Neural Computing and Applications

, Volume 31, Supplement 1, pp 35–46 | Cite as

A multi-objective location and channel model for ULS network

  • Hejun LiangEmail author
  • Guanghui Yuan
  • Jingti Han
  • Lily Sun
S.I. : Machine Learning Applications for Self-Organized Wireless Networks


In this paper, we construct a multi-objective location and channel model for ULS network to alleviate the urban traffic congestion problem. First, we construct a multi-objective node selection model and obtain the position of the first-level nodes in the ULS network by using agglomerative hierarchical clustering method. Then, we obtain the position of the second-level nodes in the ULS network by using the greedy algorithm. We also calculate the service scope, actual traffic volume and transport rate of first-level node for each node based on the determined node group. After that, we select the optimal channel scheme to make nodes at different levels of the ULS and its load more balanced by using the plant growth simulation algorithm. We extract the key variables of the problem, quantify some indicators with reasonable quantification, construct a multi-objective location and channel model based on the actual logistics situation for the specific region and achieve reasonable results to meet multiple objectives. Therefore, the model could be used as a reference for the construction of urban ULS network.


Logistics engineering Underground logistics system (ULS) Location and channel model Multi-objective Plant growth simulation algorithm (PGSA) 


Compliance with ethical standards

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Central Asia Research Centre for Cloud ComputingShanghai University of Finance and EconomicsShanghaiChina
  2. 2.Department of Computer ScienceUniversity of ReadingReadingUK

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