Efficient Method for Optimizing Calcium Silicate Masonry Unit Manufacturing Using Simulation-Based Optimization and Decomposition

2016 ◽  
Vol 856 ◽  
pp. 99-108
Author(s):  
Toni Donhauser ◽  
Joachim Lohse ◽  
Jörg Franke ◽  
Peter Schuderer

This paper describes an overall, simulation-based optimization approach to control plant operations for manufacturing calcium silicate masonry units (CS), which is directed towards and thus immediately applicable to practical processes. Starting from an investigation and classification of the CS production in order to differentiate the properties of each sub-process, specific target criteria are derived. To enable the influencing of these targets, relevant parameters including their mutual interdependencies are identified. On this basis, the criticality of each process step is assessed in order to determine improvement potentials and to investigate possible adjustments to the parameters.The elementary production types indicate a mix of the discontinuous and continuous processing in CS plants. Particularly, this work shows that through interrupting the continuous material flow, the hardening process is the main criteria for a plant’s success in meeting its targets, especially concerning energy efficiency. To achieve a feasible approach, the work develops a solving method geared to an optimized hardening process.Therefore, a formulation of a measureable target system is established, which is the prerequisite for modeling the whole optimization problem. An expedient decomposition of this optimization model to smaller sub-problems provides an efficient solving of these complex job-scheduling problems, in order to direct the method towards an operative use. The paper concludes with the determination of potential solving procedures for the overall problem and appropriate algorithms for solving the sub-problems.

2018 ◽  
Vol 108 (04) ◽  
pp. 221-227
Author(s):  
T. Donhauser ◽  
L. Baier ◽  
T. Ebersbach ◽  
J. Franke ◽  
P. Schuderer

Die Kalksandsteinherstellung weist aufgrund prozesstechnisch und zeitlich divergierender Teilprozesse einen hohen Planungs- sowie Steuerungsaufwand auf. Durch Einsatz eines simulationsgestützten Optimierungsverfahrens kann diese Komplexität bewältigt werden. Um bei hoher Lösungsqualität eine Laufzeit zu erreichen, die einen operativen Einsatz des Verfahrens gestattet, wird auf Basis einer vorangegangenen Studie ein Dekompositionsansatz implementiert und dessen Eignung durch Testläufe validiert.   Calcium silicate masonry production requires a great deal of planning and control due to the fact that subprocesses vary in terms of process technology and time. To overcome this complexity, a simulation-based optimization approach is applied. As a short runtime that allows the method to be used operationally and yet still offers a high quality of solution is crucial, a decomposition approach is implemented on the basis of a previous study and its suitability is validated by means of test runs.


2017 ◽  
Vol 107 (04) ◽  
pp. 288-292
Author(s):  
M. Kück ◽  
J. Ehm ◽  
T. Hildebrandt ◽  
M. Prof. Freitag ◽  
E. M. Prof. Frazzon

Der Trend zur Fertigung individualisierter Produkte in kleinen Losgrößen erfordert hochflexible Produktionssysteme. Durch die damit verbundene Systemdynamik wird die Reihenfolgeplanung zu einem komplexen Planungsproblem. Der Beitrag beschreibt ein simulationsbasiertes Optimierungsverfahren, welches Echtzeitinformationen zur adaptiven Selektion geeigneter Prioritätsregeln verwendet. Das Potenzial des Ansatzes wird anhand eines Anwendungsfalls aus der Halbleiterindustrie demonstriert.   The trend to manufacturing individualized products in small-scale series demands highly flexible production systems. Because of the dynamic nature of such production systems, scheduling becomes a complex planning problem with frequent need for rescheduling. This article describes a data-driven simulation-based optimization approach using real-time information for adaptive job shop scheduling. The potential of the approach is demonstrated by a use case from semiconductor industry.


2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Seungchul Lee ◽  
Jun Ni

This paper presents wafer sequencing problems considering perceived chamber conditions and maintenance activities in a single cluster tool through the simulation-based optimization method. We develop optimization methods which would lead to the best wafer release policy in the chamber tool to maximize the overall yield of the wafers in semiconductor manufacturing system. Since chamber degradation will jeopardize wafer yields, chamber maintenance is taken into account for the wafer sequence decision-making process. Furthermore, genetic algorithm is modified for solving the scheduling problems in this paper. As results, it has been shown that job scheduling has to be managed based on the chamber degradation condition and maintenance activities to maximize overall wafer yield.


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