An integrated approach to vehicle routing and container loading problems under uncertainty.

• Grant number: 24/14591-5
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: July 15, 2025
• End date: January 14, 2026
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Franklina Maria Bragion de Toledo
• Grantee: Douglas Nogueira Do Nascimento
• Supervisor: Leandro Callegari Coelho
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: Université Laval, Canada
• Associated to the scholarship: 23/16405-1 - An integrated approach to vehicle routing and container loading problems under uncertainty, BP.PD

Abstract

This research project aims to study the integration of the Vehicle Routing Problem (VRP) with the Container Loading Problem (CLP), considering a heterogeneous fleet with alcohol/petrol and electric vehicles and uncertainties in travel times between clients. Integrating these two problems is of great practical relevance, as the efficiency in load allocation directly impacts the effectiveness of delivery routes. Both problems, fundamental in optimizing logistics operations, are traditionally addressed in a deterministic context and with a homogeneous fleet. In this research work, we will seek to reduce costs and the environmental impact of delivery routes. Thus, by solving the problem in an integrated way, we aim to make better use of the space available for delivery (packing) and reduce greenhouse gas emissions, including electric vehicles, in the planning. In addition, we will also address uncertainties regarding travel times. In this way, this research aims to fill an important research gap: developing exact and heuristic solution methods for VRP-CLP integration using a mixed fleet and under uncertainties that present optimal solutions and/or good quality heuristic solutions in an acceptable resolution time for real situations. Therefore, we intend to generate less polluting and robust solutions capable of dealing with unforeseen scenarios. The efficiency of the methods developed will be evaluated through computational experiments with randomly generated data and data adapted from the literature.