An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management

Hsin Ping Liu, Frederick Kin Hing Phoa*, Yun-Heh Chen-Burger, Shau Ping Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Introduction: The Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints. Method: In this work, we introduce an extension of the SIB method that can simultaneously tackle these two challenges. To pursue faster computing, CPU parallelization techniques are employed for algorithm acceleration. Results: The performance of the SIB method is examined on the problems of designing selling schemes in different scales. It outperforms the Genetic Algorithm (GA) in terms of both the speed of convergence and the optimized capacity as measured using improvement multipliers.

Original languageEnglish
Article number1283974
JournalFrontiers in Computational Neuroscience
Volume18
DOIs
Publication statusPublished - 18 Jan 2024

Keywords

  • CPU parallelization
  • selling scheme
  • supply chain management
  • swarm intelligence
  • tensor-type particle

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

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