Academic Journal
A smart predict-then-optimize framework for vehicle rebalancing problem.
| Τίτλος: | A smart predict-then-optimize framework for vehicle rebalancing problem. |
|---|---|
| Συγγραφείς: | Guo, Yuhang1 (AUTHOR), Su, Zicheng1 (AUTHOR) suzicheng@tongji.edu.cn, Yang, Hai2 (AUTHOR), Liang, Enming3 (AUTHOR), Zhong, Chen4 (AUTHOR), Ma, Wanjing1 (AUTHOR) mawanjing@tongji.edu.cn |
| Πηγή: | Transportation Research Part B: Methodological. Apr2026, Vol. 206, pN.PAG-N.PAG. 1p. |
| Θεματικοί όροι: | *Supply & demand, *Ridesharing services, *Motor vehicle fleets, Mixed integer linear programming, Robust optimization, Sensitivity analysis |
| Εταιρία/Οντότητα: | Didi Chuxing (Company) |
| Περίληψη: | • A regional-level vehicle rebalancing model is proposed, incorporating operational constraints and supply–demand uncertainty. • A piecewise-linear response function is designed to capture the dynamic supply–demand conditions. • A Smart Predict-then-Optimize (SPO) framework is introduced to bridge predictive models and optimization layers. • The effectiveness of the proposed framework is demonstrated through numerical and simulator experiments on real-world ride-hailing data. Matching the imbalanced supply and demand through vehicle rebalancing is critical for enhancing the operational efficiency of ride-hailing platforms. However, the traditional two-stage Predict-then-Optimize (PO) framework suffers from a mismatch between the loss function of the upstream prediction model and the objective function used in downstream decision-making. To tackle this challenge, we propose a Smart Predict-then-Optimize (SPO) framework, in which the prediction model is trained to directly minimize decision loss. Firstly, we formulate the regional-level vehicle rebalancing problem as a mixed integer linear programming (MILP) model, aiming to maximize the Gross Merchandise Volume (GMV) of the ride-hailing platform. After that, the Spatial and Temporal Identity (STID) model is employed to predict future demand and supply. Instead of training the prediction model by minimizing fitting error, we adopt a decision-focused loss function determined by the solution of the optimization model. Considering that uncertain parameters appear in the constraints, we develop a penalty-augmented loss function along with a corresponding solution adjustment method. Moreover, we propose a perturbation-based method to address the challenge of gradient backpropagation through the non-differentiable optimization layer, which enables the gradients of the decision loss to be obtained via zeroth-order approximation. The theoretical properties are checked, showing that the method can yield an unbiased approximation of the gradient. We conduct extensive experiments on a real-world dataset from Didi Chuxing, including both numerical studies and simulation experiments. The results show that the proposed SPO framework improves the average GMV by 2.19% compared to rule-based rebalancing and by 0.28% compared to the PO strategy. In particular, the prediction model trained by the SPO method can learn the utility of each region, enabling more effective vehicle rebalancing by dispatching drivers from low-utility origins to high-utility destinations. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: A smart predict-then-optimize framework for vehicle rebalancing problem. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guo%2C+Yuhang%22">Guo, Yuhang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Su%2C+Zicheng%22">Su, Zicheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> suzicheng@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Hai%22">Yang, Hai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Enming%22">Liang, Enming</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhong%2C+Chen%22">Zhong, Chen</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Wanjing%22">Ma, Wanjing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mawanjing@tongji.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Transportation+Research+Part+B%3A+Methodological%22">Transportation Research Part B: Methodological</searchLink>. Apr2026, Vol. 206, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Supply+%26+demand%22">Supply & demand</searchLink><br />*<searchLink fieldCode="DE" term="%22Ridesharing+services%22">Ridesharing services</searchLink><br />*<searchLink fieldCode="DE" term="%22Motor+vehicle+fleets%22">Motor vehicle fleets</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+optimization%22">Robust optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="DE" term="%22Didi+Chuxing+%28Company%29%22">Didi Chuxing (Company)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • A regional-level vehicle rebalancing model is proposed, incorporating operational constraints and supply–demand uncertainty. • A piecewise-linear response function is designed to capture the dynamic supply–demand conditions. • A Smart Predict-then-Optimize (SPO) framework is introduced to bridge predictive models and optimization layers. • The effectiveness of the proposed framework is demonstrated through numerical and simulator experiments on real-world ride-hailing data. Matching the imbalanced supply and demand through vehicle rebalancing is critical for enhancing the operational efficiency of ride-hailing platforms. However, the traditional two-stage Predict-then-Optimize (PO) framework suffers from a mismatch between the loss function of the upstream prediction model and the objective function used in downstream decision-making. To tackle this challenge, we propose a Smart Predict-then-Optimize (SPO) framework, in which the prediction model is trained to directly minimize decision loss. Firstly, we formulate the regional-level vehicle rebalancing problem as a mixed integer linear programming (MILP) model, aiming to maximize the Gross Merchandise Volume (GMV) of the ride-hailing platform. After that, the Spatial and Temporal Identity (STID) model is employed to predict future demand and supply. Instead of training the prediction model by minimizing fitting error, we adopt a decision-focused loss function determined by the solution of the optimization model. Considering that uncertain parameters appear in the constraints, we develop a penalty-augmented loss function along with a corresponding solution adjustment method. Moreover, we propose a perturbation-based method to address the challenge of gradient backpropagation through the non-differentiable optimization layer, which enables the gradients of the decision loss to be obtained via zeroth-order approximation. The theoretical properties are checked, showing that the method can yield an unbiased approximation of the gradient. We conduct extensive experiments on a real-world dataset from Didi Chuxing, including both numerical studies and simulation experiments. The results show that the proposed SPO framework improves the average GMV by 2.19% compared to rule-based rebalancing and by 0.28% compared to the PO strategy. In particular, the prediction model trained by the SPO method can learn the utility of each region, enabling more effective vehicle rebalancing by dispatching drivers from low-utility origins to high-utility destinations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Transportation Research Part B: Methodological is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.trb.2026.103411 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Supply & demand Type: general – SubjectFull: Ridesharing services Type: general – SubjectFull: Motor vehicle fleets Type: general – SubjectFull: Mixed integer linear programming Type: general – SubjectFull: Robust optimization Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Didi Chuxing (Company) Type: general Titles: – TitleFull: A smart predict-then-optimize framework for vehicle rebalancing problem. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guo, Yuhang – PersonEntity: Name: NameFull: Su, Zicheng – PersonEntity: Name: NameFull: Yang, Hai – PersonEntity: Name: NameFull: Liang, Enming – PersonEntity: Name: NameFull: Zhong, Chen – PersonEntity: Name: NameFull: Ma, Wanjing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01912615 Numbering: – Type: volume Value: 206 Titles: – TitleFull: Transportation Research Part B: Methodological Type: main |
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