运作管理学术论坛（10）：李德彪

时间：2026-06-17

报告人：李德彪（福州大学）

报告题目： Feature-driven Robust Stochastic Scheduling for Printed Circuit Board Assembly

报告时间：2026年6月22日（周一）上午9:00-10:00

报告地点：劝学楼425室

主办单位：运作管理与优化决策研究团队

【报告人简介】

李德彪，工学博士，福州大学经济与管理学院教授，博士生导师，福州大学经济与管理学院院长助理。博士毕业于美国纽约州立大学宾汉姆顿分校系统科学与工业工程系。上海交通大学管理科学与工程博士后。兼任中国仿真学会智能仿真优化与调度专委会常务委员，中国运筹学会医疗运作分会常务理事，中国系统工程学会智能制造系统工程分会委员，中国运筹学会排序调度分会理事。从事“生产与运作管理”方向研究，主要应用于制造和医疗行业。主持国家自然科学基金面上项目和青年项目等十余项科研项目。在POMS, EJOR, IEEE TASE, Omega, IJPR, COR等国际期刊和会议上发表40余篇论文。受邀担《Computers in Industry》（ABS三星）期刊副主编，《Complex System Modeling and Simulation》期刊副主编。获得福建省自然科学基金“杰出青年”项目，福建省“百人计划”青年项目等荣誉。

【报告摘要】

Motivated by an industry project, this talk addresses scheduling in printed circuit board assembly (PCBA), a bottleneck of electronic manufacturing. We consider uncertain processing and setup times arising from machine variability and human intervention, and model the problem as identical parallel machine scheduling to minimize total completion time and makespan. We develop a feature-driven robust stochastic optimization model that embeds production features into decision-making: processing times are predicted via support vector regression, while setup uncertainty is captured through event-wise ambiguity sets constructed by K-means clustering. The model is reformulated as a mixed-integer linear program and solved using a branch-and-price (B&P) algorithm. Experiments on real-world data show that the proposed approach outperforms sample average approximation (SAA) and standard distributionally robust optimization (DRO) by 52% and 33%, respectively. The B&P algorithm scales well to realistic instances, and sensitivity analysis reveals the impact of setup scenarios on the trade-off between solution quality and computational effort.

撰稿：赵永丽     审核：吴志樵   印明鹤