Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Swarm activity-based dynamic PSO for distribution decision

Swarm activity-based dynamic PSO for distribution decision The distribution decision is a complicated constrained optimisation problem that plays a key role in the production planning and inventory scheduling at the current era of intelligent big data, since few studies have developed new models integrating intelligent supply chain management. With the aim at the limitation of traditional methods which are difficult to obtain feasible solutions in large-scale search space with limited time, a new swarm activity-based intelligent optimisation algorithm, called PSO-SAW, is reconstructed in this paper by improving the particle swarm optimisation (PSO). The methodology is validated through several benchmarks and experimental applications to some distribution decision problems adopted from the literatures. Empirical results have implied the feasibility, effectiveness and robustness of the proposed method. Moreover, the experimental results of the algorithm have also verified the promising performances and applicability to distribution decision problems by comparing with other considered stochastic algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Automation and Control Inderscience Publishers

Swarm activity-based dynamic PSO for distribution decision

Loading next page...
 
/lp/inderscience-publishers/swarm-activity-based-dynamic-pso-for-distribution-decision-uQHCvyJFKu

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1740-7516
eISSN
1740-7524
DOI
10.1504/ijaac.2022.122620
Publisher site
See Article on Publisher Site

Abstract

The distribution decision is a complicated constrained optimisation problem that plays a key role in the production planning and inventory scheduling at the current era of intelligent big data, since few studies have developed new models integrating intelligent supply chain management. With the aim at the limitation of traditional methods which are difficult to obtain feasible solutions in large-scale search space with limited time, a new swarm activity-based intelligent optimisation algorithm, called PSO-SAW, is reconstructed in this paper by improving the particle swarm optimisation (PSO). The methodology is validated through several benchmarks and experimental applications to some distribution decision problems adopted from the literatures. Empirical results have implied the feasibility, effectiveness and robustness of the proposed method. Moreover, the experimental results of the algorithm have also verified the promising performances and applicability to distribution decision problems by comparing with other considered stochastic algorithms.

Journal

International Journal of Automation and ControlInderscience Publishers

Published: Jan 1, 2022

There are no references for this article.