報告時間：2023年10月13日（星期五）14:30-15:30
報告地點：管理學(xué)院新大樓第二學(xué)術(shù)報告廳
報 告 人：鄧天虎 博士
工作單位：清華大學(xué)
舉辦單位：管理學(xué)院
報告簡介：
This paper investigates the optimization of periodic-review assemble-to-order (ATO) production systems with multiple products assembled from multiple components, under the data-driven setting where only historical demand data is available and demand distributions are unknown. To address this challenge, we propose a semi-model-based fitted Q iteration (S-FQI) algorithm framework that leverages the known transition dynamics. We provide a proof of the statistical convergence rate of the proposed algorithm concerning the number of iterations, the number of demand samples, and the number of generated trajectories.
Additionally, we introduce the convex-TD3 (CTD3) algorithm to tackle practical challenges by incorporating the convex property of ATO systems and utilizing an input convex neural network (ICNN) to improve efficiency and effectiveness.
報告人簡介：
鄧天虎，鄧天虎（博士，副教授）目前就職于清華大學(xué)工業(yè)工程系。2013年于美國加州大學(xué)伯克利分校獲得工業(yè)工程與運籌博士學(xué)位，2008年于清華大學(xué)工業(yè)工程系獲得學(xué)士學(xué)位。目前研究方向側(cè)重智慧供應(yīng)鏈。以第一作者和通訊作者在Manufacturing & Service Operations Management、Operations Research等國際學(xué)術(shù)期刊和學(xué)術(shù)會議發(fā)表論文20余篇。
