Inventory rebalancing through pricing in public bike sharing systems
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This thesis aims to improve the operational performance of public bike sharing systems using pricing schemes. Demand data from Capital Bikeshare in Washington, DC is used for analysis. A price vector is used to offer incentives to customers to take bicycles from or park them at neighboring stations in order to minimize the number of unbalanced stations. This reduces the need for trucks and dedicated staff to carry out inventory repositioning. For smaller networks, a bilevel optimization model is introduced to minimize the number of unbalanced stations optimally. The results are compared with two heuristic approaches. One approach involves Genetic Algorithm, while the second changes the price by segregating the stations into different categories based on their current inventory profile, future demand, and maximum and minimum inventory values calculated to fulfill certain desired service level requirements. It is shown that this approach reduces the overall operating cost while partially or fully obviating the need for a manual repositioning operation.