The theoretical results of in-process product temperature, primary drying time, and moisture content mapping and history are consistent with the experimental results, suggesting the theoretical model should be useful in process development and "trouble-shooting" applications.
In this paper, we propose two original and efficient approaches for enforcing singleton arc consistency. In the first one, the data structures used to enforce arc consistency are shared between all subproblems where a domain is reduced to a singleton. This new algorithm is not optimal but it requires far less space and is often more efficient in practice than the optimal algorithm SAC-Opt. In the second approach, we perform several runs of a greedy search (where at each step, arc consistency is maintained), possibly detecting the singleton arc consistency of several values in one run. It is an original illustration of applying inference (i.e., establishing singleton arc consistency) by search. Using a greedy search allows benefiting from the incrementality of arc consistency, learning relevant information from conflicts and, potentially finding solution(s) during the inference process. We present extensive experiments that show the benefit of our two approaches. This paper is a compilation and an extension of [5] and [18].
In this paper, we propose a comprehensive study of second-order consistencies
(i.e., consistencies identifying inconsistent pairs of values) for constraint
satisfaction. We build a full picture of the relationships existing between
four basic second-order consistencies, namely path consistency (PC),
3-consistency (3C), dual consistency (DC) and 2-singleton arc consistency
(2SAC), as well as their conservative and strong variants. Interestingly, dual
consistency is an original property that can be established by using the
outcome of the enforcement of generalized arc consistency (GAC), which makes it
rather easy to obtain since constraint solvers typically maintain GAC during
search. On binary constraint networks, DC is equivalent to PC, but its
restriction to existing constraints, called conservative dual consistency
(CDC), is strictly stronger than traditional conservative consistencies derived
from path consistency, namely partial path consistency (PPC) and conservative
path consistency (CPC). After introducing a general algorithm to enforce strong
(C)DC, we present the results of an experimentation over a wide range of
benchmarks that demonstrate the interest of (conservative) dual consistency. In
particular, we show that enforcing (C)DC before search clearly improves the
performance of MAC (the algorithm that maintains GAC during search) on several
binary and non-binary structured problems
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