研究 (Research)

Journal articles:

• P.-S. Chen, H.-Y. Liu, and C.-H. Hsu, Economic Order Quantity of Deteriorating Drugs with New and Redistributed Demands in a Closed-loop Supply Chain: From the Viewpoint of a Hospital Pharmacy, International Journal of Systems Science: Operations & Logistics, 2026, Vol. 13, No. 1. (SCIE) 
• P.-S. Chen, J. Lyu,  C.-W. Chen, I.-H. Yu, and C.-H. Hsu, The Social-economic Value of a Renewable Energy Power Generation and Energy Storage System: A Case Study, International Journal of Systems Science: Operations & Logistics, 2026, Vol. 13, No. 1. (SCIE)
• H.-A. Bui, C.-H. Hsu, H.-W.V. Young, Y.-Y. Chen, and Y.-A. Liou, Advanced Semi-Supervised Learning for Remote Sensing-Based Land Cover Classification in the Mekong River Delta, Vietnam, Remote Sensing, 2026, 18, 989. https://doi.org/10.3390/rs18070989. (SCIE)

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

電路和電子學的產業知識

工業與系統工程學系隸屬於電機資訊學院，所以系上同學們都要修習電路學和電子學相關的課程。在電機系，這些是非常重要的基礎知識；而且，從 IC 設計、晶圓代工、到封裝測試，台灣有許多世界級的領導廠商，所以，值得同學們好好地學習相關的知識。更多資訊。

TaiwanPlus Docs, Inside Micron Taiwan’s Semiconductor Factory (美國美光科技在台灣的工廠，稱為外國直接投資 (Foreign direct investment, FDI))

以下介紹一些科普和整體產業的書籍和影片，希望可以增加同學們的學習動機，並且增加職涯發展的機會：

2024 Franz Edelman Award

2024 Edelman Competition (video, special issue, INFORMS Journal on Applied Analytics)

Pierre Pinson, Mikkel Bjørn, Simon Kristiansen, Claus B. Nielsen, Lasse Janerka, Jesper Skovgaard, Kristian Durhuus (2025) Data-Driven at Sea: Forecasting and Revenue Management at Molslinjen. INFORMS Journal on Applied Analytics 55(1):5-21. https://doi.org/10.1287/inte.2024.0177 (2024 Franz Edelman Award Winner) (Keywords: ferry operations, demand forecasting, revenue management, machine learning  (by XGBoost))

Robust Optimization Webinar

This is the youtube channel for the Robust Optimization Webinar (ROW) organized by Ahmadreza Marandi (TU Eindhoven) and Jannis Kurtz (University of Amsterdam). (Speaker)

Optimization Automates Emergency Department Nurse Scheduling at Hartford Hospital

Liangyuan Na, Jean Pauphilet, Ali Haddad-Sisakht, Louis Raison, Audrey Silver, Patricia Veronneau, Nicole Vogt, Dimitris Bertsimas (2024) Optimization Automates Emergency Department Nurse Scheduling at Hartford Hospital. INFORMS Journal on Applied Analytics.

Serve Robotics Level 4 delivery

Serve Robotics: 新聞

Alleviating Court Congestion

Shany Azaria, Boaz Ronen, Noam Shamir (2023) Alleviating Court Congestion: The Case of the Jerusalem District Court. INFORMS Journal on Applied Analytics, 54(3):267-281.

Fluid approximations for stochastic optimization

When one encounters a stochastic optimization/control problem, one popular approach is to transform it into a deterministic problem by fluid approximation. The following highly-cited classic papers illustrate the applications of this approach:   

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)

Minimizing Multimodular Functions and Allocating Capacity in Bike-Sharing Systems

Daniel Freund, Shane G. Henderson, David B. Shmoys (2022) Minimizing Multimodular Functions and Allocating Capacity in Bike-Sharing Systems. Operations Research 70(5):2715-2731. (Best OM Paper in Operations Research Award, arXiv)

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. 

