This project is a continuous research of a topic well-known in the literature, namely, the performance study of unbalanced unpaced production system. In the literature, there were many studies that investigated the statistical outputs of an unbalanced production line using simulation. This project focuses on researching the outputs like average buffer level and idle time that are rarely studied in previous research by using optimization tools from discrete event simulation software FACTS.The models used in the article (Shaaban & McNamara, 2009) have been used as a guideline during the development of the simulation models for this project. Two simulation models were created, each using different discrete event simulation software, namely FACTS analyzer and Plant simulation. Those simulation models fulfills its role in verification & validation stage, with their statistical outputs compared to each other and with Shaaban and McNamara’s results. After verification & validation comes optimization of those simulation models, by using optimization tools from FACTS.The research area expanded during the optimization phase. Originally Shaaban et.al analyzed unbalanced production line with one fixed value of coefficient of variation. In order to expand the view on the properties of an unbalanced production line, three more coefficient variation were added with total of four in this project. As a result, 12 optimization results were created at the end of this project. Each optimization has 30 000 iterations to ensure its convergence.The first step of analysis is done by locating all Pareto-optimal solutions with optimization tools in FACTS. The raw data of all solutions are later transferred and converted into EXCEL files. Using scatter graph and putting all outputs against each other in EXCEL, it creates visual graph that can be used to analyze and to investigate interesting behavior in an unbalanced production line.The analysis on the optimization results showed several interesting behaviors from production line with different settings. One being that if a production line possess worse coefficient of variation than its competition. By raising the inter-stage buffer level of the production line with inferior coefficient of variation, it can achieve the same level, if not greater outputs than its competitor who possess better coefficient of variation. The other interesting behavior are optimization results with highest outputs in regard of either idle time or average buffer level, with deep analyzation using optimization tools from FACTS. Certain operation time pattern and inter-stage buffer pattern could be observed from those results.

A maioria das pesquisas na área de alocação de capacidades entre recursos de uma linha de manufatura propõe as formas balanceadas e em bowl como as mais eficientes para o desempenho de linha como um todo. A maior parte destes estudos é baseada em sistemas simplificados de empurrar a produção, desconsiderando sistemas mais atuais de planejamento e controle da produção (PCP). Por outro lado, estudos referentes à eficiência de sistemas de planejamento e controle da produção não consideram o efeito que critérios distintos de alocação de capacidades podem ter seus desempenhos. Este trabalho tem como objetivo estudar o relacionamento entre as políticas de alocação de capacidades e os sistemas de PCP. O principal critério de desempenho adotado foi o throughput, obtido segundo níveis médios e máximos de estoque em processo em uma linha de produção com cinco recursos. Foram estudados oito tipos de critérios de alocação de capacidades e quatro tipos de sistemas PCP, segundo três níveis de desbalanceamento de cargas, três níveis de coeficiente de variabilidade dos tempos de processamento dos recursos e cinco níveis máximos de estoque em processo. Foi utilizada uma ferramenta de simulação para criar modelos e simular 1386 cenários distintos. Como resultado, percebeu-se uma estreita interdependência entre políticas de alocação e sistemas de PCP. A pesquisa identificou; ainda, que não há um critério de alocação de capacidades nem um sistema de PCP que se mostre melhorem todas as condições testadas, contrariando diversos estudos sobre o tema. / Most of the researches on production capacity allocation among resources, proposes the use of balanced and bowI allocation as the most efficient methods in terms of performance. Such studies were generally based on simplified push production systems, not considering other production pIanning and controI systems (PPC). On the other hand, studies about efficiency of PPC systems don\'t consider the effect of different criteria of capacity allocation on the performance of the PPC systems. The purpose of this research is to investigate how different capacity allocation criteria and different PPC systems interreIate among each other. The major performance criteria used to rank each combination was the resulting throughput, considering several average and maximum levels of work in process (WIP) in a production line with five resources. Eight different types of capacity allocation criteria and four types of PPC systems were studied, with three levels of unbalanced loads, three levels of variability coefficient for processing times and tive maximum WIP levels. A simulation tool was used in order to generate the models and run 1386 different scenarios. As a result, it could be noticed a strong interrelationship between the allocation criteria and the PPC systems. The research also showed, on the contrary of many studies on this subject, that for all the combination tested, none of capacity allocation criteria nor PPC systems stood out on the best option.

A maioria das pesquisas na área de alocação de capacidades entre recursos de uma linha de manufatura propõe as formas balanceadas e em bowl como as mais eficientes para o desempenho de linha como um todo. A maior parte destes estudos é baseada em sistemas simplificados de empurrar a produção, desconsiderando sistemas mais atuais de planejamento e controle da produção (PCP). Por outro lado, estudos referentes à eficiência de sistemas de planejamento e controle da produção não consideram o efeito que critérios distintos de alocação de capacidades podem ter seus desempenhos. Este trabalho tem como objetivo estudar o relacionamento entre as políticas de alocação de capacidades e os sistemas de PCP. O principal critério de desempenho adotado foi o throughput, obtido segundo níveis médios e máximos de estoque em processo em uma linha de produção com cinco recursos. Foram estudados oito tipos de critérios de alocação de capacidades e quatro tipos de sistemas PCP, segundo três níveis de desbalanceamento de cargas, três níveis de coeficiente de variabilidade dos tempos de processamento dos recursos e cinco níveis máximos de estoque em processo. Foi utilizada uma ferramenta de simulação para criar modelos e simular 1386 cenários distintos. Como resultado, percebeu-se uma estreita interdependência entre políticas de alocação e sistemas de PCP. A pesquisa identificou; ainda, que não há um critério de alocação de capacidades nem um sistema de PCP que se mostre melhorem todas as condições testadas, contrariando diversos estudos sobre o tema. / Most of the researches on production capacity allocation among resources, proposes the use of balanced and bowI allocation as the most efficient methods in terms of performance. Such studies were generally based on simplified push production systems, not considering other production pIanning and controI systems (PPC). On the other hand, studies about efficiency of PPC systems don\'t consider the effect of different criteria of capacity allocation on the performance of the PPC systems. The purpose of this research is to investigate how different capacity allocation criteria and different PPC systems interreIate among each other. The major performance criteria used to rank each combination was the resulting throughput, considering several average and maximum levels of work in process (WIP) in a production line with five resources. Eight different types of capacity allocation criteria and four types of PPC systems were studied, with three levels of unbalanced loads, three levels of variability coefficient for processing times and tive maximum WIP levels. A simulation tool was used in order to generate the models and run 1386 different scenarios. As a result, it could be noticed a strong interrelationship between the allocation criteria and the PPC systems. The research also showed, on the contrary of many studies on this subject, that for all the combination tested, none of capacity allocation criteria nor PPC systems stood out on the best option.