Often, real-world Constraint Satisfaction Problems (CSPs) are subject to uncertainty/dynamism not known in advance. Some techniques in the literature offer robust solutions for CSPs. Here, we analyze a previous exact/complete approach from the state-of-the-art that focuses on CSPs with ordered domains and dynamic bounds. However, this approach has low performance in large-scale CSPs. For this reason, in this paper, we present an inexact/incomplete approach that is faster at finding robust solutions for large-scale CSPs. It is useful when the computation time available for finding a solution is limited and/or in situations where a new one must be re-computed online because the dynamism invalidated the original one. Specifically, we present a Large Neighbourhood Search (LNS) algorithm combined with Constraint Programming (CP) and Branch-and-bound (B&B) that searches for robust solutions. We also present a robust-value selection heuristic that guides the search toward more promising branches. We evaluate our approach with large-scale CSPs instances, including the case study of scheduling problems. The evaluation shows a considerable improvement in the robustness of the solutions achieved by our algorithm for large-scale CSPs.
@InProceedings{lopez_et_al:LIPIcs.CP.2022.33, author = {L\'{o}pez, Jheisson and Arbelaez, Alejandro and Climent, Laura}, title = {{Large Neighborhood Search for Robust Solutions for Constraint Satisfaction Problems with Ordered Domains}}, booktitle = {28th International Conference on Principles and Practice of Constraint Programming (CP 2022)}, pages = {33:1--33:16}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-240-2}, ISSN = {1868-8969}, year = {2022}, volume = {235}, editor = {Solnon, Christine}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.33}, URN = {urn:nbn:de:0030-drops-166625}, doi = {10.4230/LIPIcs.CP.2022.33}, annote = {Keywords: Constraint Programming, Large Neighbourhood Search, Robust Solutions} }
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