Title: Swarm activity-based dynamic PSO for distribution decision

Authors: Yu Su; Lingjuan Hou

Addresses: School of Management, Tianjin Normal University, Tianjin 300387, China; The 54th Research Institute, China Electronics Technology Group Corporation, Shijiazhuang 050081, China; Graduate School, Sehan University, Mokpo 58613, South Korea ' School of Management, Tianjin Normal University, Tianjin 300387, China

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.

Keywords: particle swarm; swarm activity; dynamic inertia weight; distribution decision; supply chain optimisation.

DOI: 10.1504/IJAAC.2022.122620

International Journal of Automation and Control, 2022 Vol.16 No.3/4, pp.503 - 517

Received: 18 Feb 2021
Accepted: 29 Mar 2021
Published online: 04 May 2022 *

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