The complexity of branch-and-price algorithms for the capacitated vehicle routing problem with stochastic demands

The capacitated vehicle routing problem with stochastic demands (CVRPSD) is a variant of the deterministic capacitated vehicle routing problem where customer demands are random variables. While the most successful formulations for several deterministic vehicle-routing problem variants are based on a set-partitioning formulation, adapting such formulations for the CVRPSD under mild assumptions on the demands remains … Read more

Stochastic Last-mile Delivery with Crowd-shipping and Mobile Depots

This paper proposes a two-tier last-mile delivery model that optimally selects mobile depot locations in advance of full information about the availability of crowd-shippers, and then transfers packages to crowd-shippers for the final shipment to the customers. Uncertainty in crowd-shipper availability is incorporated by modeling the problem as a two-stage stochastic integer program. Enhanced decomposition … Read more

Benders Cut Classification via Support Vector Machines for Solving Two-stage Stochastic Programs

We consider Benders decomposition for solving two-stage stochastic programs with complete recourse based on finite samples of the uncertain parameters. We define the Benders cuts binding at the final optimal solution or the ones significantly improving bounds over iterations as valuable cuts. We propose a learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut classification … Read more

An Integrated Car-and-ride Sharing System for Mobilizing Heterogeneous Travelers with Application in Underserved Communities

The fast-growing carsharing and ride-hailing businesses are generating economic benefits and societal impacts in the modern society. However, both have limitation to cover demand from diverse populations, e.g., travelers in low-income, underserved communities. In this paper, we consider two types of travelers: Type~1 who rent shared cars and Type~2 who need shared rides. We propose … Read more