A data-driven discrete simulation-based optimization algorithm for car-sharing service design

Tianli Zhou, Evan Fields and Carolina Osorio

Transportation Research Part B: Methodological, 2023, vol. 178, issue C

Abstract: This paper formulates a discrete simulation-based optimization (SO) algorithm for a family of large-scale car-sharing service design problems. We focus on the profit-optimal assignment of vehicle fleet across a network of two-way (i.e., round-trip) car-sharing stations. The proposed approach is a metamodel SO approach. A novel metamodel based on a mixed-integer program (MIP) is formulated. The metamodel is embedded within a general-purpose discrete SO algorithm. The proposed algorithm is validated with synthetic toy network experiments. The algorithm is then applied to a high-dimensional Boston case study using reservation data from a major US car-sharing operator. The method is benchmarked versus several algorithms, including stochastic programming. The experiments indicate that the analytical network model information, provided by the MIP to the SO algorithm, is useful both at the first iteration of the algorithm and across subsequent iterations. The solutions derived by the proposed method are benchmarked versus the solution deployed in the field by the car-sharing operator. Via simulation, the proposed solutions improve those deployed with an average improvement of profit of 6% and of vehicle utilization of 3%.

Keywords: Discrete simulation-based optimization; Metamodel; Large-scale car-sharing fleet allocation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.trb.2023.102818

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