Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Thomas Sobottka"'
Publikováno v:
Tehnički Glasnik, Vol 16, Iss 3, Pp 328-335 (2022)
This paper presents a digital material planning approach, utilizing simulation-based optimization to select and parametrize article specific demand forecasting methods. Demand forecasts are the basis of material requirements planning in consumption-
Externí odkaz:
https://doaj.org/article/d038377369bb42e5a69e0140fd43cf1a
Publikováno v:
Journal of Manufacturing and Materials Processing, Vol 4, Iss 3, p 94 (2020)
This paper presents the development and evaluation of a digital method for multi-criteria optimized production planning and control of production equipment in a case-study of an Austrian metal casting manufacturer. Increased energy efficiency is a ma
Externí odkaz:
https://doaj.org/article/24bb5cc99a0f4565b452881ea80ed522
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81b1b1cd7b6efd65e89d10b5fe1655e1
https://doi.org/10.2139/ssrn.4458212
https://doi.org/10.2139/ssrn.4458212
Publikováno v:
Procedia CIRP. 104:1221-1226
This paper investigates the feasibility and performance of Deep Reinforcement Learning (RL) as a method for optimizing assembly cell configurations in adaptable cell-oriented assembly systems (ACAS). ACAS can be as productive as conventional assembly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::969e948bf010172bba619ad7be1b30d2
https://doi.org/10.1016/j.procir.2021.11.205
https://doi.org/10.1016/j.procir.2021.11.205
Publikováno v:
ASIM SST 2022 Proceedings Langbeiträge.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3b2f1826111e0176cc9a6205a3d5914
https://doi.org/10.11128/arep.20.a2017
https://doi.org/10.11128/arep.20.a2017
Publikováno v:
Procedia Manufacturing. 39:1844-1853
Advanced production planning and scheduling approaches increasingly rely on simulation-based optimization methods. This entails the problem of a high computational effort due to complex models, resulting in limitations for the practical application o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1480dbb30ba4d1407906df005aa9ea44
https://doi.org/10.1016/j.promfg.2020.01.254
https://doi.org/10.1016/j.promfg.2020.01.254
Publikováno v:
Procedia CIRP. 61:440-445
This research aims to develop a novel planning tool able to increase both the energy efficiency and general performance of production systems using a hybrid-simulation based, multi-criteria optimization, with this particular paper focusing on the opt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d9a12f1d41030f1586481a873dac572
https://doi.org/10.1016/j.procir.2016.11.151
https://doi.org/10.1016/j.procir.2016.11.151
Publikováno v:
Computers & Industrial Engineering. 147:106620
This paper introduces an innovative method for multi-objective optimization-based production planning with a rolling horizon for food manufacturing. It features a combination of a heuristic mixed-integer optimization and a metaheuristic optimization.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64a626212e6bfcd6f10d93654be170c1
https://doi.org/10.1016/j.cie.2020.106620
https://doi.org/10.1016/j.cie.2020.106620
This presented research comprises the development of an optimization module for use in a novel production optimization tool – similar in function but not mode of operation to an Advanced Planning System –, with energy efficiency incorporated into
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94aec3e4900716267427d4254fe75f7b
https://publica.fraunhofer.de/handle/publica/252852
https://publica.fraunhofer.de/handle/publica/252852
Publikováno v:
2017 Winter Simulation Conference (WSC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0525a5119bb5244c606495f9d8b0178
https://doi.org/10.1109/wsc.2017.8248064
https://doi.org/10.1109/wsc.2017.8248064