This talk focuses on the integrated load planning and sequencing problem (LPSP) for double-stack trains. This decision-making problem occurs in intermodal terminals and consists in assigning containers from a storage area to slots on railcars of outbound trains and in determining the loading sequence of the handling equipment. By extending prior work on load planning, we propose two different integer programming formulations. We show by an extensive numerical study that we can solve instances with up to 50 containers with a commercial general-purpose solver in less than 20 min. A case study based on real data provided by the Canadian National Railway Company highlights that the LPSP can reduce the number of container handling in intermodal terminals compared to sequential solutions by on average 11.3% and 16.5% for gantry cranes and reach stackers, respectively. In the last part of the talk, we briefly discuss the importance of considering the operational load planning problem when devising tactical network plans for intermodal transportation. In this context, combining operations research and machine learning methodologies has great potential.
The talk is based on the following papers:
Ruf, M., Cordeau, J.-F., Frejinger, E. The Load Planning and Sequencing Problem for Double-Stack Intermodal Trains. Journal of Rail Transport Planning & Management 23:100337, 2022.
Larsen, E., Lachapelle, S., Bengio, Y., Frejinger, E., Lacoste-Julien, S., Lodi, A., Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information. INFORMS Journal on Computing 34(1):227-242, 2022.
Larsen, E., Frejinger, E., Gendron, B., Lodi, A., Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning, arXiv:2205.00897.
About the Speaker:
Prof. Emma Frejinger is a Professor at the Department of Computer Science and Operations Research at Université de Montréal. She is the holder of the Canada Research Chair in Demand Forecasting and Optimization of Transport Systems, and the holder of the CN Chair in Optimization of Railway Operations. She has a Ph.D. in mathematics from École Polytechnique Fédérale de Lausanne, Switzerland. Her areas of expertise include both demand forecasting and optimization of transportation networks. Her research is mostly focused on developing new methodologies combining techniques from operations research and machine learning to tackle large-scale real-world problems. Her current major projects are related to the optimization of railway operations. Her students and herself have won several international awards, including the prestigious TSL Dissertation Prize bestowed by the INFORMS Transport Science and Logistics Society. She has realized numerous projects in collaboration with public and private actors. She is a member of CIRRELT, she works part-time as a scientific advisor for IVADO Labs and she is a founding fellow of AI Sweden.