Abstract
Hub location problems are widely analyzed in fields of logistic and transportation industry for cost reduction. In this paper, a novel algorithm framework based on machine learning is proposed to improve solution quality of hub location problems for large-scale instances. First, a deep-learning based probabilistic hub-ranker (DLHr) is developed to determine the priority of nodes to be selected as hubs. Next, two node-ranking based approaches DL-CBS and DL-GVNS are developed to augment the DLHr for single allocation hub location problems. DL-CBS is an augment algorithm embedding DLHr-ranking into clustering-based potential hub sets algorithm (CBS) while DL-GVNS embeds DLHr-ranking into general variable neighborhood search (GVNS). The numerical results evidence that DLHr outperforms baselines on the node-ranking task and helps to identify potential hubs. Evaluation on a wide range of experiments shows that DL-CBS and DL-GVNS improve solution quality of single allocation hub location problems compared with vanilla CBS and GVNS, revealing DLHr ranking helps to boost the performance of traditional heuristics.
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