Optimized Train Path Selection Method for Daily Freight Train Scheduling

Abstract

Train timetables are usually established far in advance of operations and are based on forecasted demands. However, due to changes in actual freight traffic, railways need to determine the actual operation of trains arising in daily operations through train path selection, i.e., selecting a portion of the timetable for these trains to execute. To improve current freight train scheduling in daily operations, this paper suggests taking into account the car flow transfer between consecutive trains and shipment delivery time requirements. A train path selection optimization model is developed to minimize the total travel time of freight trains while seeking minimum penalties for shipment delivery delays. A tabu search algorithm is designed to solve this problem. The effectiveness of the proposed model and algorithm is demonstrated by numerical experiments on instances built on real data from the Menghua railway, a rail freight corridor. The results show that, compared to current practice, this optimization method can achieve a reduction in total train travel time and ensure the punctual delivery of shipments.

Keywords

• Freight transport
• train scheduling
• tabu search
• daily operations