Dynamic feedback algorithm based on spatial corner fitness for solving the three-dimensional multiple bin-size bin packing problem

To improve cargo loading efficiency and achieve diverse needs of companies for the loading process, this paper innovatively establishes a multiple objective mixed integer programming model for the three-dimensional multiple bin-size bin packing problem (3D-MBSBPP). The model is designed to maximize...

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Vydáno v:Complex & intelligent systems Ročník 10; číslo 3; s. 4055 - 4081
Hlavní autoři: Liu, Yi, Jiang, Xiaoyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.06.2024
Springer Nature B.V
Springer
Témata:
Ablation
Algorithms
Cargo
Cargo handling
Complexity
Computational Intelligence
Containers
Data Structures and Information Theory
Dynamic feedback algorithm
Effectiveness
Engineering
Feedback
Fitness
Genetic algorithms
Heuristic methods
Improved genetic algorithm
Integer programming
Mixed integer
Multiple objective mixed integer programming model
Original Article
Packing problem
Spatial corner fitness assessment strategy
Three-dimensional multiple bin-size bin packing problem
Three-dimensional multiple bin-size bin packing problem
Improved genetic algorithm
Dynamic feedback algorithm
Multiple objective mixed integer programming model
Spatial corner fitness assessment strategy
ISSN:2199-4536, 2198-6053
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Shrnutí:To improve cargo loading efficiency and achieve diverse needs of companies for the loading process, this paper innovatively establishes a multiple objective mixed integer programming model for the three-dimensional multiple bin-size bin packing problem (3D-MBSBPP). The model is designed to maximize container space utilization rate and cargo load balance, minimize container usage costs, and incorporates some practical constraints. On this basis, we propose a novel dynamic feedback algorithm based on spatial corner fitness (SCF_DFA) to solve this model, which consists of three stages. Specifically, Stage 1 employs a heuristic algorithm based on spatial corner fitness to optimize the search of the remaining spaces. Stage 2 employs a container type selection algorithm to dynamically adjust and optimize container types. Stage 3 uses an improved genetic algorithm to improve the quality of the solutions of the first two stages. We demonstrate the effectiveness of the proposed algorithm through comparative experiments on benchmark instances, and apply it to solve the real-life instances for the 3D-MBSBPP. The results show that the proposed algorithm can make the average container space utilization rate reach 85.38%, which is 1.48% higher than that of baseline method, while the loading results obtained are more balanced, indicating the advantages of the SCF_DFA in solving 3D-MBSBPP. Furthermore, we conduct ablation experiments to confirm the effectiveness of each component within the algorithm.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-024-01368-5