Abstract This paper presents a model to the facility location problem which incorporates both location characteristics and rival effects within a business cluster. Location characteristics in the model are predefined selection criteria such as rent, distance, safety and size. Rival effects represent the competition and collaboration relationship among close facility locations in the business cluster where candidate locations are located. The model presented uses data envelopment analysis (DEA) and weighted hyperlink-induced topic search (HITS) algorithm. DEA determines the efficient and inefficient locations as well as benchmarking relationship. The weighted HITS algorithm with the distance parameter considers the rival effects among locations to identify hubs and authorities. The applicability of our proposed model is demonstrated with a case study that rank apartments in the University area.
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A HITS-based model for facility location decision
Semantic Scholar · Business · 2020
Abstract
Abstract This paper presents a model to the facility location problem which incorporates both location characteristics and rival effects within a business cluster. Location characteristics in the model are predefined selection criteria such as rent, distance, safety and size. Rival effects represent the competition and collaboration relationship among close facility locations in the business cluster where candidate locations are located. The model presented uses data envelopment analysis (DEA) and weighted hyperlink-induced topic search (HITS) algorithm. DEA determines the efficient and inefficient locations as well as benchmarking relationship. The weighted HITS algorithm with the distance parameter considers the rival effects among locations to identify hubs and authorities. The applicability of our proposed model is demonstrated with a case study that rank apartments in the University area.