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Abstract(s)
No cenário empresarial atual, marcado por constantes mudanças e alta competitividade, as
organizações precisam de inovar de forma rápida e consistente. Isso tem levado ao
desenvolvimento de novas abordagens, métodos e ferramentas de otimização que auxiliam o
processo de tomada de decisões. Na gestão de projetos, garantir a eficiência e a eficácia é
crucial para alcançar o sucesso, e, por esse motivo, as ferramentas de apoio são projetadas para
melhorar esses dois fatores críticos. Esta dissertação tem como propósito aplicar técnicas de
otimização no escalonamento de projetos, com especial foco na utilização de meta-heurísticas,
nomeadamente o Simulated Annealing. Para alcançar esse objetivo, foi elaborado um modelo
matemático e um protótipo destinado a apoiar a gestão de cronogramas de projetos,
otimizando a alocação de recursos e minimizando os prazos de execução, em particular nos
ambientes complexos e com diversas restrições. O estudo iniciou-se com uma revisão detalhada
da literatura sobre métodos de escalonamento de projetos, abordando tanto as técnicas exatas,
quanto as meta-heurísticas, de forma a fundamentar a escolha da técnica de otimização. De
seguida, foi desenvolvido um modelo matemático que incorpora as especificidades do
escalonamento de projetos com restrição de recursos. A implementação do protótipo foi
realizada com base nesse modelo, sendo testado em dados académicos para determinar a sua
eficácia. Os resultados obtidos demonstraram que o protótipo é capaz de criar cronogramas
eficazes e para além disso, apresentou uma flexibilidade notável, adaptando-se a diferentes
tipos de projetos e múltiplos projetos. A dissertação conclui que o uso de meta-heurísticas
como o Simulated Annealing oferece uma abordagem poderosa e eficaz para resolver
problemas complexos de escalonamento de projetos, proporcionando uma vantagem
substancial para as organizações que operam em ambientes dinâmicos e altamente restritos.
Contudo, o estudo reconhece algumas limitações, como a necessidade de validação adicional
em diferentes contextos industriais e a possibilidade de integrar outras meta-heurísticas e
métodos híbridos para melhorar o desempenho do framework proposto. Finalmente, são
sugeridos caminhos para futuras investigações, incluindo a aplicação do modelo em diferentes
setores, a adaptação do protótipo para suportar decisões em tempo real e a exploração de
novas técnicas de otimização que possam complementar o Simulated Annealing, ampliando
assim a aplicabilidade das soluções propostas.
In today’s rapidly evolving and competitive business environment, organizations must continuously innovate, leading to the development of new optimization techniques, methods, and tools to support decision-making. In project scheduling management, efficiency and effectiveness are crucial for organizational success, and the tools developed aim to enhance these two critical factors. This dissertation focuses on applying optimization techniques to project scheduling, with a particular emphasis on metaheuristics, specifically Simulated Annealing. To achieve this objective, a mathematical model and a prototype were developed to support project schedule management by optimizing resource allocation and minimizing execution times, especially in complex environments with multiple constraints. The study began with a comprehensive literature review on project scheduling methods, covering both exact techniques and metaheuristics, to substantiate the choice of the optimization technique. A mathematical model was then developed, incorporating the specific requirements of resourceconstrained project scheduling. The prototype was implemented based on this model and tested using academic data to assess its effectiveness. The results demonstrated that the prototype could generate effective schedules and exhibited remarkable flexibility, adapting to different types of projects and multi-project environments. The dissertation concludes that using metaheuristics, such as Simulated Annealing, provides a powerful and effective approach to solving complex project scheduling problems, offering significant advantages for organizations operating in dynamic and highly constrained environments. However, the study acknowledges limitations, such as the need for further validation across different industrial contexts and the potential to integrate other metaheuristics or hybrid methods to enhance the framework's performance. Finally, future research directions include applying the model in various sectors, adapting the prototype for real-time decision-making, and exploring new optimization techniques to complement Simulated Annealing, thereby expanding the applicability of the proposed solutions.
In today’s rapidly evolving and competitive business environment, organizations must continuously innovate, leading to the development of new optimization techniques, methods, and tools to support decision-making. In project scheduling management, efficiency and effectiveness are crucial for organizational success, and the tools developed aim to enhance these two critical factors. This dissertation focuses on applying optimization techniques to project scheduling, with a particular emphasis on metaheuristics, specifically Simulated Annealing. To achieve this objective, a mathematical model and a prototype were developed to support project schedule management by optimizing resource allocation and minimizing execution times, especially in complex environments with multiple constraints. The study began with a comprehensive literature review on project scheduling methods, covering both exact techniques and metaheuristics, to substantiate the choice of the optimization technique. A mathematical model was then developed, incorporating the specific requirements of resourceconstrained project scheduling. The prototype was implemented based on this model and tested using academic data to assess its effectiveness. The results demonstrated that the prototype could generate effective schedules and exhibited remarkable flexibility, adapting to different types of projects and multi-project environments. The dissertation concludes that using metaheuristics, such as Simulated Annealing, provides a powerful and effective approach to solving complex project scheduling problems, offering significant advantages for organizations operating in dynamic and highly constrained environments. However, the study acknowledges limitations, such as the need for further validation across different industrial contexts and the potential to integrate other metaheuristics or hybrid methods to enhance the framework's performance. Finally, future research directions include applying the model in various sectors, adapting the prototype for real-time decision-making, and exploring new optimization techniques to complement Simulated Annealing, thereby expanding the applicability of the proposed solutions.
Description
Keywords
Project management Project scheduling Multi-project scheduling Metaheuristics Simulated annealing Gestão de projetos Escalonamento de projetos Escalonamento multi-projeto Projetos Meta-heurísticas