Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221200091-10.doi: 10.11896/jsjkx.221200091

• Artificial Intelligence • Previous Articles     Next Articles

Improved Metaheuristics for Single Container Loading Problem with Complex Constraints

LIU Rixin, QIN Wei, XU Hongwei   

  1. Department of Industrial Engineering and Management,School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China
  • Published:2023-11-09
  • About author:LIU Rixin,born in 1996,postgraduate,is a member of China Computer Federation.His main research interests include operation research and intelligent manufacture.
    QIN Wei,born in 1982,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include complex system modeling and control and optimization.
  • Supported by:
    National Key Research and Development Program of China(2019YFB1704401).

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Abstract: Three-dimensional single container loading problem(3D-SCLP) has become one of the most classic engineering problems in the field of optimization because of its wide application in manufacturing and logistics.However,the current optimization scheme mainly considers the optimization and improvement of algorithm and local constraints,but fails to fully consider the actual complex constraints,such as weight limit,load balance,cargo stability,stacking constraints and human factors,which leads to the problem that the theoretical loading rate of the existing methods is high,but the practicality is low.In order to solve this problem,this paper proposes an improved meta-heuristic algorithm based on the Aquila optimizer on the basis of fully considering the complex constraints of multiple realities.It is based on population optimization strategy,and combines differential mutation and Gaussian disturbance with potential point strategy to achieve rapid convergence under complex constraints,and it is verified on the data of a medium-scale industrial example.Compared with the traditional heuristic optimization method,the proposed method can solve the three-dimensional packing optimization problem under complex constraints,and is superior to the existing solutions in terms of actual space utilization and generation efficiency,thus embodies intelligent packing,realizes standardization and intelligence of packing,and reduces manual participation.

Key words: Single container loading problem, Complex constraints, Aquila Optimizer, Gaussian disturbance, Differential mutation

CLC Number: 

  • TP301
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