Low-Carbon Economy in Urban Solid Waste Logistics Management

Abstract

With the development of scientific research and engineering practice, the amount of refractory waste is increasing gradually, and algorithms for the logistics management of waste have gained attention. Because there are a lot of obstacles in the city, solid waste will increase the field distance during transportation, thus producing a lot of Oxocarbon, which will promote the greenhouse effect. In order to reduce carbon production in the process of urban solid waste logistics management, this research improves the brainstorming algorithm based on low-carbon economy and discussion. The study first proposes a large number of different ideas based on the low-carbon economy. Then, through fitness comparison, the idea of clustering centers is further optimized. For similar ideas that arise during the thinking process, research has added mutation factors to facilitate ideas jumping out of local optima. Finally, the proposed fusion algorithm will be tested on the Logis dataset to calculate the carbon emissions of solid waste transportation devices within a day and compared with three models, such as brainstorming based on discussion. The motion distances of the four model design paths are 22 km, 23 km, 26 km, and 27 km, respectively, with corresponding carbon emissions of 3.9 t, 6.0 t, 6.8 t, and 8.9 t, respectively. The experimental results showed that the proposed model not only had the shortest design path but also had the lowest carbon emissions, which was in line with the current low-carbon economy environment. The application of this model in the rational transportation of urban solid waste will be useful. It is a novelty to integrate the idea of a low-carbon economy into the DMBSO algorithm, which can effectively deal with the carbon growth of the environment.

Keywords

• low carbon economy
• brainstorming optimization algorithm
• the best idea
• variation factor
• solid waste transportation