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Auswahl der wissenschaftlichen Literatur zum Thema „Teoretická kapacita“
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Zeitschriftenartikel zum Thema "Teoretická kapacita"
Brandtner, Michal, und Adam Boháček. „DATOVÉ STRUKTURY PRO Ú?ELY LCA S VYUŽITÍM BIM: REŠERŠE A TEORETICKÁ VÝCHODISKA“. Czech Journal of Civil Engineering 6, Nr. 2 (31.01.2021): 21–27. http://dx.doi.org/10.51704/cjce.2020.vol6.iss2.pp21-27.
Der volle Inhalt der QuelleHampejs, Tomáš. „Náboženství v laboratoři sociální implicitní kognice: víra v automatických reakcích jako implicitní postoj a dovednost“. Sociální studia / Social Studies 10, Nr. 2 (24.04.2013): 85–114. http://dx.doi.org/10.5817/soc2013-2-85.
Der volle Inhalt der QuelleDvořák, Dominik, Karel Starý und Petr Urbánek. „Malá škola po pěti letech: proměny školy v době reformy“. Pedagogická orientace 25, Nr. 1 (28.02.2015): 9–31. http://dx.doi.org/10.5817/pedor2015-1-9.
Der volle Inhalt der QuelleTopsø Larsen, Karin, Rikke Brandt Broegaard und Lene Havtorn Larsen. „Translokalt engagement i landdistrikterne: Den mobile elites nye legeplads?“ Dansk Sociologi 30, Nr. 4 (16.04.2020): 59–79. http://dx.doi.org/10.22439/dansoc.v30i4.6232.
Der volle Inhalt der QuelleSkov Henriksen, Lars. „Tillid – et spørgsmål om moral?“ Dansk Sociologi 22, Nr. 2 (05.07.2011): 47–63. http://dx.doi.org/10.22439/dansoc.v22i2.3560.
Der volle Inhalt der QuelleDissertationen zum Thema "Teoretická kapacita"
Šátek, Dominik. „Teplotní závislost kapacity negativní elektrody pro sodno – iontové akumulátory“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442527.
Der volle Inhalt der QuelleEINARSSON, JOHANNA, und HELENA SÖDERLUND. „Prognostisering av produktionskapacitet - En studie på PET-Turbuhaler, AstraZeneca“. Thesis, KTH, Industriell produktion, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192899.
Der volle Inhalt der QuelleA company needs an accurate capacity plan to become successful. The capacity plan is an important tool for planning and anticipating production which is essential to be able to meet future demands. It is therefore of great importance to get an accurate forecasting of the production capacity, which is the main topic of this report. During the spring semester 2016, the authors of this report were contacted by the production unit PET-Turbuhaler at AstraZeneca in Södertälje. PET-Turbuhaler requested an accurate model for the long term, 12-24 months, forecasting of their production capacity. From this problem, a research question has been formulated into; Which is the best way for PET-Turbuhaler to work to reach an accurate long term, 12-24 months, production capacity prognosis? A pre-study, a literature study and an internal and an external benchmarking were conducted in order to answer the research question. The result from these were afterwards compiled and analyzed. The pre-study at PET-Turbuhaler gave an overview of the work with the current Microsoft Excel-model and its associated problems. The pre-study did also consist of the authors’ own analysis of PET-Turbuhalers capacity model. The literature study was made to investigate how theory advocates the work with capacity forecasting. It showed a difference between theoretical and real capacity. The real capacity is calculated by subtracting the plant’s scheduled and nonscheduled capacity losses (such as time losses for lunch, meetings, set-ups, machine breakdowns and defects from production) from the theoretical capacity. The theoretical capacity of the plant is the capacity when the plant runs 24 hours a day every day of the year. The analysis showed that the current model PET-Turbuhaler use today consist of more or less the same parameters as the literature suggests. The authors could therefore realize that the current model is not necessarily the main problem at this stage. The biggest problem is rather how the current model is being used by the employees. Parameters within the current model are not continuously updated with right data as PET-Turbuhaler thought. The consequence of this is that the long term forecasting is based on out-of-date data even though new and more accurate data is available. The research question can be answered based on the analysis. The most interesting result was the insight that the short term forecasting is not as accurate as PET-Turbuhaler believed. This gives, in order to achieve a good long term forecasting, that PET-Turbuhaler must first improve their short term forecasting by establishing a standardized way of working with the model. Only then can the long term forecasting be accurate. Through discussions regarding the result the authors were able to suggest improvements on how PET-Turbuhaler could work to reach an accurate long term forecast of their production capacity prognosis. The recommendations include continuous evaluation of collected data, regular meetings between production support and production line managers and the benefit of using a sensor, in the end of the production line, to registrate the output rate.
Bárta, Tomáš. „Informačně-teoretické vlastnosti vybraných stochastických neuronálních modelů“. Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-382919.
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