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Abstract(s)
A otimização dos processos numa empresa torna-se cada vez mais essencial no crescimento de uma
empresa. Para enfrentar a concorrência, é indispensável fazer um planeamento dos processos que
a empresa realiza.
Este trabalho incidiu no estudo do Planeamento Agregado da Produção com o objetivo de otimizar
o sistema produtivo de uma empresa. O princípio do (PAP) é ajustar a capacidade de produção à
previsão da procura. Este método permite determinar: níveis de produção e stock, contratações e
demissões de funcionários, número de horas extras, atrasos e satisfação da procura de modo a
atingir os menores custos possíveis.
Para o decorrer deste trabalho, é pretendido apresentar e desenvolver um modelo matemático que
consiga atender a todas as necessidades do processo produtivo, considerando todos os aspetos
desde a produtividade do pessoal até ao produto ou serviço. Foi desenvolvido um protótipo de PAP
que permite obter a melhor solução de produção considerando as condições impostas.
Posteriormente, o protótipo desenvolvido de PAP foi testado e comparado com outros métodos de
planeamento como o Nivelamento pela média, o Excesso e capacidade e a Adaptação á procura, de
modo a determinar qual o método apresenta os melhores resultados. Todos os métodos foram
testados através com valores aleatórios. Dos testes efetuados, conclui-se que o modelo
matemático desenvolvido apresenta a melhor solução, no entanto, uma ótima alternativa a este
método é o Nivelamento pela Média que apresentou bons resultados. Para confirmar os resultados
foram realizados testes estáticos, que permitiram concluir que existe uma diferença significativa
entre as médias dos grupos uma vez que o valor p (<,001) é muito inferior ao valor de 𝛼 de 0,05.
Dos testes realizados, destaca-se o protótipo PAP que para além de ter um p-value muito inferior a
0,05 apresenta a melhor média entre os grupos.
Optimizing a company's processes is becoming increasingly essential for its growth. In order to face up to the competition, it is essential to plan the processes that the company carries out. This work focused on the study of Aggregate Production Planning with the aim of optimizing a company's production system. The principle of PAP is to adjust production capacity to forecast demand. This method makes it possible to determine: production and stock levels, hiring and firing of employees, number of overtime hours, delays and meeting demand in order to achieve the lowest possible costs. The aim of this work is to present and develop a mathematical model that can meet all the needs of the production process, considering all aspects from staff productivity to the product or service. A PAP prototype has been developed that allows the best production solution to be obtained considering the conditions imposed. Subsequently, the developed PAP prototype was tested and compared with other planning methods such as Average Leveling, Excess Capacity and Adaptation to Demand, in order to determine which method gives the best results. All the methods were tested using random values. From the tests carried out, it can be concluded that the mathematical model developed presents the best solution; however, an excellent alternative to this method is Leveling by Average, which showed good results. To confirm the results, static tests were carried out, which concluded that there is a significant difference between the means of the groups since the p-value (<.001) is much lower than the α-value of 0.05. Of the tests carried out, the PAP prototype stands out as having a p-value much lower than 0.05 and the best average between the groups.
Optimizing a company's processes is becoming increasingly essential for its growth. In order to face up to the competition, it is essential to plan the processes that the company carries out. This work focused on the study of Aggregate Production Planning with the aim of optimizing a company's production system. The principle of PAP is to adjust production capacity to forecast demand. This method makes it possible to determine: production and stock levels, hiring and firing of employees, number of overtime hours, delays and meeting demand in order to achieve the lowest possible costs. The aim of this work is to present and develop a mathematical model that can meet all the needs of the production process, considering all aspects from staff productivity to the product or service. A PAP prototype has been developed that allows the best production solution to be obtained considering the conditions imposed. Subsequently, the developed PAP prototype was tested and compared with other planning methods such as Average Leveling, Excess Capacity and Adaptation to Demand, in order to determine which method gives the best results. All the methods were tested using random values. From the tests carried out, it can be concluded that the mathematical model developed presents the best solution; however, an excellent alternative to this method is Leveling by Average, which showed good results. To confirm the results, static tests were carried out, which concluded that there is a significant difference between the means of the groups since the p-value (<.001) is much lower than the α-value of 0.05. Of the tests carried out, the PAP prototype stands out as having a p-value much lower than 0.05 and the best average between the groups.
Description
Keywords
Aggregate Production Planning (APP) Mixed Integer Linear Programming Optimization Excel ANOVA