English

Vehicle Fleet Sizing, Positioning and Routing Problem with Stochastic Customers

Optimization and Control 2021-09-21 v3

Abstract

The Vehicle Fleet Sizing, Positioning and Routing Problem with Stochastic Customers (VFSPRP-SC) consists on pairing strategic decisions of depot positioning and fleet sizing with operational vehicle routing decisions while taking into account the inherent uncertainty of demand. We successfully solve the VFSPRP-SC with a methodology comprised of two main blocks: i) a scenario generation phase and ii) a two-stage stochastic program. For the first block, a set of scenarios is selected with a simulation-based approach that captures the behavior of the demand and allows us to come up with different solutions that could match different risk profiles. The second block is comprised of a facility location and allocation model and a Multi Depot Vehicle Routing Problem (MDVRP) assembled under a two-stage stochastic program. We propose several novel ideas within our methodology: problem specific cuts that serve as an approximation of the expected second stage costs as a function of first stage decisions; an activation paradigm that guides our main optimization procedure; and, a way of mapping feasible routes from one second-stage problem data into another; among others. We performed experiments for two cases: the first case considers the expected value of the demand, and the second case considers the right tail of the demand distribution, seeking a conservative solution. By using acceleration techniques we obtain solutions within 1 to 6 hours, reasonable times considering the strategic nature of the decision. For the ex-post evaluation, we solve 75% of the instances in less than 3 minutes, meaning that the methodology used to solve the MDVRP is well suited for daily operation.

Keywords

Cite

@article{arxiv.2109.08114,
  title  = {Vehicle Fleet Sizing, Positioning and Routing Problem with Stochastic Customers},
  author = {David Corredor-Montenegro and Maria Jose Consuegra-Laino and Alfaima Lucía Solano-Blanco and Camilo Gómez},
  journal= {arXiv preprint arXiv:2109.08114},
  year   = {2021}
}

Comments

16 pages, 12th AIMMS-MOPTA Optimization Modeling Competition

R2 v1 2026-06-24T06:02:46.688Z