Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems

TL;DR

This paper develops a multi-objective optimization approach for static overnight bike rebalancing in shared mobility systems, considering demand uncertainty and robustness to improve service quality.

Contribution

It introduces a tri-objective model with a genetic algorithm and domain-specific operators, enhancing solution quality for real-world bike rebalancing problems.

Findings

Experiments on Barcelona Bicing show well-distributed Pareto solutions.

The proposed method outperforms greedy baselines in solution quality.

Robustness objectives help reduce peak-demand service degradation.

Abstract

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.