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To solve the mismatch between the supply and demand of shared electric vehicles (SEVs) caused by the uneven distribution of SEVs in space and time, an SEV relocating optimization model is designed based on a reward mechanism. The aim of the model is to achieve a cost-minimized rebalancing of the SEV system. Users are guided to attend the relocating SEVs by a reward mechanism, and employees can continuously relocate multiple SEVs before returning to the supply site. The optimization problem is solved by a heuristic column generation algorithm, in which the driving routes of employees are added into a pool by column generation iteratively. In the pricing subproblem of column generation, the Shuffled Complex Evolution–University of Arizona (SCE–UA) is designed to generate a driving route. The proposed model is verified with the actual data of the Dalian city. The results show that our model can reduce the total cost of relocating and improve the service efficiency.

To solve the mismatch between the supply and demand of shared electric vehicles (SEVs) caused by the uneven distribution of SEVs in space and time, an SEV relocating optimization model is designed based on a reward mechanism. The aim of the model is to achieve a cost-minimized rebalancing of the SEV system. Users are guided to attend the relocating SEVs by a reward mechanism, and employees can continuously relocate multiple SEVs before returning to the supply site. The optimization problem is solved by a heuristic column generation algorithm, in which the driving routes of employees are added into a pool by column generation iteratively. In the pricing subproblem of column generation, the Shuffled Complex Evolution--University of Arizona (SCE-UA) is designed to generate a driving route. The proposed model is verified with the actual data of the Dalian city. The results show that our model can reduce the total cost of relocating and improve the service efficiency. [ABSTRACT FROM AUTHOR]