Topic: Optimal Intraproject Learning
Speaker: Prof. Guohua Wan
Location: Room 306, Glorious Sun Building, Yan'an Road Campus
Time: 2021-01-06 15:30:00
Brief introduction of the speaker: Guohua Wan is currently a professor at Antai College of Economics & Management, Shanghai Jiao Tong University. His main research interests are: theory and algorithms of sequencing and scheduling, operations and supply chain management, and enterprise information management. His research results have been published in Management Science, Operations Research, Mathematics of Operations Research, Production and Operations Management, INFORMS Journal on Computing and other international academic journals. He is currently the Senior Editor of Production and Operations Management, the flagship journal of the Production and Operations Management Society (POMS), the Associate Editor of Journal of Management Analytics, and the editorial board member of Journal of Systems Management. He is a member of the Editorial Board of the Journal of Management Analytics and the Journal of Systems & Management. He is also a member of the Executive Board of the Operations Research Society of China, the Chairman of the Medical Management Branch and the Vice Chairman of the Operations Research Society of Shanghai.
Report Overview: Motivated by a project management problem faced by the CBG of Huawei Corporation, we study the problem of intraproject learning, where information gained from certain completed tasks in a project may be used to complete similar later tasks in the same project more efficiently. While the project management literature suggests the potential value of using this information within the same project, our work is apparently the first to model and optimize intraproject learning. We model the tradeoff between investing time in learning from completed tasks and achieving reduced durations for subsequent tasks, to minimize total project cost. We show intractability of the model and develop a heuristic that finds near optimal solutions and a strong relaxation that allows some learning from partially completed tasks. For the project at Huawei Corporation, our model and algorithm provide more than 20% improvement in project duration. In general, our model and algorithm provide learning solutions that enable reductions in project duration averaging 7% for projects with 40 tasks and 22% for those with 200 tasks. Our sensitivity analysis results identify project characteristics where intraproject learning is most beneficial, thus motivate project managers to understand and apply intraproject learning to improve the performance of their ongoing projects.