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Issue title: Special Section: Intelligent Data Aggregation Inspired Paradigm and Approaches in IoT Applications
Guest editors: Xiaohui Yuan and Mohamed Elhoseny
Article type: Research Article
Authors: Jing, Yun; * | Guo, Siye | Zhang, Zhenhua | Jeng, Embrima
Affiliations: School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
Correspondence: [*] Corresponding author. Yun Jing, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China. E-mail: [email protected].
Abstract: The propose of this paper is building the flow shop model with full-loaded constraints and maximum wagons within the stage. Based on sequence theory, technical operations of marshalling station are described as process of flow shop. By definition of key trains, the adjustment of the classification schedule of inbound trains is attributed to the adjustment of key trains for reducing invalid solution. Based on the LS rules to construct an initial solution, tabu search algorithm (TS) dynamically adjusts taboo step, and build a network model of static wagon-flow allocation for the objective function, to ensure the feasibility of solutions. Finally, the example demonstrates the effectiveness of the algorithm, and differences between full-loaded and full-cars constrains.
Keywords: Marshaling station, wagon-flow allocating, sequence theory, taboo search, full-loaded
DOI: 10.3233/JIFS-179106
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 527-536, 2019
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