Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems
2026-04-09 • Neural and Evolutionary Computing
Neural and Evolutionary Computing
AI summaryⓘ
The authors study how to best move bikes overnight in a city bike-sharing system to meet uncertain user demand the next day. They create a model that tries to minimize travel distance, unmet user demand, and also improve service during busy times. Using a genetic algorithm, they find good bike relocation plans that balance these goals, and they test it on Barcelona's bike network with 460 stations. Their approach outperforms simple methods by offering better trade-offs between different objectives.
bike-sharing systemsdemand uncertaintytri-objective optimizationovernight rebalancingNon-dominated Sorting Genetic Algorithm IIPareto setroute planningsimulationstation capacityservice robustness
Authors
Diego Daniel Pedroza-Perez, Gabriel Luque, Sergio Nesmachnow, Jamal Toutouh
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.