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Rich vehicle routing problems: models and algorithms for deterministic and stochastic variants

Grant number: 19/23596-2
Support Opportunities:Regular Research Grants
Duration: September 01, 2020 - February 28, 2023
Field of knowledge:Engineering - Production Engineering - Operational Research
Convênio/Acordo: CONFAP - National Council of State Research Support Foundations
Principal Investigator:Pedro Augusto Munari Junior
Grantee:Pedro Augusto Munari Junior
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated researchers: Bruno Petrato Bruck ; Teobaldo Leite Bulhões Júnior


In the past few years, the development of computer technology together with methodological advances in Operations Research has motivated the investigation of more complex variants of the Vehicle Routing Problem (VRP). These variants are often inspired by real-world applications and they often include challenging objective functions and constraints. In the literature, such problems are referred to as Rich Vehicle Routing Problems (RVRPs). The interest on RVRPs has recently increased and especially focused on deterministic variants, although most realistic applications are typically characterized by a high level of uncertainty. Indeed, logistics managers must be prepared to make complex decisions without complete information. On the other hand, taking uncertainties into account adds more challenges to modeling and solving the involved problems. This research project aims at studying RVRPs that arise on commercial and humanitarian logistics, under deterministic and stochastic perspectives. We intend to develop new mathematical formulations and exact, heuristic and hybrid methods for RVRPs that capture real-world features and are computationally efficient to aid decision making in practice. This project has the potential of establishing and consolidating a collaboration between research groups from the states of Paraíba and São Paulo, in an effort to effectively address challenging practical problems, with scientific and technological outcomes for both states. (AU)

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