Scholarship 24/06580-3 - Branch-and-cut, Otimização - BV FAPESP
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The vehicle routing problem with stochastic demand: new models and solution methods

Grant number: 24/06580-3
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Start date until: September 01, 2024
End date until: August 31, 2026
Field of knowledge:Engineering - Production Engineering - Operational Research
Principal Investigator:Pedro Augusto Munari Junior
Grantee:Caio Paziani Tomazella
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated research grant:22/05803-3 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings, AP.TEM

Abstract

In this project, we intend to address the vehicle routing problem with stochastic demands (VRPSD), which aims at defining a set of minimum cost routes for a vehicle fleet, in order to fulfill the demands of customers, considering that these demands are uncertain at the time of planning and, thus, can be altered during the routes' execution. The deterministic approach for this problem, in which it is assumed that demands are fully known when planning the routes and do not change, has significant chances of resulting in infeasible routes since, in practice, there can be unpredictable factors in the demand. In these cases, routes that were deemed optimal during the planning stage can result in low service levels and high operational costs for fulfilling additional demands. Therefore, the stochastic approach for this problem becomes valuable, since it considers possible variations in the demand and the costs of these additional operations that are needed when the variations occur, which are called recourse policies. In this proposal, the VRPSD is approached using stochastic programming with recourse, with the development of innovative compact formulations and integer L-shaped methods, and considering probability distributions represented by finite support vectors and scenarios. Therefore, we expect contributions to the VRPSD literature with the proposal of novel models and solution methods that advance the state-of-the-art and that are computationally efficient for their application in real-world, large-scale cases.

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