Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems
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arXiv
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| Format: | Preprint |
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2026
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| author | Pedroza-Perez, Diego Daniel Luque, Gabriel Nesmachnow, Sergio Toutouh, Jamal |
| author_facet | Pedroza-Perez, Diego Daniel Luque, Gabriel Nesmachnow, Sergio Toutouh, Jamal |
| contents | Dock-based bike-sharing systems exhibit spatial imbalances between bicycle supply and user demand, often addressed through overnight truck-based rebalancing. This work studies static overnight rebalancing under demand uncertainty modeled as a tri-objective optimization problem. The objectives minimize total travel distance, expected unmet demand, and a robustness-oriented unmet demand measure over high-demand scenarios.
Route plans are evaluated via a recourse simulation that enforces truck loads and station capacity constraints across multiple demand realizations. The robustness objective supports selecting plans that reduce peak-demand service degradation. Trade-off solutions are approximated with Non-dominated Sorting Genetic Algorithm II using a permutation--partition encoding and domain-specific relocation operators, including a biased best-improvement move for station relocation.
Experiments on the real Barcelona Bicing system with 460 stations show well-distributed Pareto sets and substantial contributions to the reference non-dominated set. Greedy constructive baselines mainly yield extreme solutions and are often dominated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08296 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems Pedroza-Perez, Diego Daniel Luque, Gabriel Nesmachnow, Sergio Toutouh, Jamal Neural and Evolutionary Computing Dock-based bike-sharing systems exhibit spatial imbalances between bicycle supply and user demand, often addressed through overnight truck-based rebalancing. This work studies static overnight rebalancing under demand uncertainty modeled as a tri-objective optimization problem. The objectives minimize total travel distance, expected unmet demand, and a robustness-oriented unmet demand measure over high-demand scenarios. Route plans are evaluated via a recourse simulation that enforces truck loads and station capacity constraints across multiple demand realizations. The robustness objective supports selecting plans that reduce peak-demand service degradation. Trade-off solutions are approximated with Non-dominated Sorting Genetic Algorithm II using a permutation--partition encoding and domain-specific relocation operators, including a biased best-improvement move for station relocation. Experiments on the real Barcelona Bicing system with 460 stations show well-distributed Pareto sets and substantial contributions to the reference non-dominated set. Greedy constructive baselines mainly yield extreme solutions and are often dominated. |
| title | Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2604.08296 |