Research on Delivery Network Optimization Based on Crowdsourcing Theory-Reference-Cited by-同舟云学术

Research on Delivery Network Optimization Based on Crowdsourcing Theory

Published:2022-03-20 Issue:2 Volume:26 Page:147-159
ISSN:1883-8014
Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
language:en
Short-container-title:JACIII

Author:

Li Jiacheng,Noto Masato,Zhang Yang, ,

Abstract

Under a new wave of technological revolution and industrial change, Internet plus has penetrated into every aspect of production and life. For the traditional takeout industry, the new crowdsourcing distribution mode based on o2o (online to offline) provides new ideas for distribution but also leads to great challenges. In view of the existing development problems and transformation needs of the delivery network, this paper explores the delivery mechanism of delivery orders in crowdsourcing mode, focusing on the delivery path and work efficiency. To optimize the delivery network, taking the shortest delivery path and the least time delay as objectives, this paper establishes a crowdsourcing delivery path optimization model with a time window and includes an example application model. The results show that the model can solve the delivery problem in crowdsourcing mode.

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

Reference31 articles.

1. Y. Chen, “Location and Pricing of Terminal Distribution Based on Customer Choice Equilibrium,” Doctral Thesis, Southwest Jiaotong University, 2018 (in Chinese).

2. Y. Ding, “Study on Delivery Mode Selection of MeiTuan Takeout,” Master Thesis, Nanjing University, 2015 (in Chinese).

3. J. Howe, “The Rise of Crowd Sourcing,” Wired, Vol.149, No.6, pp. 176-183, 2006.

4. D. K. Tse and P. C. Wilton, “Models of Consumer Satisfaction Formation: An Extension,” J. of Marketing Research, Vol.25, No.2, pp. 204-212, 1988.

5. O. Toubia, “Idea Generation, Creativity, and Incentives,” Marketing Science, Vol.25, No.5, pp. 411-425, 2006.