Supercharging Plant Configurations Using Z3
Publication Date
6-17-2021
Document Type
Conference Proceeding
Publication Title
Integration of Constraint Programming, Artificial Intelligence, and Operations Research 18th International Conference, CPAIOR 2021, Vienna, Austria, July 5–8, 2021, Proceedings
Editor
Peter J. Stuckey
DOI
10.1007/978-3-030-78230-6_1
First Page
1
Last Page
25
Abstract
We describe our experiences using Z3 for synthesizing and optimizing next generation plant configurations for a car manufacturing company (The views expressed in this writing are our own. They make no representation on behalf of others). Our approach leverages unique capabilities of Z3: a combination of specialized solvers for finite domain bit-vectors and uninterpreted functions, and a programmable extension that we call constraints as code. To optimize plant configurations using Z3, we identify useful formalisms from Satisfiability Modulo Theories solvers and integrate solving capabilities for the resulting non-trivial optimization problems.
Department
Computer Engineering
Recommended Citation
Nikolaj Bjørner, Maxwell Levatich, Nuno P. Lopes, Andrey Rybalchenko, and Chandrasekar Vuppalapati. "Supercharging Plant Configurations Using Z3" Integration of Constraint Programming, Artificial Intelligence, and Operations Research 18th International Conference, CPAIOR 2021, Vienna, Austria, July 5–8, 2021, Proceedings (2021): 1-25. https://doi.org/10.1007/978-3-030-78230-6_1
