题目: Data-Driven Analysis of Strategic-Operational Interfaces in Freight Electrification under Deep Uncertainty
时间：2024年11月11日上午 10：00
地点：浙江大学紫金港校区yl23411永利A523
主讲人：唐讴 教授，瑞典林雪平大学
主持人：霍宝锋 教授，yl23411永利
主讲人简介：
Ou Tang holds a full professorship in Production Economics since 2010 at Linköping University, Sweden, where he obtained a PhD in 2000. He served as associate editor and editor in the International Journal of Production Economics since 2008, he is the past-president of the International Society for Inventory Research, and currently president elected for the European Decision Science Institute. Ou Tang’s principal research interest is in the field of operations and supply chain management, more specifically it includes inventory modelling, manufacturing planning and control systems, closed loop supply chain management, sustainable supply chains，supply chain risk management, and China related operations management issues. He has published about 100 scientific articles in international journals such as the European Journal of Operational Research, Computers and Operations Research, Omega, International Journal of Production Economics, Production and Operations Management, and others.
Ou Tang has extensive industrial experience with his research projects. As the principal investigator, he has audited and analyzed production and logistics systems, and proposed improvement suggestions in about 50 companies such as Volvo, Scania, Toyota, Siemens, Hewlett-Packard, General Electric, Ericsson, Electrolux, IKEA，Sapa, SSAB, Stora Enso, Alfa lava, Atlas Copco, SKF, among others.
讲座摘要：Battery electric trucks (BETs) offer environmental benefits but have been challenged by technical and economic viability compared to internal combustion engine trucks (ICETs). Meanwhile, fleet owner-operators have difficulties applying lifecycle cost analyses and simulations to strategic decisions regarding electrification due to missing details of operational fleet management, high uncertainty, and lack of generalizability. Therefore, in this study we develop a data-driven method to systematically analyze BET and ICET fleets for over 100,000 scenarios to measure the effects of strategic decisions, operational optimization, and deep uncertainty on Total Cost-of-Ownership (TCO) and relative competitiveness. We find that electrification becomes more efficient at scale, with larger fleet sizes allowing route and charging optimization to better minimize the impact of operational constraints, thus improving service level, charging times, and utilization. Partial electrification of fleets for large networks shows to be a robust pathway regardless of uncertainties such as battery prices and future battery technology.
