Uma abordagem estocástica para aumento de produtividade em linhas de montagem: o problema de balanceamento de produção
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Data
2018-08-27
Autores
Souza, Yuri Prado
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Universidade Estadual Paulista (Unesp)
Resumo
Neste trabalho propõe-se uma abordagem para o Problema de Balanceamento de Linhas de Montagem (do inglês, Assembly Line Balancing Problem - ALBP) para aumentar a eficiência de uma indústria montadora de veículos. O ALBP caracteriza-se como um problema de sequenciamento de tarefas em estações de trabalho classificado como um problema de Otimização Combinatória NP-difícil e, portanto, a solução exata do problema em ambientes reais geralmente implica em elevado custo computacional. Para resolver o ALBP, foram formulados um modelo matemático de otimização inteira mista para obtenção de soluções determinísticas e um modelo estocástico com recurso que considera a incerteza dos tempos de execução das tarefas pelos operadores. A motivação para o desenvolvimento do presente trabalho decorre da observação de interrupções constantes do fluxo de produção nesta indústria, atribuídas às mais diversas naturezas, e que causavam transtornos e elevados níveis de estresse aos trabalhadores. Ambos os modelos, determinístico e estocástico, aumentaram a capacidade de produção de 196 unidades/dia para 245 e 233 unidades/dia, respectivamente. O modelo estocástico aumentou o tempo de ciclo CT em 5,6% quando comparado ao modelo determinístico, embora diminua a capacidade efetiva em 4,8% Porém, não considerar a incerteza no tempo de execução das tarefas pode diminuir a quantidade produzida em até 10,6%. Contrariamente ao entendimento comum em linhas de montagem, este trabalho conclui que reduzir os tempos de ociosidade aos níveis mínimos é prejudicial à produtividade de linhas de montagem. Isto se deve ao fato de que uma parcela do tempo atribuído à ociosidade dos operadores, na verdade contêm um tempo adicional gerado pela incerteza do tempo de execução das tarefas. Os resultados sugerem que a abordagem do ALBP sob incerteza contribui para o aumento dos índices de capacidade operacional da empresa. Devido ao grande esforço computacional necessário para a solução dos modelos de otimização propostos (determinístico e estocástico), não se consegue resolver, em um tempo computacional razoável, exemplares de dimensões reais do problema. Em vista disto, o trabalho propõe também uma heurística para a solução do ALBP visando minimizar o tempo de ciclo. Experimentos computacionais sugerem que a heurística proposta obtém resultados razoáveis para grandes exemplares do problema em um tempo computacional pequeno
This work proposes solution approaches to the Assembly Line Balancing Problem (ALBP) to increase the efficiency of a vehicle assembler industry. The ALBP is characterized as a task sequencing in workstations which is classified as a NP-hard Combinatorial Optimization problem and, therefore, the exact solution of the problem in real environments usually implies a high computational cost. In order to solve the ALBP, a mathematical model of mixed integer optimization to obtain deterministic solutions and a stochastic model with resource that considers the uncertainty of the execution times of the tasks by the operators were formulated. The motivation for the development of this work stems from the constant interruptions of the production flow in this industry, attributed to the most diverse natures, which cause disorders and high levels of stress to the workers. The deterministic and stochastic models increased the production capacity from 196 units / day to 245 and 233 units / day, respectively. The stochastic model increased the cycle time by 5.6% when compared to the deterministic model, although it reduced the effective capacity by 4.8%, which is equivalent to 12 vehicles / day. However, not considering the uncertainty in task execution times can decrease the amount produced by up to 10.6% or 26 vehicles / day. Contrary to the most acceptable idea, this work concludes that reducing idle times to minimum levels is detrimental to assembly line productivity. This is due to the fact that a portion of the time attributed to the idleness of the operators actually contains an additional time generated by the uncertainty of the execution time of the tasks. The results suggest that the approach of the ALBP under uncertainty contributes to the increase of the indices of operational capacity of the company. Due to the great computational effort required to solve the proposed optimization models (deterministic and stochastic), it is not possible to solve real instances of the problem in a reasonable computational time. In view of this, this work also proposes a heuristic for the ALBP solution in order to minimize the cycle time. Computational experiments suggest that the proposed heuristic obtains reasonable results for large instances of the problem in a small computational time
This work proposes solution approaches to the Assembly Line Balancing Problem (ALBP) to increase the efficiency of a vehicle assembler industry. The ALBP is characterized as a task sequencing in workstations which is classified as a NP-hard Combinatorial Optimization problem and, therefore, the exact solution of the problem in real environments usually implies a high computational cost. In order to solve the ALBP, a mathematical model of mixed integer optimization to obtain deterministic solutions and a stochastic model with resource that considers the uncertainty of the execution times of the tasks by the operators were formulated. The motivation for the development of this work stems from the constant interruptions of the production flow in this industry, attributed to the most diverse natures, which cause disorders and high levels of stress to the workers. The deterministic and stochastic models increased the production capacity from 196 units / day to 245 and 233 units / day, respectively. The stochastic model increased the cycle time by 5.6% when compared to the deterministic model, although it reduced the effective capacity by 4.8%, which is equivalent to 12 vehicles / day. However, not considering the uncertainty in task execution times can decrease the amount produced by up to 10.6% or 26 vehicles / day. Contrary to the most acceptable idea, this work concludes that reducing idle times to minimum levels is detrimental to assembly line productivity. This is due to the fact that a portion of the time attributed to the idleness of the operators actually contains an additional time generated by the uncertainty of the execution time of the tasks. The results suggest that the approach of the ALBP under uncertainty contributes to the increase of the indices of operational capacity of the company. Due to the great computational effort required to solve the proposed optimization models (deterministic and stochastic), it is not possible to solve real instances of the problem in a reasonable computational time. In view of this, this work also proposes a heuristic for the ALBP solution in order to minimize the cycle time. Computational experiments suggest that the proposed heuristic obtains reasonable results for large instances of the problem in a small computational time
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Palavras-chave
Problema de Balanceamento de Linhas de Montagem, Programação Inteira Mista, Programação Estocástica, Modelo estocástico com recurso, Heurística, Assembly line balancing problem, Mixed integer programming, Stochastic programming, Stochastic model with recourse, Heuristic, Métodos de linha de montagem