Planejamento e programação da produção em um ambiente job shop com pedidos sob encomenda

• Autor(a) principal: Souza, Rafael Querino de
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/16452
• topic: Job shop flexível; Programação Inteira Mista; Dimensionamento e sequenciamento de lotes; Indústria de Máquinas e Equipamentos; Usinagem; MIP; Flexible Job Shop; Lot sizing and scheduling; Machinery industry; Machining; ENGENHARIAS::ENGENHARIA DE PRODUCAO::PESQUISA OPERACIONAL

Production planning and scheduling of machined-to-order parts in a job shop involves lot sizing and scheduling decisions. In this environment, there are several machines capable to execute various operations. Several ordered operations are required to transform raw material into each product and there is a possibility to produce a large number of products, which implies on machine setup and, consequently, reducing the available operational time. This study attempts to develop a model to assist in making decisions like lot sizing, allocation of operations in each machine for each lot and operations sequencing in each machine in an integrated approach in a flexible job shop environment that makes to order. The main contributions are the development and validation of mathematical model that represents the problem. Besides that, the review of production planning and scheduling models in job shop environment and the description of the problem in the case of machinery and equipment industry. Three formulations that integrate the decisions are analyzed and the one that achieves the optimal solution in the shortest time using CPLEX algorithm was chosen. Results indicate that the chosen model presents a feasible production plan and can be further applied in the machining sector. In a dynamic production environment, it might be necessary to update the production planning frequently, therefore a relax-and-fix algorithm is used to find an initial solution, which is further improved by a fix-and-optimize algorithm.