Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916486774259712 |
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| author | Carocci, Luca Silvano Han, Qiwei |
| author_facet | Carocci, Luca Silvano Han, Qiwei |
| contents | This study presents a novel application of Graph Neural Networks (GNNs) to optimize dealership network planning for a luxury car manufacturer in the U.S. By conducting a comprehensive literature review on dealership location determinants, the study identifies 65 county-level explanatory variables, augmented by two additional measures of regional interconnectedness derived from social and mobility data. An ablation study involving 34 variable combinations and ten state-of-the-art GNN operators reveals key insights into the predictive power of various variables, particularly highlighting the significance of competition, demographic factors, and mobility patterns in influencing dealership location decisions. The analysis pinpoints seven specific counties as promising targets for network expansion. This research not only illustrates the effectiveness of GNNs in solving complex geospatial decision-making problems but also provides actionable recommendations and valuable methodological insights for industry practitioners. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection Carocci, Luca Silvano Han, Qiwei Machine Learning Social and Information Networks This study presents a novel application of Graph Neural Networks (GNNs) to optimize dealership network planning for a luxury car manufacturer in the U.S. By conducting a comprehensive literature review on dealership location determinants, the study identifies 65 county-level explanatory variables, augmented by two additional measures of regional interconnectedness derived from social and mobility data. An ablation study involving 34 variable combinations and ten state-of-the-art GNN operators reveals key insights into the predictive power of various variables, particularly highlighting the significance of competition, demographic factors, and mobility patterns in influencing dealership location decisions. The analysis pinpoints seven specific counties as promising targets for network expansion. This research not only illustrates the effectiveness of GNNs in solving complex geospatial decision-making problems but also provides actionable recommendations and valuable methodological insights for industry practitioners. |
| title | Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2408.13961 |