Cluster Computing

, Volume 22, Supplement 1, pp 1443–1458 | Cite as

Research on coordinated scheduling of multiple systems and single jobs based on improved particle swarm optimization

  • Dingyou Lei
  • Peng ZhangEmail author
  • Yinggui ZhangEmail author
  • Yangkun Xia
  • Shuo Zhao


Railway container yard is an important node in container transportation system, plays a very important role in the global logistics integration, improve the railway container freight yard handling equipment operation scheduling level, speed up the internal connection efficiency, reasonable co-ordination of container truck railway container freight yard handling equipment resources configuration can significantly improve the overall efficiency of railway container freight yard, reduce comprehensive operation cost. This article has carried on the research from the different aspect and the application angle separately: firstly, multi system for unloading the single operation coordination scheduling problem, in order to improve the efficiency of container handling equipment, reduce the waiting time of the container class column, the key equipment of different types (yard crane, container truck, internal to the hair line Longmen crane) work plan and consider the coordination of scheduling, considering the control the mutual influence between different handling equipment, build a multi system operation coordination scheduling model, the optimization results obtained through calculation by using the improved particle swarm algorithm; secondly, the dual task coordinated scheduling problem of multi system for unloading and loading operations, in the work cycle range, considering the hair line gantry crane and yard crane loading and unloading order, internal container truck transport order, we constructed a multi system coordination operation The mathematical model of scheduling is used to realize the optimization of multiple systems. The improved particle swarm algorithm is used to obtain the optimization results through an example, and the relevant conclusions are obtained by analysis.


Railway container yard Multi system coordination scheduling Particle swarm optimization Optimization operator Improved particle swarm optimization (PSO) algorithm 



The work described in this paper was supported by grants from National Natural Science Foundation of China (Nos. 71501190 and 71771218).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Traffic & Transport EngineeringCentral South UniversityChangshaChina

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