20.500.12556/RUL-111716 Optimizacija proizvodnje z uporabo avtomatsko vodenih vozil Production process optimization using automated guided vehicles Trendi industrije vse bolj stremijo k vpeljavi konceptov, povezanih s tovarno 4.0. Temeljijo na uporabi novih tehnologij, kot so internet stvari, kibernetsko-stvarni sistemi in analitika velepodatkov. Soočanje z izzivi uvedbe teh konceptov in tehnologij postaja ključno za ohranitev konkurenčnosti podjetij na trgu. V tem delu prikažemo implementacijo nekaterih idej tovarne 4.0. Najprej predstavimo optimizacijo proizvodnega procesa z uporabo avtomatsko vodenih vozil. S pomočjo simulacije proizvodnega procesa poiščemo optimalne parametre pri njihovi vpeljavi in ugotovimo, da potrebujemo štiri vozila, s čimer bi nadomestili dva delavca v proizvodnji. Ekonomska analiza pokaže, da bi se investicija povrnila v štirih letih. Naslednji cilj dela je avtomatizirati razporejanje delavcev na stroje. Problem rešimo s kombinacijo požrešnega in genetskega algoritma ter dobimo boljše rezultate v primerjavi z ročno razporeditvijo. Nato predstavimo možno uporabo koncepta digitalnega dvojčka v procesu proizvodnje. Uporabimo tehnike strojnega učenja za napovedovanje delovanja strojev, s čimer bi lahko preventivno identificirali napake v delovanju. Industry is increasingly striving to introduce concepts related to Industry 4.0. They are based on the new technologies, such as the Internet of Things, the cyber-physical systems and the big data analytics. Facing the challenges of introducing these concepts and technologies is becoming key to maintain the competitiveness of companies on the market. In the thesis we show the implementation of some aspects of Industry 4.0. First, we introduce the optimization of the production process using the automated guided vehicles. By simulating the production process, we find that we need four vehicles to replace two production workers. Economic analysis reveals that the investment would return in four years. The next goal of the thesis is to automate the assignment of machines to workers. We solve the problem with a combination of a greedy and a genetic algorithm and get better results compared to the manual assignment. We also show one of the possible uses of the digital twin concept in the production process. With the use of the machine learning we predict the performance of machines, which could allow us to preventively identify malfunctions. optimizacija procesa proizvodnje tovarna 4.0 simulacija proizvodnje avtomatsko vodena vozila strojno učenje genetski algoritem digitalni dvojček production process optimization industry 4.0 production simulation automated guided vehicles machine learning genetic algorithm digital twin true false false Slovenski jezik Angleški jezik Magistrsko delo/naloga 2019-10-10 12:35:00 2019-10-10 12:35:04 2022-08-23 03:44:53 0000-00-00 00:00:00 2019 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 22761 1538415811 Solar_Rok_-_Optimizacija_proizvodnje_z_uporabo_avtomatsko_vodenih_vozil.pdf Solar_Rok_-_Optimizacija_proizvodnje_z_uporabo_avtomatsko_vodenih_vozil.pdf 1 7808C7443E3042D1C9E1D90B0B5EE76C 36b629aec6b9376c944d6da18eafca6d62d94498f0b0a1e34904cdb08f11c010 76a4867c-a1b7-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=123614 Fakulteta za računalništvo in informatiko 0 0 0