Balancing and sequencing of an automotive assembly line through mixed integer linear programming

• Ano de defesa: 2023
• Autor(a) principal: Marqueze, Thiago Tristão
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial; UTFPR
• Palavras-chave em Português: Balanceamento de linha de montagem; Programação linear; Produtividade industrial; Programação (Matemática); Modelos matemáticos; Indústria automobilística; Assembly-line balancing; Linear programming; Industrial productivity; Programming (Mathematics); Mathematical models; Automobile industry and trade; CNPQ::ENGENHARIAS; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/33291

Companies are constantly looking for ways to improve their productivity and profits. Furthermore, assembly lines must be flexible and produce different products on the same production line to compete in the current market, in which customization is rising. Line balancing is a tool that can increase production and reduce idle time for operators and workstations. As balancing requires changing the allocation of tasks between stations, it is, in practical terms, considered a medium or long-term decision. In a complementary way, production sequencing is considered a short-term decision; it is usually applied to a given known distribution of tasks across stations, and defines the order in which product models will be produced for a relatively short analysis horizon (some hours or days, depending on the production line). This study aims to balance a real vehicle assembly line in the automotive industry. In addition, balance and sequence a theoretical problem inspired by the real line. Mixed Integer Linear Programming is proposed to technically assist in balancing and sequencing tasks. The work initially reviews bibliographic concepts relevant to the topic. Then, the real problem is described, showing the different models produced on the line, their work contents, and the precedence diagram between tasks. A resolution methodology was proposed with three different analyses, each with its mathematical model. These analyses were developed as the problem’s characteristics were deepened. Analysis 1 sought to find the balance of tasks for eight different production scenarios (productive mix) observed during six months of practical production. Analysis 1 found a solution with a 10.1% improvement in cycle time but failed to meet the demand of a specific mix. Next, Analysis 2 aimed to balance each specific mix, then added a variable to limit the number of changes in balance and find a solution resilient to changes in demand, obtaining a 12.0% reduction in cycle time. This analysis met all eight productive mixes present in the six-month period, was validated by the line specialist, and was implemented. Finally, Analysis 3 explored the possibility of performing balancing and sequencing simultaneously in a theoretical problem inspired by the considered problem. Once again, it is sought to find a single solution capable of meeting different demands (productive mix), with a 17.1% reduction in the steady-state cycle time in relation to the initial state of the line. Analysis 3 shows that carrying out balancing and sequencing in an integrated approach tends to provide results that are more aligned with the productive potential of the line considered.