Volume 48 Issue 5
May  2022
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Article Contents
ZHANG Changyong, LIU Jiayu. Multi-box container loading problem based on hybrid genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 747-755. doi: 10.13700/j.bh.1001-5965.2020.0665(in Chinese)
Citation: ZHANG Changyong, LIU Jiayu. Multi-box container loading problem based on hybrid genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 747-755. doi: 10.13700/j.bh.1001-5965.2020.0665(in Chinese)

Multi-box container loading problem based on hybrid genetic algorithm

doi: 10.13700/j.bh.1001-5965.2020.0665
Funds:

National Natural Science Foundation of China 51707195

the Fundamental Research Funds for the Central Universities 3122016A009

More Information
  • Corresponding author: ZHANG Changyong, E-mail: cyzhang@cauc.edu.cn
  • Received Date: 30 Nov 2020
  • Accepted Date: 24 Dec 2020
  • Publish Date: 20 May 2022
  • Aviation multi-box container loading is an important link to realize fast, efficient and safe air cargo transportation. Aimed at the multi-box container loading optimization problem of multiple cargoes and container types under realistic constraints, a mathematical optimization model is built and a hybrid genetic algorithm is proposed to solve the cargo loading layout scheme, so as to make full use of the container loading space. The three-stage code is used to determine the loading order, cargo placement status and container number to generate the initial population randomly. The optimal solution protection strategy is added in the conventional selection operation, and the center of gravity, non-overlapping and load-bearing constraints are taken into account in the fitness function to evaluate the solution. The simulated annealing operator is added to avoid falling into the local optimum by using its jump property, which further improves the optimization effect. Through the comparison of examples, it shows that the proposed algorithm can still maintain a high volume utilization rate under various constraints, and can well solve the loading of strong and weak heterogeneous cargoes. The feasibility and applicability of the algorithm are further verified by using specific cargo data. The average volume utilization rate of four kinds of air container is higher than 82%, which shows that the proposed algorithm can effectively solve the cargo loading problem of regular and irregular multi-box containers, and has good engineering application value.

     

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