4.7 Article

A hybrid adaptive large neighborhood search algorithm for the large-scale heterogeneous container loading problem

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 189, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2021.115909

关键词

Container loading problem; Hybrid adaptive large neighborhood search; Heuristic packing algorithm

资金

  1. Funds for International Cooperation and Exchange of the National Natural Science Foundation of China [M-0310]
  2. Science Fund for Creative Research Groups of the National Natural Science Foundation of China [72021002]

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This paper aims to solve the large-scale heterogeneous container loading problem with a hybrid adaptive large neighborhood search algorithm. Computational experiments show that the proposed algorithm outperforms other algorithms for the HCLP.
This paper aims to solve the large-scale heterogeneous container loading problem (HCLP), which is an extensive form of the multiple container loading problem, in a limited time. The target is to choose a set of containers of different sizes to accommodate all products and minimize the wasted space rate. Although the heterogeneous container selection problem is a general problem in the logistics industry, few related studies have been conducted. This study also considers some practical constraints, such as weight limits and suspension constraints. A hybrid adaptive large neighborhood search (HALNS) algorithm, which includes a set of original destroy-repair operators, especially for heterogeneous container selection problems, and integrates a heuristic packing algorithm, is proposed to solve the problem in an acceptable time. To verify the efficiency of the proposed algorithm, computational experiments on real-world instances from a multinational logistics company are performed, and the results are compared with those of other existing algorithms. The results indicate that the proposed algorithm outperforms other algorithms for the HCLP.

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