Improved Swarm Intelligence-Based Logistics Distribution Optimizer: Decision Support for Multimodal Transportation of Cross-Border E-Commerce

Jiayi Xu, Mario Di Nardo () and Shi Yin
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Jiayi Xu: School of International Trade and Economics, Anhui University of Finance and Economics, Bengbu 233030, China
Mario Di Nardo: Department of Chemical, Materials, and Production Engineering, University of Naples Federico II, 80138 Naples, Italy
Shi Yin: Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650504, China

Mathematics, 2024, vol. 12, issue 5, 1-20

Abstract: Cross-border e-commerce logistics activities increasingly use multimodal transportation modes. In this transportation mode, the use of high-performance optimizers to provide decision support for multimodal transportation for cross-border e-commerce needs to be given attention. This study constructs a logistics distribution optimization model for cross-border e-commerce multimodal transportation. The mathematical model aims to minimize distribution costs, minimize carbon emissions during the distribution process, and maximize customer satisfaction as objective functions. It also considers constraints from multiple dimensions, such as cargo aircraft and vehicle load limitations. Meanwhile, corresponding improvement strategies were designed based on the Sand Cat Swarm Optimization (SCSO) algorithm. An improved swarm intelligence algorithm was proposed to develop an optimizer based on the improved swarm intelligence algorithm for model solving. The effectiveness of the proposed mathematical model and improved swarm intelligence algorithm was verified through a real-world case of cross-border e-commerce logistics transportation. The results indicate that using the proposed solution in this study, the cost of delivery and carbon emissions can be reduced, while customer satisfaction can be improved.

Keywords: cross-border e-commerce; multimodal transport; swarm intelligence algorithm; logistics distribution; sand cat swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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