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The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem

Julie Kienzle, Serge Bisaillon, Teodor Gabriel Crainic, Emma Frejinger

TL;DR

This work introduces the Intermodal Railroad Blocking and Railcar Fleet-Management (IBRM) problem, a tactical planning challenge that integrates three consolidation processes and heterogeneous railcar fleet management within a cyclic service network. It advances a four-layer time-space network (SSND-RM) ILP formulation and a relax-and-fix–based warm-start heuristic to enable solving large-scale CN data with practical accuracy. The study demonstrates that ignoring loading constraints or fleet management yields substantial underestimation of required capacity, while coordinated fleet design and multi-platform railcars can significantly improve capacity utilization. The results provide actionable managerial insights for intermodal operators and pave the way for future stochastic and operational extensions in rail network planning.

Abstract

Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses tactical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a scheduled service network design with resource management (SSND-RM) formulation, expressed as an integer linear program. While such formulations are challenging to solve at scale, we demonstrate that our problem can be tackled with a general-purpose solver when provided with high-quality warm-start solutions. To this end, we design a construction heuristic inspired by a relax-and-fix procedure. We evaluate the methodology on realistic, large-scale instances from our industrial partner, the Canadian National Railway Company: a North American Class I railroad. The computational experiments show that the proposed approach efficiently solves practically relevant instances, and that solutions to the SSND-RM formulation yield substantially more accurate capacity estimations compared to those obtained from simpler baseline models. Managerial insights from our study highlight that ignoring railcar fleet management or container loading constraints can lead to a severe underestimation of required capacity, which may result in costly operational inefficiencies. Furthermore, our results show that the use of multi-platform railcars improves overall capacity utilization and benefits the network, even if they can locally lead to less efficient loading as measured by terminal-level slot utilization performance indicators.

The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem

TL;DR

This work introduces the Intermodal Railroad Blocking and Railcar Fleet-Management (IBRM) problem, a tactical planning challenge that integrates three consolidation processes and heterogeneous railcar fleet management within a cyclic service network. It advances a four-layer time-space network (SSND-RM) ILP formulation and a relax-and-fix–based warm-start heuristic to enable solving large-scale CN data with practical accuracy. The study demonstrates that ignoring loading constraints or fleet management yields substantial underestimation of required capacity, while coordinated fleet design and multi-platform railcars can significantly improve capacity utilization. The results provide actionable managerial insights for intermodal operators and pave the way for future stochastic and operational extensions in rail network planning.

Abstract

Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses tactical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a scheduled service network design with resource management (SSND-RM) formulation, expressed as an integer linear program. While such formulations are challenging to solve at scale, we demonstrate that our problem can be tackled with a general-purpose solver when provided with high-quality warm-start solutions. To this end, we design a construction heuristic inspired by a relax-and-fix procedure. We evaluate the methodology on realistic, large-scale instances from our industrial partner, the Canadian National Railway Company: a North American Class I railroad. The computational experiments show that the proposed approach efficiently solves practically relevant instances, and that solutions to the SSND-RM formulation yield substantially more accurate capacity estimations compared to those obtained from simpler baseline models. Managerial insights from our study highlight that ignoring railcar fleet management or container loading constraints can lead to a severe underestimation of required capacity, which may result in costly operational inefficiencies. Furthermore, our results show that the use of multi-platform railcars improves overall capacity utilization and benefits the network, even if they can locally lead to less efficient loading as measured by terminal-level slot utilization performance indicators.
Paper Structure (43 sections, 8 equations, 6 figures, 5 tables, 1 algorithm)

This paper contains 43 sections, 8 equations, 6 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Description
  3. Train services.
  4. Containers.
  5. Railcars.
  6. Loading Rules.
  7. Blocks.
  8. Railcar fleet management.
  9. Decision-making problem.
  10. Literature Review
  11. Network Modeling
  12. General Structure and Train Layer
  13. Embarking on a Train
  14. The Container Layer
  15. The Car Layer
  16. ...and 28 more sections

Figures (6)

  • Figure 1: Illustrative examples of loading rules (not respected in the top figure, respected in the bottom figure)
  • Figure 2: Illustrative example of trains, blocks and demands
  • Figure 3: Four-layer (train, block, car, container) time-space network illustration
  • Figure 4: Illustration of how the demands are unbalanced and their types
  • Figure 5: Fleet composition for railcar scenarios with 40-ft and 53-ft platforms without extra trains
  • ...and 1 more figures