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Titlebook: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problem; First International Jean-Charles Rég

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31#
發(fā)表于 2025-3-27 00:25:18 | 只看該作者
Filtering Algorithms for the Same?Constrainttiset of values assigned to the variables in?. is contained in the multiset of values assigned to the variables in?.. Same?is the special case of UsedBy?in which |.|=|.|. In this paper we show algorithms that achieve arc consistency and bound consistency for the Same?constraint and in its extended v
32#
發(fā)表于 2025-3-27 02:44:13 | 只看該作者
33#
發(fā)表于 2025-3-27 07:47:17 | 只看該作者
34#
發(fā)表于 2025-3-27 12:45:09 | 只看該作者
35#
發(fā)表于 2025-3-27 16:37:39 | 只看該作者
Generating Benders Cuts for a?General Class of Integer Programming Problemsive form is presented first, as a?basis for the subsequent derivations of simple valid cuts. Under a?qualification condition, a?. Benders cut in linear form can be identified. A?cut generation problem is formulated to elicit it. The simple valid Benders cut is further generalized to a?. Benders cut,
36#
發(fā)表于 2025-3-27 20:32:48 | 只看該作者
37#
發(fā)表于 2025-3-28 00:22:51 | 只看該作者
Super Solutions in Constraint Programmingriables lose their values, the solution can be repaired by assigning these variables with?. new values and at most?. other variables. Super solutions are a?generalization of supermodels in propositional satisfiability. We focus in this paper on (1,0)-super solutions, where if one variable loses its
38#
發(fā)表于 2025-3-28 03:07:44 | 只看該作者
39#
發(fā)表于 2025-3-28 10:16:04 | 只看該作者
Dynamic Heaviest Paths in DAGs with Arbitrary Edge Weightsms for this problem which work on DAGs with strictly positive edge weights. They handle edges of zero or negative weight by replacing each of them by (potentially many) edges with positive weights. In this paper we show an alternative solution, which has the same complexity and handles arbitrary edg
40#
發(fā)表于 2025-3-28 14:09:32 | 只看該作者
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