Electric or fuel? Green routing optimization with time uncertainty in fourth-party logistics
5月 8, 2026·,
,,,·
0 分钟阅读时长
Hongliang Xu
Min Huang*
Songchen Jiang
Yuxin Zhang
Junwei Wang
Xingwei Wang
摘要
This paper investigates a green fourth-party logistics (4PL) routing optimization problem, incorporating transportation time uncertainty and the strategic choice between fuel vehicles (FVs) and electric vehicles (EVs) to reduce carbon emissions. To ensure timely delivery under time uncertainty, a mixed-integer chance-constrained programming (MICCP) model is developed. Given limited historical data, the MICCP model is reformulated through sample average approximation method and a Sample Mean-based Heuristic (SMH) algorithm is further proposed, which works as a data-driven method for potentially broader chance constrained programming with left-hand uncertainty. Numerical study validates the effectiveness of proposed models and algorithm, and offers managerial insights on balancing electric and fuel vehicle usage. We find that (1) stricter carbon tax policies do not necessarily motivate 4PLs to adopt EVs for emission reduction; (2) increasing switching time between transport providers will weaken the pooling advantages of 4PL mode, but encouraging the adoption of EVs.
类型
出版物
Transportation Research Part D: Transport and Environment
Authors
Assistant Professor
I will soon join the School of Management at Xi’an Jiaotong University. I received my Ph.D. degree from the College of Information Science and Engineering at Northeastern University, China, under the supervision of Prof. Min Huang. During my doctoral studies, I was also a visiting Ph.D. student at the Institute of Operations Research and Analytics, National University of Singapore, supervised by Prof. Chung-Piaw Teo. My research focuses on data-driven optimization, distributionally robust optimization, and stochastic modeling, with applications in supply chain management, such as inventory optimization, supply chain network design, logistics planning.