Optimising railcar transfer chain via fuzzy programming and a simulated annealing algorithm

Abstract

AbstractWith the accelerated changes of trade and economic structure, the fluctuation of shipment size is increasing in railway transportation. Railway companies are facing continuous challenges about how to optimise railcar transfer chain to achieve the balance between the workload of railway network and daily changing transportation demands. In this paper, elastic capacity constraints are designed to solve the number fluctuation of railcars in shipments. We define available capacity belts to describe the elastic capacities of railway network, then fuzzy theory is introduced. A membership function is designed to designate the satisfaction degree for the number of railcars, and a non-linear integer programming model is developed. We test the model with two numerical examples from the 2019 Railway Applications Section Problem Solving Competition, and a simulated annealing algorithm is employed to solve the problem. In the experiment with 16 yards, the model generated 1304 variables. Furthermore, as the scale of the railway network increases, the number of variables exhibited exponential explosive growth. In the experiment with 32 yards, the model generated 76,037 variables and determined 365 direct train services, resulting in an operating cost of 245,014,388 car-hours. The results of the experiments effectively verify the effectiveness of the model.KEYWORDS: Freight railway networkrailcar transfer chainfuzzy theorysimulated annealing algorithm Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.AcknowledgementsThe research was supported by the National Natural Science Foundation of China (U2268207).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant number U2268207].Notes on contributorsBoliang LinBoliang Lin received the Ph.D. degree in transportation management engineering from Southwest Jiaotong University, Chengdu, China, in 1994. From 1995 to 1997, he was a Post-doctoral Researcher with Beijing Jiaotong University. From 1997 to 2000. He was an Associate Professor with Beijing Jiaotong University. Since 2000, he has been a Professor with the Department of Transportation Management Engineering, Beijing Jiaotong University. His research interests include railway operation management, transportation systems network design, network flow techniques, transportation and logistics, and intelligent transportation system.Zhenyu WangZhenyu Wang received the B.S. degree in transportation engineering from Lanzhou Jiaotong University, Gansu, China, in 2019. He is currently pursuing the Ph.D. degree with the Department of Transportation Management Engineering, Beijing Jiaotong University, China. His research interests include transportation optimization and intelligent transportation system.