Pub Date : 2018-08-01DOI: 10.1109/LISS.2018.8593262
Xuedong Gao, Ai Wang
Human naturally analyze and decide a problem from different perspectives, hierarchies, and dimensions, that is referred to as scale transformation (ST). Clustering, as one of the most effective data analysis tools, should support this ST demand. Hence, this paper focuses on the ST problem among clustering analysis especially for decision making. We define the variable-scale dataset based on the rough set theory. What’s more, an algorithm of variable-scale clustering (VSC) is also proposed. A case study shows that compared to the k-modes, the clustering results of the VSC are more available and accessible to decision makers.
{"title":"Variable-Scale Clustering","authors":"Xuedong Gao, Ai Wang","doi":"10.1109/LISS.2018.8593262","DOIUrl":"https://doi.org/10.1109/LISS.2018.8593262","url":null,"abstract":"Human naturally analyze and decide a problem from different perspectives, hierarchies, and dimensions, that is referred to as scale transformation (ST). Clustering, as one of the most effective data analysis tools, should support this ST demand. Hence, this paper focuses on the ST problem among clustering analysis especially for decision making. We define the variable-scale dataset based on the rough set theory. What’s more, an algorithm of variable-scale clustering (VSC) is also proposed. A case study shows that compared to the k-modes, the clustering results of the VSC are more available and accessible to decision makers.","PeriodicalId":338998,"journal":{"name":"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131947167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-01DOI: 10.1109/LISS.2018.8593273
Yiheng Zheng, Yisong Li
With the increasing development of mobile technology and popularity of ‘New Retail’ concept, more and more venture capitalists have been chasing investment opportunities in the unmanned retail business, such as F5 future store, Xiaomai, Xingbianli, CityBox, also and e-commerce firms Alibaba Group Holdings and JD.com. Currently the main forms of unmanned retail are smart vending machines, unmanned convenience stores, and unmanned smart shelves in offices. Additionally, other concepts, such as unmanned cafes and bookstores, continue to emerge. These forms have a great advantage: with the help of new technologies, the cost of unmanned retail is much lower than that of traditional retail forms. However, how to formulate an effective replenishment and distribution strategy is the biggest operational problem, also is the main research question of this paper. Firstly, this paper forecasts the purchase demand of each node based on the analysis of the nodes’ historical sales data, and then according to the demand forecast results, optimizes the distribution routes to effectively reduce transportation costs and achieve supply chain optimization.
{"title":"Unmanned Retail’s Distribution Strategy Based on Sales Forecasting","authors":"Yiheng Zheng, Yisong Li","doi":"10.1109/LISS.2018.8593273","DOIUrl":"https://doi.org/10.1109/LISS.2018.8593273","url":null,"abstract":"With the increasing development of mobile technology and popularity of ‘New Retail’ concept, more and more venture capitalists have been chasing investment opportunities in the unmanned retail business, such as F5 future store, Xiaomai, Xingbianli, CityBox, also and e-commerce firms Alibaba Group Holdings and JD.com. Currently the main forms of unmanned retail are smart vending machines, unmanned convenience stores, and unmanned smart shelves in offices. Additionally, other concepts, such as unmanned cafes and bookstores, continue to emerge. These forms have a great advantage: with the help of new technologies, the cost of unmanned retail is much lower than that of traditional retail forms. However, how to formulate an effective replenishment and distribution strategy is the biggest operational problem, also is the main research question of this paper. Firstly, this paper forecasts the purchase demand of each node based on the analysis of the nodes’ historical sales data, and then according to the demand forecast results, optimizes the distribution routes to effectively reduce transportation costs and achieve supply chain optimization.","PeriodicalId":338998,"journal":{"name":"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129927961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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