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The road construction industry is one which highly depends on just-in-time supply chains. The production planning and control on a building site is a complex process. In practice the planning is conducted well in advance so that the control function is limited by supervising the execution of the plan. The deviation between a predefined plan and its real-world execution is a common problem in the road construction industry. This master's thesis approaches this problem by developing a software component that executes the production planning as well as the production control ad-hoc. To build a…mehr

Produktbeschreibung
The road construction industry is one which highly depends on just-in-time supply chains. The production planning and control on a building site is a complex process. In practice the planning is conducted well in advance so that the control function is limited by supervising the execution of the plan. The deviation between a predefined plan and its real-world execution is a common problem in the road construction industry. This master's thesis approaches this problem by developing a software component that executes the production planning as well as the production control ad-hoc. To build a dynamic and multifunctional system, a machine learning approach will be used for the implementation. This system will be analysed according to its usability in the real-world example of a road construction. As being a part of the cyber-physical system of the SmartSite research project, the resulting software component of this master's thesis will be executed on a real construction site.
Autorenporträt
Sebastian Meyl wurde 1988 in Villingen geboren und ist Masterstudent der Wirtschaftsinformatik an der Universität Hohenheim. Er hat einen Bachelor of Science der Wirtschaftswissenschaften mit Schwerpunkt in der Informatik von der Universität Ulm und ist gelernter Bankkaufmann.