Leveraging Large Language Models for the Bin Packing Problem
Published in Proceedings of the Korean Institute of Information Scientists and Engineers (KIISE) Conference, 2025
Abstract. Despite being introduced in the last century, the bin packing problem (BPP) remains one of the most studied topics in optimization due to its wide applicability to real-world problems. The most essential goal of the problem is to enable more objects to be packed into bins, while using the fewest bins possible. Computationally, the problem is NP-hard, meaning there is no known polynomial-time algorithm to find the optimal solution for large input sizes. Despite numerous heuristic and meta-heuristic algorithms proposed for the BPP, their performance remains below expectations. Therefore, in this paper, we leverage a pre-trained large language model to find the best possible solution for the BPP. Finally, we provide experiments on a randomly generated dataset to illustrate the performance gain compared with the existing heuristic algorithms.
Recommended citation: Dat K Nguyen, Phuong-Nam Tran, Trung Thanh Pham, Loc X Nguyen, Choong Seon Hong. (2025). "Leveraging Large Language Models for the Bin Packing Problem." Proceedings of the Korean Institute of Information Scientists and Engineers (KIISE) Conference, 399-401.
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