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Data-driven robust optimization for contextual vehicle rebalancing in on-demand ride services under demand uncertainty.

Authors :
Guo, Zhen
Yu, Bin
Shan, Wenxuan
Yao, Baozhen
Source :
Transportation Research Part C: Emerging Technologies. Sep2023, Vol. 154, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

The rebalancing of idle vehicles is critical to mitigating the supply–demand imbalance in on-demand ride services. Motivated by a ride service platform, this paper investigates a short-term vehicle rebalancing problem under demand uncertainty in the presence of contextual data. We deploy a novel data-driven robust optimization approach that takes a direct path from "Data" to "Decision" instead of the predict-then-optimize paradigm and leverages the prediction problem structure to seamlessly integrate demand predictions with optimization models. We further develop a risk-based uncertainty set to evaluate how well uncertain demand is estimated from contextual data by prediction models, and discuss the classes of prediction models that are highly compatible with robust optimization models. Based on the convex analysis and duality theory, we reformulate the original models into equivalent Mixed Integer Second Order Cone Programmings (MISOCPs) that are solvable via state-of-the-art commercial solvers. To solve large-scale instances, we utilize the affine decision rule technique to derive polynomial-sized reformulations. Extensive experiments are conducted on the instances based on a real-world on-demand ride service in Chengdu. The computational experiments demonstrate the promising performance of our rebalancing strategies and solution approaches. • Study a contextual vehicle rebalancing problem under demand uncertainty. • Deploy a data-driven approach to integrate optimization and demand prediction. • Develop a risk-based uncertainty set characterized by a set of prediction models. • Present exact and approximation reformulations in a master–subproblem framework. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0968090X
Volume :
154
Database :
Academic Search Index
Journal :
Transportation Research Part C: Emerging Technologies
Publication Type :
Academic Journal
Accession number :
169970332
Full Text :
https://doi.org/10.1016/j.trc.2023.104244