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Logistic Analytics Management in the Service Supply Chain Market Using Swarm Intelligence Modelling

Logistic Analytics Management in the Service Supply Chain Market Using Swarm Intelligence Modelling

Congcong Wang
Copyright: © 2022 |Volume: 15 |Issue: 4 |Pages: 16
ISSN: 1935-5726|EISSN: 1935-5734|EISBN13: 9781683180272|DOI: 10.4018/IJISSCM.305851
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MLA

Wang, Congcong. "Logistic Analytics Management in the Service Supply Chain Market Using Swarm Intelligence Modelling." IJISSCM vol.15, no.4 2022: pp.1-16. http://doi.org/10.4018/IJISSCM.305851

APA

Wang, C. (2022). Logistic Analytics Management in the Service Supply Chain Market Using Swarm Intelligence Modelling. International Journal of Information Systems and Supply Chain Management (IJISSCM), 15(4), 1-16. http://doi.org/10.4018/IJISSCM.305851

Chicago

Wang, Congcong. "Logistic Analytics Management in the Service Supply Chain Market Using Swarm Intelligence Modelling," International Journal of Information Systems and Supply Chain Management (IJISSCM) 15, no.4: 1-16. http://doi.org/10.4018/IJISSCM.305851

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Abstract

The industry sustainability in today's globalization relies on cost-effective supply chain management of diverse markets and logistics. Supply chain risks typically limit profits over the overall expense of the supply chain. In the supply chain design practices, the volatility of demand and limitations of levels are essential concerns. In this paper, a swarm intelligence-assisted supply chain management framework (SISCMF) has been proposed to increase profit and improve logistics performance. Due to the simplicity of design and rapid convergence, swarm intelligence (SI) algorithms are widely used in most supply network design fields and efficiently solve large-dimensional problems. A significant increase in resolving these problems has been seen in particle swarm optimization and ant colony algorithm. The simulation result suggested the operational cost (92.7%), demand prediction ratio (95.2%), order delivery ratio (96.9%), customer feedback ratio (98.2%), and product quality ratio (97.2%).