In several transportation systems, vehicles can choose where to meet customers rather than stopping in fixed locations. This added flexibility, however, requires coordination between vehicles and customers that adds complexity to routing operations. This paper develops scalable algorithms to optimize these operations. First, we solve the one-stop subproblem in the
ℓ
1
space and the
ℓ
2
space by leveraging the geometric structure of operations. Second, to solve a multistop problem, we embed the single-stop optimization into a tailored coordinate descent scheme, which we prove converges to a global optimum. Third, we develop a new algorithm for dial-a-ride problems based on a subpath-based time–space network optimization combining set partitioning and time–space principles. Finally, we propose an online routing algorithm to support real-world ride-sharing operations with vehicle–customer coordination. Computational results show that our algorithm outperforms state-of-the-art benchmarks, yielding far superior solutions in shorter computational times and can support real-time operations in very large-scale systems. From a practical standpoint, most of the benefits of vehicle–customer coordination stem from comprehensively reoptimizing “upstream” operations as opposed to merely adjusting “downstream” stopping locations. Ultimately, vehicle–customer coordination provides win–win–win outcomes: higher profits, better customer service, and smaller environmental footprint.
This paper was accepted by Chung Piaw Teo, optimization.
Funding:
This research was supported by the National Natural Science Foundation of China [Grants 72288101, 52221005 and 52220105001].
Supplemental Material:
The e-companion and data are available at
https://doi.org/10.1287/mnsc.2023.4739
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Routing Optimization with Vehicle–Customer Coordination

10.1287/mnsc.2023.4739