The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem

TL;DR

This paper introduces a new integrated planning problem for intermodal railroad operations, modeling resource management and consolidation processes, and demonstrates effective solution methods on large-scale real-world instances, improving capacity estimation accuracy.

Contribution

It formulates a novel SSND-RM model for intermodal rail operations, incorporating heterogeneous fleet management and consolidation, and develops a heuristic to solve large-scale instances efficiently.

Findings

The approach accurately estimates capacity compared to baseline models.

Ignoring fleet and container constraints underestimates capacity needs.

Multi-platform railcars enhance overall network capacity utilization.

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.