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[48230] Artykuł: A Constraint-Based Approach to Modeling and Solving Resource-Constrained Scheduling ProblemsCzasopismo: Computational Collective Intelligence Tom: 9875, Strony: 423-433ISSN: 0302-9743 ISBN: 978-3-319-45242-5 Wydawca: SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY Opublikowano: 2016 Seria wydawnicza: Lecture Notes in Artificial Intelligence Autorzy / Redaktorzy / Twórcy
Grupa MNiSW: Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science) Punkty MNiSW: 15 Klasyfikacja Web of Science: Proceedings Paper Pełny tekst DOI Web of Science Keywords: Constraint programming  Mathematical programming  Resource-constrained scheduling problem  Knowledge-based approach  |
Constrained scheduling problems are common in manufacturing, project management, transportation, supply chain management, software engineering, computer networks etc. Multiple binary and integer decision variables representing the allocation of resources to activities and numerous specific constraints on these variables are typical components of the constraint scheduling problem modeling. With their increased computational complexity, the models are more demanding, particularly when methods of operations research (mathematical programming, network programming, dynamic programming) are used. By contrast, most resource-constrained scheduling problems can be easily modeled as instances of the constraint satisfaction problems (CSPs) and solved using constraint programming (CP) or others methods. In the CP-based environment the problem definition is separated from the methods and algorithms used to solve the problem. Therefore, a constraint-based approach to resource-constrained scheduling problems that combines an OR-based approach for problem solving and a CP-based approach for problem modeling is proposed. To evaluate the applicability and efficiency of this approach and its implementation framework, illustrative examples of resource-constrained scheduling problems are implemented separately for different environments.