Accelerating Fleet Upgrade Decisions with Machine-Learning Enhanced Optimization

2025-07-29

AI Summary

Traditional integer programming approaches for large-scale fleet renewal and upgrade decisions suffer from high computational complexity and poor scalability. Method: This paper proposes a hybrid optimization–machine learning framework. First, it extends the classical integer programming model into a mixed discrete-continuous optimization framework supporting both “renewal” and “upgrade” decision options. Second, it innovatively incorporates machine learning surrogate models to replace computationally expensive exact subroutines, substantially reducing runtime overhead. Contribution/Results: Evaluated on a real-world automotive industry case, the method achieves near-optimal solution quality while accelerating computation by over an order of magnitude—enabling efficient, long-term sustainable management decisions for fleets comprising thousands of vehicles. Its core contribution is the first scalable, upgrade-aware fleet optimization paradigm, seamlessly integrating rigorous mathematical modeling with data-driven surrogate-based solving.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Optimize fleet renewal and upgrade decisions for cost and sustainability
• Address computational limits of traditional integer programming for large fleets
• Propose machine-learning method for scalable near-optimal fleet solutions

Innovation

• Extended integer programming for fleet upgrades
• Machine learning transforms optimization problem
• Near-optimal scalable solutions for fleets