Paper Info
Title
Research On Cargo-Loading Optimization Based On Genetic And Fuzzy Integration
Abstract
With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.
Year
DOI
Venue
2021
10.3233/JIFS-189669
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
DocType
Volume
Cargo loading, optimization model, genetic algorithm (GA), fuzzy control system (FCS)
Journal
40
Issue
ISSN
Citations 
4
1064-1246
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yanwei Du100.34
Feng Chen200.34
Xiaoyi Fan300.34
Lei Zhang400.34
Henggang Liang500.34