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Swarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management

Swarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management

Qian Tian, Qingwei Yin, Yagang Meng
Copyright: © 2022 |Volume: 15 |Issue: 4 |Pages: 20
ISSN: 1935-5726|EISSN: 1935-5734|EISBN13: 9781683180272|DOI: 10.4018/IJISSCM.305845
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MLA

Tian, Qian, et al. "Swarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management." IJISSCM vol.15, no.4 2022: pp.1-20. http://doi.org/10.4018/IJISSCM.305845

APA

Tian, Q., Yin, Q., & Meng, Y. (2022). Swarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management. International Journal of Information Systems and Supply Chain Management (IJISSCM), 15(4), 1-20. http://doi.org/10.4018/IJISSCM.305845

Chicago

Tian, Qian, Qingwei Yin, and Yagang Meng. "Swarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management," International Journal of Information Systems and Supply Chain Management (IJISSCM) 15, no.4: 1-20. http://doi.org/10.4018/IJISSCM.305845

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Abstract

Supply chain management has become increasingly important as an academic subject due to globalization developments contributing to massive production-related benefits reallocation. The huge volume of data produced in the global economy means that new tools must be created to manage and evaluate the data and measure organizational performance worldwide. Smart technologies such as swarm intelligence and big data analytics can help get clear data of the location, condition, and environment of products and processes at any time, anywhere to make smart decisions and take corrective schedules that the supply chain can run more effectively. This study proposes the swarm intelligence modeling-based logistic analytics management (SIMLAM) in service supply chain market. A generalized structure for swarm intelligence implementation in supply chain management is suggested, which is advantageous to industry practitioners. Different deterministic methods practically fail due to the intrinsic computational complexity of the problem of higher dimensions.