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Titlebook: Decision Diagrams for Optimization; David Bergman,Andre A. Cire,John Hooker Book 2016 The Editor(s) (if applicable) and The Author(s), und

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https://doi.org/10.1007/978-1-4419-0522-2valuation for a wide range of problems, including the traveling salesman problem with time windows, the sequential ordering problem, and minimum-tardiness sequencing problems. The results demonstrate that MDD propagation can improve a state-of-the-art constraint based scheduler by orders of magnitude in terms of solving time.
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Recursive Modeling,tate-dependent costs with canonical arc costs in a decision diagram, a technique that can sometimes greatly simplify the recursion, as illustrated by a textbook inventory management problem. It concludes with an extension to nonserial recursive modeling and nonserial decision diagrams.
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MDD Propagation for , Constraints, . is NP-hard, but fixed-parameter tractable. Furthermore, we present an MDD propagation algorithm that may not establish MDD consistency, but is highly effective in practice when compared to conventional domain propagation.
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Data Mining and Decision Support for the maximum independent set problem. The chapter concludes by describing an alternative method to generate relaxed diagrams, the ., and exemplify its application to a single-machine makespan problem.
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