Revolutionizing E-Commerce Logistics: AI-Driven Path Optimization for Sustainable Success

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Detalles Bibliográficos
Publicado en:	International Journal of Information Technologies and Systems Approach vol. 17, no. 1 (2024), p. 1
Autor principal:	Chen, Xia
Otros Autores:	Guo, Lina, Islam, Qamar
Publicado:	IGI Global
Materias:	Particle swarm optimization Integer programming Customer satisfaction Success Shelving Data analysis Inventory control Batch processing Multiple objective analysis Automation Efficiency Machine learning Big Data Dynamic programming Brand loyalty Genetic algorithms Artificial intelligence Sustainable development Route optimization Costs Decision making Spline functions Supply chains Stability Electronic commerce Simulated annealing Logistics Traveling salesman problem Optimization algorithms Inventory management Path planning Inventory Information management Logistics management Safety management
Acceso en línea:	Citation/Abstract Full Text - PDF
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Descripción
Resumen:	The traditional approach to logistics path planning is hindered by lengthy procedures. In this study, we explore the multi-objective optimization of logistics management, considering the conventional path and time efficiency indices alongside shelf safety and stability as additional objective functions. Based on particle swarm optimization (PSO), we optimize objective functions for internal path planning, scheduling timeliness, and shelf safety and stability. We then determine optimal routes under varying order demands using PSO and ultimately optimize the final path using dynamic programming and spline function restrictions to meet actual demand. Empirical results indicate that the proposed solution method outperforms other calculation methods, such as genetic algorithm (GA) and simulated annealing (SA), demonstrating over 10% improvement in time and total distance consumption. Further practical application tests demonstrate that the model in this study has a beneficial impact on all five distinct types of orders through efficient deployment optimization.
ISSN:	1935-570X 1935-5718
DOI:	10.4018/IJITSA.355016
Fuente:	Engineering Database