Titlebook: Integration of AI and OR Techniques in Constraint Programming; 12th International C Laurent Michel Conference proceedings 2015 Springer Int

騙子

Intersect

玩忽職守

Time-Table Disjunctive Reasoning for the Cumulative Constraint, a limited resource by several tasks?– is one of the core components that are surely responsible of this success. Unfortunately, ensuring bound-consistency for the . constraint is already NP-Hard. Therefore, several relaxations were proposed to reduce domains in polynomial time such as Time-Tabling,

功多汁水

Uncertain Data Dependency Constraints in Matrix Models, do exist among such data, e.g. upper bound on the sum of uncertain production rates per machine, sum of traffic distribution ratios from a router over several links. For tractability reasons existing approaches in constraint programming or robust optimization frameworks assume independence of the d

騷動(dòng)

GROUP

Enhancing MIP Branching Decisions by Using the Sample Variance of Pseudo Costs,d by history information, mainly the variable pseudo-costs, which are used to estimate the objective gain. At the beginning of the search, such information is usually collected via an expensive look-ahead strategy called strong branching until variables are considered reliable..The reliability notio

眼界

BDD-Guided Clause Generation,nstraint programming. We present a generic method to learn valid clauses from exact or approximate binary decision diagrams (BDDs) and resolution in the context of SAT solving. We show that any clause learned from SAT conflict analysis can also be generated using our method, while, in addition, we c

心痛

財(cái)政

奇思怪想