A smart predict-then-optimize framework for vehicle rebalancing problem

Y. Jay Guo; Zicheng Su; Hai Yang; Enming Liang; Chen Zhong; Wanjing Ma

doi:10.1016/j.trb.2026.103411

February 5, 2026Transportation Research Part B Methodological

A smart predict-then-optimize framework for vehicle rebalancing problem

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Key Points

The optimization framework improves vehicle rebalancing efficiency, leading to better resource allocation.
Key evidence shows up to 25% reduction in downtime for vehicles when using predictive algorithms.
Analysis focused on vehicle rebalancing in urban settings, using advanced logistic systems to enhance performance.
Implications suggest that implementing this framework could vastly improve transportation systems, with broader applications in the logistics industry.

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Implication

Observational analysis identifies improved transportation efficiency through a predictive optimization framework for vehicle rebalancing, highlighting implications for logistics management.

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Guo et al. (2026) studied this question.

synapsesocial.com/papers/69a766d9badf0bb9e87dea6c https://doi.org/10.1016/j.trb.2026.103411

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A smart predict-then-optimize framework for vehicle rebalancing problem

Key Points

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Implication

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A smart predict-then-optimize framework for vehicle rebalancing problem

Key Points

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Implication

Discussion

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