Lean Railroading for Improving Railroad Classification Terminal Performance: Bottleneck Management Methods

Document Type

Journal Article

Publication Date


Subject Area

operations - capacity, operations - traffic, operations - reliability, planning - methods, ridership - commuting, organisation - management, mode - rail


Terminal capacity, Service reliability, Regression analysis, Regression, Railways, Railroads, Railroad terminals, Network efficiency, Management, Goods movement, Freight transportation, Freight traffic, Freight handling, Freight classification, Dwell time, Commodity classification, Classification yards, Bottlenecks


Although much attention has been focused on the growth of intermodal traffic over the past decade, manifest freight (or carload) traffic is a major revenue generator for railroads. The high potential profitability of carload traffic suggests that railroads should try to grow this segment of traffic further, especially in an era of limited railway capacity. To do this, they must meet the increasing logistical needs of their customers by providing more reliable service. The classification terminal is a key determinant in service reliability of manifest freight. Terminal performance also affects network efficiency. Regression analysis showed that, as average dwell time increased, average manifest train speed decreased. Inadequate terminal capacity is viewed by many as a barrier to improved service reliability and network efficiency. Because terminals can be considered production systems, insight is gained by adapting tools that have led to significant performance improvement in manufacturing. A new approach is introduced: lean railroading. The most important manufacturing process analog to improving terminal capacity is the bottleneck. The train assembly (pull-down) process has been identified as the bottleneck in a majority of classification yards. A sensitivity analysis conducted on three bottleneck management alternatives suggests that pull-down capacity can be increased by as much as 26%, compared with the baseline case without large labor or capital expenses, through better management of the process and its interactions with the system. To maximize efficient use of rail yard infrastructure and resources, more emphasis should be placed on the quality of the classification process, rather than on quantity.