Solving an Urban Ridesharing Problem with Stochastic Travel Times: A Simheuristic Approach

Abstract

Ridesharing and carsharing concepts are redefining mobility practices in cities across the world. These concepts, however, also raise noticeable operational challenges that need to be efficiently addressed. In the urban ridesharing problem (URSP), a fleet of small private vehicles owned by citizens should be coordinated in order to pick up passengers on their way to work, hence maximizing the total value of their trips while not exceeding a deadline for reaching the destination points. Since this is a complex optimization problem, most of the existing literature assumes deterministic travel times. This assumption is unrealistic and, for this reason, we discuss a richer URSP variant in which travel times are modeled as random variables. Using random travel times also forces us to consider a probabilistic constraint regarding the duration of each trip. For solving this stochastic optimization problem, a simheuristic approach is proposed and tested via a series of computational experiments.