Bi-level Programming-Based Optimization of Vehicle Scheduling and Warehouse Operations in Power Logistics Parks

yongjie ma ("1.School of Traffic and Transportation Engineering, Kunming University of Science and Technology 2.China Southern Power Grid Internet Service Co., Ltd.")
Ji-quan Zhang (Southern Power Grid Supply Chain (Yunnan) Co., Ltd.；)
Wen-xian Guo (Southern Power Grid Supply Chain (Yunnan) Co., Ltd.；)
Tian-chi Deng (Southern Power Grid Supply Chain (Yunnan) Co., Ltd.；)
Yong-xin Jing (Southern Power Grid Supply Chain (Yunnan) Co., Ltd.；)
Ji-yuan Chen (Southern Power Grid Supply Chain (Yunnan) Co., Ltd.；)
xian-guang jia (School of Traffic and Transportation Engineering, Kunming University of Science and Technology)

International Journal of Advanced Science and Computer Applications
Vol. 5 No. 1 (2026): March 2026

To address the coordination between vehicle scheduling and warehouse operations in power logistics parks, this paper develops a bi-level programming model: the upper level optimizes supply chain vehicle scheduling, and the lower level optimizes warehouse inspection and inbound–outbound operations. An improved Carnivorous Plant Algorithm (ISCMA-CPA), integrating self-learning, Cauchy mutation, and mayfly reproduction strategies, is proposed to solve the model. Real-world data from nine vehicles and 45 material types are used for validation. Compared with benchmark algorithms (WOA, IWOA, GA, MA, CPA, AFSC), ISCMA-CPA achieves superior performance: at the upper level, average working time, waiting time, and travel distance are reduced by 35.3%, 75.0%, and 9.3%, respectively, and the objective value decreases by 0.9%; at the lower level, inspection time and path distance decrease by 14.4%, achieving the lowest objective value of 7276.96. The results demonstrate that the proposed bi-level model and algorithm effectively shorten operation time, reduce waiting and travel distances, and realize coordinated optimization of vehicle scheduling and warehouse operations. Overall, this study provides a practical optimization framework that enhances operational efficiency, supports intelligent decision-making, and promotes the green transformation of power material logistics.