The Order Allocation Problem and the Algorithm of Network Freight Platform under the Constraint of Carbon Tax Policy

Int J Environ Res Public Health. 2022 Sep 2;19(17):10993. doi: 10.3390/ijerph191710993.

Abstract

In order to solve the problems of improper order allocation and the lack of a carbon emission constraint system in the road freight transportation industry, this paper proposed an order allocation mechanism of network freight transportation with carbon tax constraints and established an order allocation optimization model with carbon tax constraints. Based on the basic characteristics of the problem, this paper redesigns the ant colony labor division expansion model, and designs a corresponding algorithm to solve the problem. By improving the update rules of the stimulus value and the threshold value, the matching difference between the order and the driver of the network freight platform is enlarged, and the matching relation-ship is dynamically adjusted, the order allocation scheme is optimized, and a more appropriate carbon tax rate range in this industry is explored. Furthermore, the problem is solved by a 0-1 integer programming algorithm, which is compared with the algorithm designed in this paper. Through multiple numerical simulation experiments, the effectiveness and feasibility of the algorithm are verified. The experimental results show that the order allocation arrangement of the online freight platform with carbon tax constraints is more economical and environmentally friendly.

Keywords: carbon tax; division of labor; division of labor in ant colonies; online freight; order distribution.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Carbon* / analysis
  • Industry
  • Policy
  • Transportation

Substances

  • Carbon

Grants and funding

This research was funded by the Ministry of Education of Humanities and Social Science Project of China, grant number 19YJA630040, and the Modern Business Research Center of Zhejiang Gongshang University, grant number 2021SWB013Z.