Journal of Engineering, Project, and Production Management, 2026, 16(4), 2025-269
Emergency Logistics Network Planning Based on Multi-Strategy Improved SSA
1 Lecturer, School of Economics and Management, Yunnan Vocational College of Mechanical and Electrical Technology, Kunming, 650201, China,
2 Associate Professor, Office of Academic Affairs, Yunnan University of Finance and Economics, Kunming, 650221, China, E-mail: gaotingfengtf@outlook.com (corresponding author)
Project Management
Received November 12, 2025; revised April 23, 2026; May 7, 2026; accepted May 13, 2026
Available online May 29, 2026
Abstract: The occurrence of public health incidents and natural disasters has seriously affected people’s daily lives. In emergency situations, supplies are often scarce, making it difficult to balance timeliness, cost, and demand urgency in emergency logistics planning. To enhance the SALP swarm algorithm (SSA), this study suggests an emergency logistics planning model based on several tactics. First, a multi-objective optimization model is constructed to minimize transportation time and total cost, and to maximize satisfaction of demand urgency. To balance the capacities of local exploitation and global exploration, the study employs a nonlinear, adaptive, inertial weighting mechanism. Additionally, it employs a Levy flight mechanism to increase the algorithm’s ability to escape local optima and a backpropagation learning technique to improve the quality of the original population. Experiments indicated that the improved algorithm achieved optimal optimization accuracy (mean squared error reduced to 1×10^-100) after approximately 90 iterations. The second-best SALP swarm algorithm attained optimal optimization accuracy of 1×10-15. During supply disruptions, the total cost of the improved SALP algorithm rose from the baseline value of USD 53480 to USD 58660 (an increase of USD 5180), which was lower than the USD 8820 increase observed in the whale optimization algorithm. The minimum relative unfairness of the improved algorithm was 0.083. After removing the nonlinear adaptive adjustment, the total logistics cost increased by USD 219,000. In summary, the proposed model can effectively enhance the coordination and planning capabilities of emergency logistics, reduce casualties and the occurrence of secondary disasters caused by material delays, and minimize economic losses.
Keywords: SALP swarm algorithm, Emergency logistics, Logistics network planning, Nonlinear adaptive, Demand urgency.
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Citation:Gao, Y. and Gao, T. (2026). Emergency Logistics Network Planning Based on Multi-Strategy Improved SSA. Journal of Engineering, Project, and Production Management, 16(4), 2025-269.
