Optimization and system implementation of fuzzy integrated algorithm model for logistics supply chain under supply and demand uncertainty background

While improving the operational efficiency of the enterprise, the logistics supply chain directly or indirectly affects the performance of the enterprise because of its own and external uncertainty, resulting in tangible or intangible losses. In this era of rapid change and increasing competition, r...

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Bibliographic Details
Published in:Neural computing & applications 2023-02, Vol.35 (6), p.4295-4305
Main Authors: Li, Yanfen, Yang, Jingyi, Wang, Yuancong
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Competitive advantage
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Genetic algorithms
Global optimization
Image Processing and Computer Vision
Linear programming
Logistics
Optimization
Optimization models
Probability and Statistics in Computer Science
S.I.: Artificial Intelligence Technologies in Sports and Art Data Applications
Special Issue on Artificial Intelligence Technologies in Sports and Art Data Applications
Supply & demand
Supply chains
Uncertainty
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Summary:While improving the operational efficiency of the enterprise, the logistics supply chain directly or indirectly affects the performance of the enterprise because of its own and external uncertainty, resulting in tangible or intangible losses. In this era of rapid change and increasing competition, reducing the impact of uncertainty can reduce the risk and vulnerability of the entire logistics service supply chain, and can gain or maintain a competitive advantage. Therefore, based on the background of supply and demand uncertainty, this paper establishes the fuzzy integrated optimization model of logistics supply chain system by using LR fuzzy numbers. In order to solve this model, the study carried out deterministic processing and transformed it into a deterministic multi-objective linear programming model. At the same time, this study also designed a genetic algorithm to solve the model, in order to solve the choice of potential supply and demand uncertainty in the system, and achieve the global optimization of the network. Finally, the calculations are carried out by numerical examples. The results prove the effectiveness of the model and algorithm.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07135-2