A Freight Train Optimized Scheduling Scheme Based on an Improved GJO Algorithm
Source abstract
Abstract
With the advancement of China’s industrialization, demand for express freight transportation has been rising. However, high-speed rail freight faces challenges, such as relatively low transport efficiency and lower revenues, compared with air and road modes. To address these issues, this paper focuses on freight train operations. First, it analyzes key influencing factors, including operating costs and benefits. Next, it conducts a comprehensive assessment of train consist capacity, freight node capacity, transport demand, and the number of freight services, and formulates an operational planning model that maximizes rail revenue, minimizes intermediate stops, and satisfies freight demand. Finally, an Improved Golden Jackal Optimization–based Genetic Algorithm (IGJOGA) is proposed to solve the model. Simulation results indicate that IGJOGA achieves higher solution efficiency than a traditional genetic algorithm for the freight train operation planning problem, and the results can provide a practical reference for freight train set operation schemes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle Routing Optimization Methods · Advanced Manufacturing and Logistics Optimization · Maritime Ports and Logistics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Funding
Key Projects of National Energy Investment Group Co., Ltd.
References (26)
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