Design and Optimization of Underground Logistics Transportation Networks

Mu Ren,Zhiping Du,Li Zhou,Ziwei Fan,Jianjun Wu

doi:10.1109/access.2019.2924438

Abstract

With the burden of city transportation system becoming bigger and bigger, it is imperative to develop reliable and efficient underground logistics. The appropriate location of cargo transshipment centers in underground logistics system is selected using the Set Covering Problem Model, Weighted Set Covering Problem Model, and the reasonable prediction of the freight volume data of major cities to ensure the maximum numbers of service nodes are covered by the least transshipment centers within a reasonable range. The timing of the construction of facilities in the system is proposed, considering the construction cost and cost recovery period of the underground logistics system. The design and optimization plan of the urban underground logistics system, based on the above, is given to achieve the purpose of relieving urban traffic congestion and increasing freight volume.

Highlights

With the rapid growth of urbanization and the continuous improvement of people’s living standards, the freight volume of many developed cities has risen rapidly
This shows that the main cause of urban traffic congestion is the rapid increase in the number of vehicles and trains on the ground brought about by the surge in traffic demand, and the increase in demand for goods logistics is the reason in part
We develop the design of the underground logistics system based on the coordinates of each business node and the corresponding freight volume Origin-Destination matrix (OD matrix)

Summary

INTRODUCTION

With the rapid growth of urbanization and the continuous improvement of people’s living standards, the freight volume of many developed cities has risen rapidly. M. Ren et al.: Design and Optimization of Underground Logistics Transportation Networks be feasible, can urban traffic pressure be effectively alleviated, and has advantages in reducing urban pollution [1] and improving transportation efficiency. The scholars mostly used traditional simulation algorithms to solve the optimization problem of underground logistics networks, but most of them discuss the characteristics of ULS systems out of specific urban characteristics. They do not carry out case studies and design solutions in light of the actual situation of ULS. There are many ways to handle key positions, and the more common standardized processing method is to use latitude and longitude data or its variant form data

SORTING OF FREIGHT VOLUME DATA

OPTIMIZING THE LOCATION OF NODES IN LEVEL-2

Findings

CONCLUSION