Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm

From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for...

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Veröffentlicht in:IEEE access Jg. 8; S. 92615 - 92626
Hauptverfasser: Li, Zhichao, Li, Yilin, Lu, Wanchun, Huang, Jilin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Business
Clustering
Clustering algorithms
Crowdsourcing
Crowdsourcing logistics
DBSCAN clustering algorithm
Domains
Economic models
Logistics
Mathematical analysis
Optimization models
Outliers (statistics)
Polynomials
Pricing
pricing optimization model
Regression analysis
Regression models
Sustainable development
Task analysis
Task complexity
ISSN:2169-3536, 2169-3536
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Abstract From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for logistics enterprises, and reasonable distribution pricing is the key to achieving sustainable development. This paper innovatively adds dynamic and decentralized characteristics of logistics on the basis of a detailed analysis of pricing methods and uses this as a basis to build a pricing model. First, based on existing crowdsourced photography task pricing data, this paper establishes a project-centric domain and builds metrics into the attributes of each project based on the data in that domain. Then, a regression model is used to fit the completion rate of previous projects, and a multiple linear regression and optimal pricing mechanism are established. Finally, the DBSCAN algorithm is used to cluster areas with a high project density, and a pricing optimization model based on polynomial Logit (MNL) is established. We found through the model analysis that the optimized pricing strategy of crowdsourcing logistics services has a better packaging completion rate based on a combination of complex factors including bundling and outliers. In short, the main contributions of this paper are to build a complex mathematical model for crowdsourcing tasks, improve the algorithmic deficiencies of the previous crowdsourcing task pricing methods, and provide a reference for further research on crowdsourcing tasks.
AbstractList From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for logistics enterprises, and reasonable distribution pricing is the key to achieving sustainable development. This paper innovatively adds dynamic and decentralized characteristics of logistics on the basis of a detailed analysis of pricing methods and uses this as a basis to build a pricing model. First, based on existing crowdsourced photography task pricing data, this paper establishes a project-centric domain and builds metrics into the attributes of each project based on the data in that domain. Then, a regression model is used to fit the completion rate of previous projects, and a multiple linear regression and optimal pricing mechanism are established. Finally, the DBSCAN algorithm is used to cluster areas with a high project density, and a pricing optimization model based on polynomial Logit (MNL) is established. We found through the model analysis that the optimized pricing strategy of crowdsourcing logistics services has a better packaging completion rate based on a combination of complex factors including bundling and outliers. In short, the main contributions of this paper are to build a complex mathematical model for crowdsourcing tasks, improve the algorithmic deficiencies of the previous crowdsourcing task pricing methods, and provide a reference for further research on crowdsourcing tasks.
Author Li, Zhichao
Li, Yilin
Lu, Wanchun
Huang, Jilin
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  givenname: Jilin
  surname: Huang
  fullname: Huang, Jilin
  email: jilinhuang@pku.edu.cn
  organization: Department of Environment Science, College of Environmental Sciences and Engineering, Peking University, Beijing, China
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SubjectTerms Algorithms
Business
Clustering
Clustering algorithms
Crowdsourcing
Crowdsourcing logistics
DBSCAN clustering algorithm
Domains
Economic models
Logistics
Mathematical analysis
Optimization models
Outliers (statistics)
Polynomials
Pricing
pricing optimization model
Regression analysis
Regression models
Sustainable development
Task analysis
Task complexity
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Title Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm
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