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Warehouse Vis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts
Author(s) -
Li Q.,
Liu Q. Q.,
Tang C. F.,
Li Z. W.,
Wei S. C.,
Peng X. R.,
Zheng M. H.,
Chen T. J.,
Yang Q.
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13996
Subject(s) - computer science , data warehouse , warehouse , analytics , selection (genetic algorithm) , online analytical processing , process (computing) , business analytics , dimensional modeling , data science , database , business model , business , business analysis , artificial intelligence , marketing , operating system
Selecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision‐making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely‐used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within‐subject study with baseline systems to assess the efficacy of our system. Experts ‘feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.