The vehicle allocation problem with uncertain parameters: robust optimization and stochastic programming approaches

• Grant number: 24/19664-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: December 01, 2024
• End date: April 30, 2025
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Reinaldo Morabito Neto
• Grantee: João Marcos Pereira Silva
• 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

The Vehicle Allocation Problem (VAP) involves allocating a fleet of vehicles to meet the forecasted freight transportation demand between terminals over a finite and multi-period time horizon. The objective is to maximize the profit generated by services performed with the company's fleet, taking into account demand forecasting. In some cases, the goal is alternatively to minimize the costs involved in meeting the entire demand, which may require hiring additional third-party haulers. The size of real-world instances faced by road freight haulers is considerably large, making it difficult to obtain optimal solutions within acceptable computational times. Therefore, the literature has focused on developing heuristic methods that allow for good solutions to be obtained within computational times that are practically tolerable. In some cases, custom exact methods have also been developed and applied to address deterministic versions of the problem, requiring reasonable computational times. In this postdoctoral research project, the aim is to extend both exact and heuristic approaches to consider uncertainties in the problem's parameters, particularly in customer demands, vehicle availability, and vehicle travel times. Stochastic programming and robust optimization models and approaches for the problem will be developed and tested to generate solutions that are more resilient to parameter uncertainties. (AU)