Accelerating Fleet Upgrade Decisions with Machine-Learning Enhanced Optimization

TL;DR

This paper introduces a machine learning-enhanced optimization method for fleet upgrade decisions, significantly improving scalability and computational efficiency over traditional integer programming in large-scale fleet management scenarios.

Contribution

It proposes a novel machine learning-based approach to transform fleet upgrade optimization into a mixed discrete-continuous problem, enabling practical large-scale application.

Findings

Machine learning approach achieves near-optimal solutions.

Significant improvements in scalability and computational performance.

Validated in a real-world automotive case study.

Abstract

Rental-based business models and increasing sustainability requirements intensify the need for efficient strategies to manage large machine and vehicle fleet renewal and upgrades. Optimized fleet upgrade strategies maximize overall utility, cost, and sustainability. However, conventional fleet optimization does not account for upgrade options and is based on integer programming with exponential runtime scaling, which leads to substantial computational cost when dealing with large fleets and repeated decision-making processes. This contribution firstly suggests an extended integer programming approach that determines optimal renewal and upgrade decisions. The computational burden is addressed by a second, alternative machine learning-based method that transforms the task to a mixed discrete-continuous optimization problem. Both approaches are evaluated in a real-world automotive industry case study, which shows that the machine learning approach achieves near-optimal solutions with significant improvements in the scalability and overall computational performance, thus making it a practical alternative for large-scale fleet management.