Categorised grouping: a framework for industry applications

• Ano de defesa: 2025
• Autor(a) principal: Mello, Rafael Granza de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/33523/001300000wfjv
• Idioma: eng
• Palavras-chave em Português: Matching; Agrupamento; Algoritmo; Framework; Otimização
• Link de acesso: https://repositorio.udesc.br/handle/UDESC/23505

Matching problems are critical in various industrial applications, such as task allocation, scheduling, and resource distribution. However, existing optimization solutions are often complex, rigid, or inaccessible to professionals without specialized expertise. To mitigate this issue, this dissertation proposes the design of a flexible model to define and solve matching and grouping problems, enabling users to configure relational data and optimization constraints without requiring deep knowledge of optimization techniques. The proposed method operates on the application's ORM (Object-Relational Mapping) model, which enhances the method's adoption given the current widespread use of ORM in the industry. In addition to the model design, this work presents a prototype implementation and introduces an innovative matching algorithm under quota constraints. This algorithm ensures fairness and feasibility in resource allocation scenarios where quotas play a crucial role. By bridging the gap between theoretical matching models and their practical applications, this research offers a structured yet adaptable approach to solving real-world matching problems.