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Cluster Computing

Erschienen in:

23.03.2018

Supply chain scheduling optimization based on genetic particle swarm optimization algorithm

verfasst von: Feng Xiong, Peisong Gong, P. Jin, J. F. Fan

Erschienen in: Cluster Computing | Sonderheft 6/2019

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Abstract

In order to optimize supply chain scheduling problem in mass customization mode, the mathematical programming of supply chain scheduling optimization problem is modelled. At the same time, model mapping is defined as a directed graph to facilitate the application of intelligent search algorithms. In addition, the features of genetic algorithm and particle swarm algorithm are introduced. Genetic algorithm has a strong global search capability, and particle swarm optimization algorithm has fast convergence speed. Therefore, the two algorithms are combined to construct a hybrid algorithm. Finally, the hybrid algorithm is used to solve the supply chain optimization scheduling problem model. Compared with other algorithms, the results show that the hybrid algorithm has better performance. The mathematical programming model used in this paper can be further extended and improved.

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Titel: Supply chain scheduling optimization based on genetic particle swarm optimization algorithm
verfasst von: Feng Xiong
Peisong Gong
P. Jin
J. F. Fan
Publikationsdatum: 23.03.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 6/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-2400-z

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