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Clustering-Based Column Generation and Heuristic Methods for the Container Loading Problem with Practical Constraints: A Case Study.

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Title: Clustering-Based Column Generation and Heuristic Methods for the Container Loading Problem with Practical Constraints: A Case Study.
Authors: Tekil-Ergün, Sezgi1,2 tekil17@itu.edu.tr, Çebi, Ferhan1 cebife@itu.edu.tr
Source: Journal of Industrial Engineering & Management. 2026, Vol. 19 Issue 1, p99-119. 21p.
Subjects: K-means clustering, Column generation (Algorithms), Heuristic, Clustering algorithms, Machine learning, Bin packing problem, Logistics
Geographic Terms: Turkey
Abstract: Purpose: This study addresses a real-world container loading problem (CLP) encountered in a logistics company in Turkey, filling a gap in the literature by solving practical constraints using a state-of-the-art algorithm. The problem involves constraints such as rotations, stackability, loading priorities, and mixed loading constraints. Design/methodology/approach: To overcome the computational challenges posed by large-scale instances, a novel three-step approach is proposed. First, the K-Means clustering algorithm is applied to group objects with similar dimensions. Then, each group is allocated to containers using a Column Generation (CG) method combined with a 3D-Best Fit Decreasing with Orientation (3D-BFDO) algorithm. Additionally, the CG process is enhanced by integrating a machine learning (ML) model to predict reduced-cost columns, improving computational efficiency and solution quality. Findings: Extensive experiments demonstrate that the proposed approach significantly improves container space utilization while reducing operational costs. The results highlight the effectiveness of ML and K-Means in enhancing traditional optimization techniques. Research limitations/implications: The study focuses on a specific set of practical constraints relevant to real-world logistics applications. Further research could explore additional constraints and scalability to different logistics environments. Practical implications: The approach offers a practical solution for logistics companies dealing with a large-scale CLP by optimizing space utilization and reducing operational costs. The integration of ML into CG presents a viable method for improving decision-making in logistics. Originality/value: The study bridges the gap between theoretical models and real-world logistics challenges by introducing a data-driven enhancement to traditional optimization techniques. The proposed integration of K-Means clustering and ML into CG represents an innovative contribution to container loading optimization. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Industrial Engineering & Management is the property of Omnia Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Purpose: This study addresses a real-world container loading problem (CLP) encountered in a logistics company in Turkey, filling a gap in the literature by solving practical constraints using a state-of-the-art algorithm. The problem involves constraints such as rotations, stackability, loading priorities, and mixed loading constraints. Design/methodology/approach: To overcome the computational challenges posed by large-scale instances, a novel three-step approach is proposed. First, the K-Means clustering algorithm is applied to group objects with similar dimensions. Then, each group is allocated to containers using a Column Generation (CG) method combined with a 3D-Best Fit Decreasing with Orientation (3D-BFDO) algorithm. Additionally, the CG process is enhanced by integrating a machine learning (ML) model to predict reduced-cost columns, improving computational efficiency and solution quality. Findings: Extensive experiments demonstrate that the proposed approach significantly improves container space utilization while reducing operational costs. The results highlight the effectiveness of ML and K-Means in enhancing traditional optimization techniques. Research limitations/implications: The study focuses on a specific set of practical constraints relevant to real-world logistics applications. Further research could explore additional constraints and scalability to different logistics environments. Practical implications: The approach offers a practical solution for logistics companies dealing with a large-scale CLP by optimizing space utilization and reducing operational costs. The integration of ML into CG presents a viable method for improving decision-making in logistics. Originality/value: The study bridges the gap between theoretical models and real-world logistics challenges by introducing a data-driven enhancement to traditional optimization techniques. The proposed integration of K-Means clustering and ML into CG represents an innovative contribution to container loading optimization. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Industrial Engineering & Management is the property of Omnia Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3926/jiem.8741
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        PageCount: 21
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      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Column generation (Algorithms)
        Type: general
      – SubjectFull: Heuristic
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      – SubjectFull: Clustering algorithms
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Bin packing problem
        Type: general
      – SubjectFull: Logistics
        Type: general
      – SubjectFull: Turkey
        Type: general
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      – TitleFull: Clustering-Based Column Generation and Heuristic Methods for the Container Loading Problem with Practical Constraints: A Case Study.
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            NameFull: Tekil-Ergün, Sezgi
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            – D: 01
              M: 01
              Text: 2026
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              Y: 2026
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