Abordagens para reagendamento de voos de helicópteros em uma empresa de óleo e gás natural

• Autor(a) principal: Fantazzini, Tarley Mansur
• Data de Publicação: 2025
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://hdl.handle.net/20.500.14289/22046
• topic: Aircraft recovery problem; Helicopters; Integer programming; Multi-objective programming; Stochastic programming; Oil and gas industry; ENGENHARIAS::ENGENHARIA DE PRODUCAO::PESQUISA OPERACIONAL; Problema de recuperação de aeronaves; Helicópteros; Programação inteira; Programação multiobjetivo; Programação estocástica; Indústria de óleo e gás

Air transportation of passengers relies on prior planning of scheduled flights. However, unforeseen events may affect this scheduling, making recovery and rescheduling actions necessary. In this context, the Aircraft Recovery Problem (ARP) arises, aiming to reschedule flights affected by such disruptions. This work addresses a specific ARP faced by a Brazilian oil and natural gas extraction company operating offshore platforms, where third-party helicopters are used to transport employees between airports and maritime units. Several optimization-based approaches are proposed to model and solve this problem. Initially, hierarchical goal programming was applied with three sequential objectives: minimizing the number of postponed flights, helicopter usage, and total delays. Optimal or near-optimal solutions were obtained with low computational times for instances with up to 50 flights. Subsequently, multi-objective models were adopted, including the weighted Chebyshev metric and the approach proposed by Lokman and Köksalan, enabling the identification of non-dominated solutions that compose the Pareto frontier. These methods showed promising results, with computational gaps below 12% and reduced execution times, even with a shortened time limit. To complement the deterministic approaches, stochastic programming techniques were incorporated to handle uncertainties in the problem parameters, aiming to capture and analyze the variability and risks associated with uncertain values such as flight time between the airport and maritime units. The integration of deterministic and stochastic approaches provided a comprehensive and flexible analysis of this ARP, enabling result optimization in both controlled and uncertain scenarios, and contributing to more effective management of aircraft recovery operations.