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Robust optimization applied to vehicle routing

Grant number: 18/00463-4
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): April 01, 2018
Effective date (End): December 31, 2019
Field of knowledge:Engineering - Production Engineering - Operational Research
Principal Investigator:Pedro Augusto Munari Junior
Grantee:Rafael Ajudarte de Campos
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated research grant:16/01860-1 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings, AP.TEM


Mathematical formulations and computational methods for the solution of the vehicle routing problem (VRP) have shown to be of great relevance in supporting decision making in logistics systems. Efficient routes can bring significant benefits in terms of transportation costs, delivery times and emissions of polluting gases, in addition of guaranteeing the level of service contracted by the customer and better working conditions for the professionals involved. This practical importance and the computational challenge of solving instances at reasonable running times make the VRP one of the most studied problems in Operations Research. Despite the very active research, few papers addressing the VRP have dealt with the inherent uncertainty of input data, so that, in several contexts, the solutions are not actually feasible in practice. Robust Optimization (RO) is a technique that allows the incorporation of uncertainties into optimization problems, so that solutions are protected against variations in data, thus increasing the chances of actually being feasible in practice. The objective of this project is to study the concepts of RO and its applications in vehicle routing. The main RO formulations for the VRP will be studied and implemented computationally and used for solving instances from the literature. In addition, we intend to use the implemented formulations for computational experiments with real-life data provided by an airline in the context of aircraft routing for on-demand air transportation services. (AU)

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