Scheduling of Automated Wet-Etch Stations with One Robot in Semiconductor Manufacturing via Constraint Answer Set Programming

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch schedul...

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Veröffentlicht in:Processes Jg. 12; H. 7; S. 1315
Hauptverfasser: García-Mata, Carmen L., Burtseva, Larysa, Werner, Frank
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.07.2024
Schlagworte:
Automated reasoning
Automation
Big Data
Combinatorial analysis
Constraints
Decision making
Declarative programming
Industrial applications
Industry 4.0
Integer programming
Linear programming
Literature reviews
Manufacturing
Mathematical programming
Mixed integer
Optimization
Production scheduling
Robots
Scheduling
Semantics
Solvers
ISSN:2227-9717, 2227-9717
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Zusammenfassung:Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constraint solvers is comparatively better than a decade ago, and some complex combinatorial problems can be better solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr12071315