Optimizing Strategic And Operational Decisions Of Car Sharing Systems Under Demand Substitution And Uncertainty

Official URL: https://risc01.sabanciuniv.edu/record=b3205829

Optimizing car sharing systems under demand uncertainty is an emerging problem that aims to ensure profitable and sustainable operations with quality of service concerns when a mix fleet of vehicles including internal combustion and electrical engines is considered. To address this problem, we propose a two-stage stochastic mixed-integer program leveraging spatial-temporal networks that capture the strategic and operational decisions of these systems over a multi-period planning horizon. We optimize the location decisions of regions to serve with purchasing decisions of the vehicles while considering parking capacities, satisfying one-way and round-trip car rental requests, and relocating cars between open regions under each demand realization. We introduce demand substitution to this problem by extending the multi-commodity formulation, and further prove that the corresponding second-stage problem has a totally unimodular constraint matrix. As our solution approach, we provide a branch-and-cut based decomposition algorithm with enhancements. Our case study demonstrates the benefits of incorporating strategic and operational decisions along with the demand substitution, and provides insights for region opening and fleet allocation plans under demand uncertainty. We further present an extensive computational study highlighting the performance of the proposed solution algorithm with significant speedups