Journal of Engineering, Project, and Production Management, 2026, 16(4), 2025-227
Optimization of Emergency Logistics Transport Scheduling in Multi-DC Supply Chains Based on SA-GOA
1 Lecturer,
School of Humanities and Management, Xi’an Traffic Engineering
Institute, Xi’an 710300, China
E-mail: yanmm9809@163.com (corresponding author).
2 Lecturer, School of Automobile, Chang’an
University, Xi’an, 710021, China,
3 Instructor, School of Humanities and
Management, Xi’an Traffic Engineering University,710300, China
Project Management
Received October 13, 2025; revised April 14, 2026; April 27, 2026; accepted May 14, 2026
Available online May 29, 2026
Abstract: Given the challenges of climate change in the world today and constant health scares among the population, it is essential to have an optimal emergency logistics transport scheduling process that will help in minimizing losses during disasters. However, current methods lack dynamic adaptability and effective multi-objective coordination. This study introduces a logistics transport scheduling optimization model that combines the Simulated Annealing (SA) algorithm with the Grasshopper Optimization Algorithm (GOA). By leveraging the strengths of Simulated Annealing, Grasshopper Optimization, Hybrid Particle Swarm Optimization (HPSO), and NSGA-II, the model achieves efficient global exploration and multi-objective optimization. Experimental results demonstrate that the model reaches 99% accuracy in cost prediction, 98% on-time delivery rate and goods integrity rates, and 95% demand satisfaction, significantly outperforming comparison models. These results demonstrate the significant adaptability and relevance of the model to the emergency logistics scheduling problem under multiple distribution centers, offering a flexible approach towards future transportation solutions.
Keywords: SA-GOA, Multi-Distribution center, HPSO, NSGA-II, Logistics scheduling, Supply Chains.
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Citation: Yan, M., Xu, C., and Feng, Y. (2026). Optimization of Emergency Logistics Transport Scheduling in Multi-DC Supply Chains Based on SA-GOA. Journal of Engineering, Project, and Production Management, 16(4), 2025-227.
