Thomas Sobottka
@fraunhofer.at
Fraunhofer Austria Research GmbH
EDUCATION
TU Wien: Vienna, AT (Dr. techn. | PhD)
University of Rostock: Rostock, DE (. | MSc)
RESEARCH, TEACHING, or OTHER INTERESTS
Industrial and Manufacturing Engineering, General Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Maximilian Nowak, Stephan Martineau, Thomas Sobottka, Fazel Ansari, and Sebastian Schlund
Elsevier BV
Leonhard Czarnetzki, Catherine Laflamme, Christoph Halbwidl, Lisa Charlotte Günther, Thomas Sobottka, and Daniel Bachlechner
Springer Nature Switzerland
Wolfgang Dummer, Alexander Gaal, Thomas Sobottka, and Fazel Ansari
Springer Nature Switzerland
Alexander Schmid, Felix Kamhuber, Thomas Sobottka, and Wilfried Sihn
University North
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-based material planning, and are an essential lever for efficient inventory and order calculation. Despite their acknowledged potential, digital tools for optimized demand calculation are still lacking in practice. Thus, the goal of the presented approach to provide an applicationoriented method to optimally select and parametrize state-of-the-art forecasting methods, based on product-specific demand data. In this approach, a rule-based selection heuristic is combined with static simulation of demand time-series and a metaheuristics-based optimization of forecasting parameters, to provide automatically optimized article-specific demand forecasts. Case studies for two companies in the capital goods industry evaluate and quantify the application potential. The results point to significantly improved, itemspecific demand planning
Julian Perwitz, Thomas Sobottka, Jan-Niklas Beicher, and Alexander Gaal
Elsevier BV
Christoph Halbwidl, Thomas Sobottka, Alexander Gaal, and Wilfried Sihn
Elsevier BV
Felix Kamhuber, Thomas Sobottka, Bernhard Heinzl, Jan Henjes, and Wilfried Sihn
Elsevier BV
Thomas Sobottka, Felix Kamhuber, and Bernhard Heinzl
MDPI AG
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 major requirement for production enterprises, especially for energy intensive production sectors such as casting. Despite the significant energy-efficiency potential through optimized planning and the acknowledged application potential for sophisticated simulation-based methods, digital tools for practical planning applications are still lacking. The authors develop a planning method featuring a hybrid (discrete-continuous) simulation-based multi-criteria optimization (a multi-stage hybrid heuristic and metaheuristic method) for a metal casting manufacturer and apply it to a heat treatment process, that requires order batching and sequencing/scheduling on parallel machines, considering complex restrictions. The results show a ~10% global goal optimization potential, including traditional business goals and energy efficiency, with a ~6% energy optimization. A basic feasibility demonstration of applying the method to synchronize energy demand with fluctuating supply by considering flexible energy prices is conducted. The method is designed to be included in the planning loop of metal casting companies: receiving orders, machine availability, temperature data and (optional) current energy market price-data as input and returning an optimized plan to the production-IT systems for implementation.
Felix Kamhuber, Thomas Sobottka, Bernhard Heinzl, and Wilfried Sihn
IEEE
This contribution introduces an innovative holistic multi-objective simheuristic approach for advanced production planning on rolling horizon basis for an European industrial food manufacturer. The optimization combines an efficient heuristic mixed-integer optimization, followed by a customized Simulated Annealing algorithm. State-of-the-Art multi-objective solution techniques fail to address highly fluctuating demands in a suitable way. Due to the lack of modelling details, as well as dynamic constraints, these methods are unable to adapt to seasonal (off-) peaks in demand and to consider resource adjustments. Our approach features dynamic capacity and stock-level restrictions, which are evaluated by an integrated simulation module, as well as a statistical explorative data analysis. In addition to a smoothed production, mid-term stock levels, setup-costs and the expected utilization of downstream equipment are optimized simultaneously. The results show a ~ 30 to 40% reduced output variation rate, thus yielding an equally reduced requirement for downstream equipment.
Thomas Sobottka, Felix Kamhuber, Mohammadali Faezirad, and Wilfried Sihn
Elsevier BV
Thomas Sobottka, Felix Kamhuber, Matthias Rössler, and Wilfried Sihn
Elsevier BV
Wilfried Sihn, Thomas Sobottka, Bernhard Heinzl, and Felix Kamhuber
Elsevier BV
Thomas Sobottka, Felix Kamhuber, and Wilfried Sihn
Walter de Gruyter GmbH
Kurzfassung Energieeffizienz ist aufgrund des zunehmenden gesellschaftlich-politischen Drucks, langfristig steigender Energiepreise sowie der Energiewende und ihrer Implikationen zu einem wichtigen Bestandteil des Zielsystems produzierender Unternehmen geworden. Großes Potenzial liegt darin, das Ziel der Energieeffizienz mittels der Produktionsplanung und -steuerung (PPS) zu verfolgen – allerdings fehlen den Unternehmen dafür aktuell die Planungswerkzeuge. Im folgenden Beitrag wird eine Planungsmethode vorgestellt, die genau hier Abhilfe schaffen soll: Mit einem neuartigen Simulator werden das Materialfluss- und Energiesystem eines Produktionssystems integriert abgebildet und mittels einer multikriteriellen Optimierung Produktionspläne erstellt, wobei auch die Anlagen-Steuerung in der Peripherie optimiert wird. Eine Industrie-Fallstudie in der Lebensmittelproduktion wird vorgestellt, in der ein erhebliches Optimierungspotenzial erzielt werden konnte.
Thomas Sobottka, Felix Kamhuber, Jan Henjes, and Wilfried Sihn
IEEE
This paper introduces a practical approach for the comprehensive simulation based planning and optimization of the production and logistics of a discrete goods manufacturer. Although simulation and optimization are well-established planning aides in production and logistics, their actual application in the field is still scarce, especially in small and medium-sized enterprises (SMEs). This is largely due to the complexity of the planning task and lack of practically applicable approaches for real-life planning scenarios. This paper provides a case study from the food industry, featuring a comprehensive planning approach based on simulation and optimization. The approach utilizes an offline-coupled multilevel simulation to smooth production and logistics planning via optimization, to optimally configure the production system using discrete-event simulation and to optimize the logistics network utilizing an agent-based simulation. The connected simulation and optimization modules can enhance the production logistics significantly, potentially providing a reference approach for similar industry applications.
Thomas Sobottka, Felix Kamhuber, and Wilfried Sihn
Elsevier BV
Thomas Sobottka, Thomas Edtmayr, and Wilfried Sihn
Walter de Gruyter GmbH
Kurzfassung Kleinladungsträger (KLT) sind in der modernen Produktion vieler Branchen nicht wegzudenken. Doch die weite Verbreitung und die damit verbundenen Stückzahlen kontrastieren mit der geringen Aufmerksamkeit, die dem Thema des Behältermanagements gewidmet wird: intransparente Materialströme, ineffiziente Reinigung und überhöhter Transportaufwand sind die Regel. Aus einem Forschungsprojekt wurde von Fraunhofer Austria daher ein Konzept zur effizienteren Behälterbewirtschaftung entwickelt, das bedarfsgerechte Reinigung und technologische Prozessunterstützung für Mehrwegbehälterkreisläufe einführt.
Dávid Gyulai, András Pfeiffer, Thomas Sobottka, and József Váncza
Elsevier BV