Practice-Based Optimization for the Strategic Locomotive Assignment Problem

TL;DR

This paper introduces a strategic optimization framework for locomotive assignment in freight rail networks, effectively handling complex constraints and large-scale problems to improve operational efficiency.

Contribution

It develops a novel network-based integer programming model with reduction techniques for large-scale, real-world locomotive assignment problems, enhancing solution scalability and practicality.

Findings

Exact solutions for large instances achieved for the first time.

Reduction rules significantly decrease network complexity.

Framework improves downstream planning effectiveness.

Abstract

This study addresses the challenge of efficiently assigning locomotives in large freight rail networks, where operational complexity and power imbalances make cost-effective planning difficult. It presents a strategic optimization framework for the Locomotive Assignment Problem (LAP), developed in collaboration with a major North American Class I Freight Railroad. The problem is formulated as a network-based integer program over a cyclic space-time network, producing a repeatable weekly locomotive assignment plan. The model captures a comprehensive set of real-world operational constraints and jointly optimizes the placement of pick-up and set-out locomotive work events, improving the effectiveness of downstream planning. To solve large-scale instances exactly for the first time, novel reduction rules are introduced to dramatically reduce the number of light travel arcs in the space-time network. Extensive computational experiments demonstrate the performance and trade-offs on real instances under a variety of practical constraints. Beyond delivering scalable, high-quality solutions, the proposed framework serves as a practical decision-support tool grounded in the operational realities of modern freight railroads.