用机器学习加速大型车队升级决策，提升效率与可扩展性。
Accelerating Fleet Upgrade Decisions with Machine-Learning Enhanced Optimization
- 将车队升级问题转化为混合离散-连续优化，降低计算复杂度。
- 机器学习方法在真实案例中达到近似最优解，计算速度显著提升。
- 适合需要频繁决策的大型车队管理场景，如租车公司或物流集团。
基于租赁的商业模式和日益增长的可持续性要求，推动了对大型机械与车辆车队更新与升级策略的高效管理需求。优化的车队升级策略能最大化整体效益、降低成本并提升可持续性。然而，传统车队优化未考虑升级选项，且基于整数规划，其运行时间随规模呈指数级增长，导致大规模车队及重复决策时计算成本高昂。本文首先提出一种扩展的整数规划方法，以确定最优的更新与升级决策；其次，提出一种基于机器学习的替代方法，将问题转化为混合离散-连续优化，缓解计算负担。两种方法在真实汽车工业案例中进行了评估，结果显示，机器学习方法在保持近似最优解的同时，显著提升了可扩展性与整体计算性能，为大规模车队管理提供了实用替代方案。
原文摘要 · Abstract (English)
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.
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