AI 養魚解決老師傅技術失傳問題

邱倢芯，從魚缸到金目鱸養殖場，AI 養魚解決老師傅技術失傳問題，科技新報，2023 年 11 月 09 日

對於建置一套系統，許多業主最擔心的莫過於建置成本；對此，劉建伸坦言，系統本身的確不便宜，從導入初期至今已經投入 8 位數的成本，但後續帶來的效益也相當明顯，像是養殖戶過去都得長時間留守漁塭，在導入系統後，可將平均每天 8 小時的工作時間降低至 6.5 小時。

人力成本也可進一步降低，王靜儀估算，透過導入系統，每 100 公頃養殖面積的人力可從 33 人降低至 10 人，節省近 2,000 萬的人力成本。

Learning an Inventory Control Policy with General Inventory Arrival Dynamics

S Andaz, C Eisenach, D Madeka, K Torkkola, R Jia, D Foster, S Kakade, Learning an Inventory Control Policy with General Inventory Arrival Dynamics, 2023, arXiv preprint arXiv:2310.17168. (Amazon)

In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arrivals model (QOT). We also allow for order quantities to be modified as a post-processing step to meet vendor constraints such as order minimum and batch size constraints -- a common practice in real supply chains. To the best of our knowledge this is the first work to handle either arbitrary arrival dynamics or an arbitrary downstream post-processing of order quantities. Building upon recent work (Madeka et al., 2022) we similarly formulate the periodic review inventory control problem as an exogenous decision process, where most of the state is outside the control of the agent. Madeka et al. (2022) show how to construct a simulator that replays historic data to solve this class of problem. In our case, we incorporate a deep generative model for the arrivals process as part of the history replay. By formulating the problem as an exogenous decision process, we can apply results from Madeka et al. (2022) to obtain a reduction to supervised learning. Finally, we show via simulation studies that this approach yields statistically significant improvements in profitability over production baselines. Using data from an ongoing real-world A/B test, we show that Gen-QOT generalizes well to off-policy data.

Queueing Theory: Classical and Modern Methods

Dimitris Bertsimas and David Gamarnik, Queueing Theory: Classical and Modern Methods, ‎Dynamic Ideas, 2022.

STRUCTURE OF THE BOOK:

• Part I describes single and multi-server queues.
• Part II treats single and multiclass queueing networks (MQNETs).
• Part III introduces asymptotic methods, including queueing networks in heavy traffic, large deviations, call centers, queues in space, and the supermarket model.
• Part IV outlines the use of optimization in queueing networks.
• Part V presents Markov chains and processes, Brownian motion, and weak convergence in the Appendix.

Supervised machine learning for theory building and testing

Yen-Chun Chou, Howard Hao-Chun Chuang, Ping Chou, and Rogelio Oliva, Supervised machine learning for theory building and testing: Opportunities in operations management, Journal of Operations Management, 2023. pp. 1–33. (Codes in R)

Machine learning's (ML's) unique power to approximate functions and identify non-obvious regularities in data have attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains unclear how OM scholars can effectively leverage supervised ML for theory building and theory testing, the primary goals of scientific research. We attempt to fill this gap by conducting a literature review of recent developments in supervised ML in OM to identify vacancies in the extant literature, shedding light on how ML applications can move beyond problem-solving into theory building, and formulating a procedure to help OM scholars leverage ML for exploratory theory development. Our procedure employs the random forest with well-developed properties and inference toolkits that are crucial for empirical research. We then expand the boundary of ML usage and connect supervised ML to the explanatory modeling and hypothesis testing employed by OM empiricists for decades, and discuss the use of supervised ML for causal inference from observational data. We posit that contemporary ML can facilitate pattern exploration and enhance the validity of theory testing. We conclude by discussing directions for future empirical OM studies that aim to leverage ML.

失敗為成功之母？

有時候，很努力也不一定會有成果。

心情低落了好幾天，喝點小酒和親友聊聊天，轉換心情。

友達 AI 數位化打造高效供應鏈

吳珍儀，友達AI數位化打造高效供應鏈 蟬聯美國製造領導獎，Yahoo 財經，2023年6月29日

友達在智慧研發環節開發色彩飽和度模擬系統，引入大數據分析演算法，進行材料選用的相關模擬，提前預測客戶喜好的顏色和材料穿透率，使設計階段效率提升83%。在面板前段製程中，建立元件標準化資料庫並結合佈局設計演算法，成功縮短50%的光罩設計時程。

A Practical End-to-End Inventory Management Model with Deep Learning

Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen (2023) A Practical End-to-End Inventory Management Model with Deep Learning. Management Science 69(2):759-773. (Data and Python codes) 

We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.

2021 Franz Edelman Award

2021 Edelman Competition

Koen Peters, Sérgio Silva, Tim Sergio Wolter, Luis Anjos, Nina van Ettekoven, Éric Combette, Anna Melchiori, Hein Fleuren, Dick den Hertog, Özlem Ergun (2022) UN World Food Programme: Toward Zero Hunger with Analytics. INFORMS Journal on Applied Analytics 52(1):8-26. https://doi.org/10.1287/inte.2021.1097 (2021 Franz Edelman Award, WFP received the Nobel Peace Prize in 2020.)