For many combinatorial problems, finding a single solution is not enough. This is clearly the
case for multi-objective optimization problems, as they have no single “best solution” and …

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SpringerLink Log in Menu Find a journal Publish with us Search Cart 1.Home 2.Constraints …

Constraint Programming (CP) standardizes many specialized “global constraints” allowing
high-level modelling of combinatorial optimization and feasibility problems. Current Mixed …

Dynamic programming (DP) is a framework used in multiple disciplines to solve decision-
making problems. In particular, in computer science and operations research (OR), DP …

Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction
and combinatorial optimization problems. In CP, problems are modeled with constraints that …

String processing is ubiquitous across computer science, and arguably more so in web
programming. In order to reason about programs manipulating strings we need to solve …

Meta-heuristics are generic search methods that are used to solve challenging
combinatorial problems. We describe these methods and highlight their common features …

Strings are extensively used in modern programming languages and constraints over strings
of unknown length occur in a wide range of real-world applications such as software …

A well-known and powerful constraint model reformulation is to compute the solutions to a
model part, say a custom constraint predicate, and tabulate them within an extensional …

This paper presents GHOST, a combinatorial optimization framework that a real-time
strategy (RTS) AI developer can use to model and solve any problem encoded as a …