A study on car flow organization in the loading end of heavy haul railway based on immune clonal selection algorithm

• Published in: Neural computing & applications Vol. 31; no. 5; pp. 1455 - 1465
• Main Authors: Yun Jing, Zhang, Zhenhua
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 01.01.2019
• Subjects: Affinity; Algorithms; Antibodies; Antigens; Computer simulation; Entropy (Information theory); Mutation; Optimization; Railroad cars
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3396-2

Both empty and heavy car flows should be taken into consideration in the study of the Optimization Model of Car Flow Organization, in the Loading End of Heavy Haul Railway. Firstly, through the analysis of empty and heavy car flow organization in the semi-closed heavy haul railway loading end, comprehensive optimization model based on ability of station and railway can be established in order to obtain car flow organization scheme with minimum residence time of both empty and heavy cars in the loading end. Secondly, immune clone algorithm is applied to solve the model in which the objective function is regarded as antibody and the constrained condition is considered as antigen to calculate the affinity, further to conduct the mutation, based on information entropy. Mutation is conducted according to the affinity. In the solving process, reproduction can be realized with the use of immune clone selection algorithm. Besides, premature convergence can be prevented by using the antibody concentration which can control the population size. Finally, simulation results show that the average search time of the proposed algorithm is reduced by 36 and 51% respectively compared with the GA and PSO.