, called MuZero, was developed by YouTube’s London-based sister company within Alphabet, DeepMind, which is dedicated to advanced A.I. research. When applied to YouTube videos, the system has resulted in a 4% reduction on average in the amount of data the video-sharing service needs to stream to users, with no noticeable loss in video quality.

A unified framework for stochastic optimization

W.B. Powell, A unified framework for stochastic optimization, European Journal of Operational Research, 2019, Volume 275, Issue 3, 16 June 2019, Pages 795-821. (pdf)

Stochastic optimization is an umbrella term that includes over a dozen fragmented communities, using a patchwork of sometimes overlapping notational systems with algorithmic strategies that are suited to specific classes of problems. This paper reviews the canonical models of these communities, and proposes a universal modeling framework that encompasses all of these competing approaches. At the heart is an objective function that optimizes over policies that is standard in some approaches, but foreign to others. We then identify four meta-classes of policies that encompasses all of the approaches that we have identified in the research literature or industry practice. In the process, we observe that any adaptive learning algorithm, whether it is derivative-based or derivative-free, is a form of policy that can be tuned to optimize either the cumulative reward (similar to multi-armed bandit problems) or final reward (as is used in ranking and selection or stochastic search). We argue that the principles of bandit problems, long a niche community, should become a core dimension of mainstream stochastic optimization.

47-779 Quantum Integer Programming

Prof. Sridhar Tayur, Dr. Davide Venturelli, and David Bernal, 47-779 Quantum Integer Programming (QuIP), Fall 2020. (lecture note)

This course is primarily designed for graduate students (and advanced undergraduates) across CMU campuses interested in integer programming (with non-linear objective functions) and the potential of near-term quantum computing for solving combinatorial optimization problems. By the end of the semester, someone enrolled in this course should be able to:

• Identify the current status of quantum computing and its potential uses for integer programming
• Access and use quantum computing resources (such as DWave Quantum Annealers)
• Set up a given integer program to be solved with quantum computing
• Work in groups collaboratively on a state-of-the-art project regarding applications of quantum computing and integer programming

Vattenfall Optimizes Offshore Wind Farm Design

Martina Fischetti, Jesper Runge Kristoffersen, Thomas Hjort, Michele Monaci, and David Pisinger,  Vattenfall Optimizes Offshore Wind Farm Design, INFORMS Journal on Applied Analytics, 2020, Vol. 50, No. 1, pp. 80–94.

In this paper, we describe the use of operations research for offshore wind farm design in Vattenfall, one of the world’s leading companies in the generation of offshore wind energy. We focus on two key aspects that Vattenfall must address in its wind farm design process. The first is determining where to locate the turbines. This aspect is important because the placement of each turbine creates interference on the neighboring turbines, causing a power loss at the overall farm level. The optimizers must minimize this interference based on the wind conditions; however, they must also consider the other costs involved, which depend on factors such as the water depth or soil conditions at each position. The second aspect involves determining how to interconnect the turbines with cables (i.e., cable optimization). This requires Vattenfall to consider both the immediate costs and long-term costs connected with the electrical infrastructure. We developed mixed-integer programming models and matheuristic techniques to solve the two problems as they arise in practical applications. The resulting tools have given Vattenfall a competitive advantage at multiple levels. They facilitate increased revenues and reduced costs of approximately 10 million euros of net present value (NPV) per farm, while ensuring a much faster, more streamlined, and efficient design process. Considering only the sites that Vattenfall has already acquired using our optimization tools, the company experienced NPV gains of more than 150 million euros. This has contributed substantially to its competitiveness in offshore tenders and made green energy cheaper for its end customers. The tools have also been used to design the first wind farms that will be constructed subsidy-free.

Martina Fischetti and David Pisinger, Mathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview, Business & Information Systems Engineering, 2019, Vol.61, No. 4, pp. 469-485. (Further details)

M. Fischetti, Mixed-integer models and algorithms for wind farm layout optimization. Master’s thesis, University of Padova, 2014. (Stochastic programming for wake effect)

Fischetti M, Fischetti M (2016) Matheuristics. Mart´ı P, Panos P, Resende MG, eds. Handbook of Heuristics (Springer International Publishing, Cham, Switzerland), 1–33.

中鋼如何用AI煉成智慧鋼廠

翁芊儒，組織、人才和技術5年布局， 中鋼如何用AI煉成智慧鋼廠，iThome，2021-03-04 

這一場變革，莫約從5年前開始推展。「我們展開數位轉型，是為了提升鋼鐵生產效率、降低成本、縮短交期，來提升產品競爭力。」中鋼技術部門代理副總經理鄭際昭，一句話點出轉型任務最重要的目的。...

IJAA 50th Anniversary Special Issue

 Special Issue of INFORMS Journal on Applied Analytics: Interfaces/IJAA 50th Anniversary Special Issue