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Process and Production as a Part of Logistic Activities (CROSBI ID 605761)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Ćosić, Predrag ; Lisjak, Dragutin ; Milčić, Diana Process and Production as a Part of Logistic Activities // VII Scientific Conference International Mechanical Engineering, COMEC 2012 / Angel Silvio Machado Rodriguez (ur.). Central University of Las Villas, Cuba and the University Ottovon- Guericke of Magdeburg, Germany, 2012. str. x1-x2

Podaci o odgovornosti

Ćosić, Predrag ; Lisjak, Dragutin ; Milčić, Diana

engleski

Process and Production as a Part of Logistic Activities

Classification consideration of the product shape and process sequencing are important conditions for designing a general model for the estimation of production times. In fact, it means development of a technological knowledge base. As a result of our analysis, we have created eight regression equations with the obtained index of determination, with the most important independent variables different for 2D and 3D model. The observed level of subjectivity, constraints and errors were the reasons to use neural networks as the second approach to estimate production time. The survival and growth of businesses in today's market is based on constant innovation and new product development. Business innovation as other approach must take place at all levels, from products, and processes to the organization itself, in order to bring about improvements in competitiveness and business efficiency. We can respond to such demands and manage the appropriate tools to simulate the activities and processes within the factory in the virtual world. The simulation (as introduction to the logistic activities) of the entire flow of materials, including all significant activities of production, storage and transportation are key requirements for quality production planning and necessary manufacturing processes. Discrete simulations, i.e. the applications used for running this type of simulation provide the possibility of creating different scenarios concerning cases of different production parameters, the burden of production capacities, as well as their delays and failures. The simulation model was improved by genetic algorithm for the purpose of minimizing costs and delivery times of products.

process planning; stepwise linear multiple regression; production time; neural network

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Podaci o prilogu

x1-x2.

2012.

objavljeno

Podaci o matičnoj publikaciji

VII Scientific Conference International Mechanical Engineering, COMEC 2012

Angel Silvio Machado Rodriguez

Central University of Las Villas, Cuba and the University Ottovon- Guericke of Magdeburg, Germany

Podaci o skupu

VII Scientific Conference International Mechanical Engineering, COMEC 2012

predavanje

05.11.2012-07.11.2012

Santa Maria del Mar, Kuba

Povezanost rada

Strojarstvo