An innovative approach to solve the carsharing demand-supply imbalance problem under demand uncertainty

Abstract

This paper studies the demand-supply imbalance problem for one-way carsharing systems under a combination of pricing strategy, relocations and access trips considering stochastic demand. A novel concept of a virtual zone is utilized to capture vehicle relocation range and client walking or biking distance constraints in one-way carsharing systems. The vehicle imbalance problem is further addressed by combining a long-term pricing strategy and real-time vehicle relocations in a two-stage stochastic programming model. In the first stage, the tactical decisions including fleet size and trip price are optimized, while anticipating the operational costs from the second stage. The second stage optimizes operational decisions under uncertain demand including vehicle relocations conditional on the tactical decisions in stage one. The model aims to maximize the profit of a carsharing company considering the fleet costs calculated in stage one and the expected operational costs and revenue obtained in stage two. A dedicated gradient search algorithm is developed to solve the two-stage stochastic programming and results are compared to a genetic algorithm and an iterated local search algorithm. The proposed model and corresponding solution approach are applied to a large-scale network with 50 zones and over 1000 vehicles in Suzhou, China. The application allows us to attain additional operational insight. Results suggest that increased prices for high demand stations during peak hours reduce demand while maintaining profitability of the system. It is also found that the real-time vehicle relocations and flexibility of clients to pick up vehicles at farther stations can increase demand service rate by as much as 10%.