SAT Encodings for Pseudo-Boolean Constraints Together With At-Most-One Constraints
Miquel Bofill, Jordi Coll, Peter Nightingale, Josep Suy and, Felix Ulrich-Oltean, Mateu Villaret

TL;DR
This paper develops new and improved SAT encodings for Pseudo-Boolean constraints with At-Most-One conditions, significantly enhancing solver efficiency and enabling more instances to be solved faster.
Contribution
It introduces several novel encodings for PB(AMO) constraints, including a compact version of the Generalized Totalizer, improving efficiency over existing methods.
Findings
PB(AMO) encodings are smaller than PB encodings
PB(AMO) encodings solve more instances within time limits
Solving time varies with PB(AMO) characteristics
Abstract
When solving a combinatorial problem using propositional satisfiability (SAT), the encoding of the problem is of vital importance. We study encodings of Pseudo-Boolean (PB) constraints, a common type of arithmetic constraint that appears in a wide variety of combinatorial problems such as timetabling, scheduling, and resource allocation. In some cases PB constraints occur together with at-most-one (AMO) constraints over subsets of their variables (forming PB(AMO) constraints). Recent work has shown that taking account of AMOs when encoding PB constraints using decision diagrams can produce a dramatic improvement in solver efficiency. In this paper we extend the approach to other state-of-the-art encodings of PB constraints, developing several new encodings for PB(AMO) constraints. Also, we present a more compact and efficient version of the popular Generalized Totalizer encoding, named…
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