Research on Multilevel Network Optimization of Urban Underground Logistics in Megaloplis

With the continuous advancement of urbanization in China, the urban population and the number of vehicles continue to increase. Air pollution and traffic congestion resulting from bulk cargo transportation have become significant obstacles to improving the living conditions of residents and urban de...

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Bibliographic Details
Published in:Ji suan ji gong cheng Vol. 49; no. 12; pp. 311 - 320
Main Author: Boyu LIU, Chengji LIANG, Yu WANG
Format: Journal Article
Language:Chinese
English
Published: Editorial Office of Computer Engineering 15.12.2023
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ISSN:1000-3428
Online Access:Get full text
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Summary:With the continuous advancement of urbanization in China, the urban population and the number of vehicles continue to increase. Air pollution and traffic congestion resulting from bulk cargo transportation have become significant obstacles to improving the living conditions of residents and urban development.To relieve the current shortage of road transportation resources in megalopolises, a multilevel underground logistics network combining deep tunnel transportation and shallow pipebag channel transportation is proposed. Part of the cargo fluence to was transferred underground to release the transport capacity. An integer programming model for the optimal design of the multilevel underground logistics network is formulated from the perspectives of cost and resource utilization efficiency.Based on the characteristics of the problem and the multilevel structural features of underground transportation, a two-layer heuristic algorithm based on simulated annealing and an immune algorithm is designed as follows.First, the solution space is decomposed using mean-shift clustering to remove unreasonable decisions.Subsequently, the two-layer heuristic algorithm is used to optimize the decision-making of node locations and traffic allocation.To minimize total construction and operating costs, a satisfactory underground logistics multilayer network planning scheme can be obtained through multiple iterations.The numerical experiment results and a case study demonstrate that the proposed algorithm outperforms the traditional genetic algorithm. It improves solution optimization by 2% to 7% for node layout and network planning problems while reducing computation time by approximately 50%, further validating the proposed model.
ISSN:1000-3428
DOI:10.19678/j.issn.1000-3428.0066023