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.

翻译：基于桩式共享单车系统中存在自行车供给与用户需求之间的空间失衡问题，通常采用夜间卡车再平衡策略。本文研究需求不确定性下的静态夜间再平衡问题，并将其建模为三目标优化模型。优化目标包括最小化总行驶距离、预期未满足需求以及针对高需求场景的鲁棒性未满足需求度量。路径规划方案通过包含卡车装载约束与站点容量约束的补偿模拟进行评估，该模拟涵盖多个需求实现场景。鲁棒性目标有助于筛选能降低峰值需求服务退化的规划方案。采用基于排列-分区编码的快速非支配排序遗传算法（NSGA-II）与领域特定重组算子（包括面向站点重定位的偏优最优改进移动）逼近帕累托前沿。基于包含460个站点的巴塞罗那Bicing真实系统实验表明，该方法能够生成分布良好的帕累托解集，且对参考非支配解集具有显著贡献。基于贪婪构造的基线方法主要生成极端解，且通常处于被支配状态。