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Documents authored by Stergiou, Kostas

Document
DOI: 10.4230/LIPIcs.CP.2024.14
A CP/LS Heuristic Method for Maxmin and Minmax Location Problems with Distance Constraints

Authors: Panteleimon Iosif, Nikolaos Ploskas, Kostas Stergiou, and Dimosthenis C. Tsouros

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
In facility location problems we seek to locate a set of facilities in an area, where clients may be present, so that some criterion is optimized. For instance, in the p-center problem we seek to minimize the maximum distance between any client and its closest facility, whereas in the p-dispersion problem we seek to maximize the minimum distance between any two facilities. Hence, in the former we have a minmax objective, whereas in the latter we have a maxmin objective. Recently, a variant of p-dispersion where distance constraints exist between facilities was studied from a CP and ILP perspective. An incomplete CP solver that uses a greedy heuristic to prune branches was shown to significantly outperform Gurobi and OR-Tools in terms of execution time, although it failed to discover optimal or near-optimal solutions in many instances. We enhance this work in two directions, regarding the effectiveness and the applicability of the approach. We first show how local search can be used to obtain better estimations of the bound at each node, resulting in more focused pruning, which allows for optimal or near-optimal solutions to be discovered in many more instances. Then, we demonstrate how the framework can be applied on the p-center problem with distance constraints, comparing it to ILP and CP models implemented in Gurobi and OR-Tools, respectively.
Cite as

Panteleimon Iosif, Nikolaos Ploskas, Kostas Stergiou, and Dimosthenis C. Tsouros. A CP/LS Heuristic Method for Maxmin and Minmax Location Problems with Distance Constraints. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 14:1-14:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{iosif_et_al:LIPIcs.CP.2024.14,
  author =	{Iosif, Panteleimon and Ploskas, Nikolaos and Stergiou, Kostas and Tsouros, Dimosthenis C.},
  title =	{{A CP/LS Heuristic Method for Maxmin and Minmax Location Problems with Distance Constraints}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{14:1--14:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.14},
  URN =		{urn:nbn:de:0030-drops-206995},
  doi =		{10.4230/LIPIcs.CP.2024.14},
  annote =	{Keywords: Constraint Programming, Local Search, facility location, distance constraints, optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.30
The p-Dispersion Problem with Distance Constraints

Authors: Nikolaos Ploskas, Kostas Stergiou, and Dimosthenis C. Tsouros

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
In the (maxmin) p-dispersion problem we seek to locate a set of facilities in an area so that the minimum distance between any pair of facilities is maximized. We study a variant of this problem where there exist constraints specifying the minimum allowed distances between the facilities. This type of problem, which we call PDDP, has not received much attention within the literature on location and dispersion problems, despite its relevance to real scenarios. We propose both ILP and CP methods to solve the PDDP. Regarding ILP, we give two formulations derived from a classic and a state-of-the-art model for p-dispersion, respectively. Regarding CP, we first give a generic model that can be implemented within any standard CP solver, and we then propose a specialized heuristic Branch&Bound method. Experiments demonstrate that the ILP formulations are more efficient than the CP model, as the latter is unable to prove optimality in reasonable time, except for small problems, and is usually slower in finding solutions of the same quality than the ILP models. However, although the ILP approach displays good performance on small to medium size problems, it cannot efficiently handle larger ones. The heuristic CP-based method can be very efficient on larger problems and is able to quickly discover solutions to problems that are very hard for an ILP solver.
Cite as

Nikolaos Ploskas, Kostas Stergiou, and Dimosthenis C. Tsouros. The p-Dispersion Problem with Distance Constraints. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 30:1-30:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{ploskas_et_al:LIPIcs.CP.2023.30,
  author =	{Ploskas, Nikolaos and Stergiou, Kostas and Tsouros, Dimosthenis C.},
  title =	{{The p-Dispersion Problem with Distance Constraints}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{30:1--30:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.30},
  URN =		{urn:nbn:de:0030-drops-190679},
  doi =		{10.4230/LIPIcs.CP.2023.30},
  annote =	{Keywords: Facility location, distance constraints, optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.54
Learning Max-CSPs via Active Constraint Acquisition

Authors: Dimosthenis C. Tsouros and Kostas Stergiou

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
Constraint acquisition can assist non-expert users to model their problems as constraint networks. In active constraint acquisition, this is achieved through an interaction between the learner, who posts examples, and the user who classifies them as solutions or not. Although there has been recent progress in active constraint acquisition, the focus has only been on learning satisfaction problems with hard constraints. In this paper, we deal with the problem of learning soft constraints in optimization problems via active constraint acquisition, specifically in the context of the Max-CSP. Towards this, we first introduce a new type of queries in the context of constraint acquisition, namely partial preference queries, and then we present a novel algorithm for learning soft constraints in Max-CSPs, using such queries. We also give some experimental results.
Cite as

Dimosthenis C. Tsouros and Kostas Stergiou. Learning Max-CSPs via Active Constraint Acquisition. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 54:1-54:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{tsouros_et_al:LIPIcs.CP.2021.54,
  author =	{Tsouros, Dimosthenis C. and Stergiou, Kostas},
  title =	{{Learning Max-CSPs via Active Constraint Acquisition}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{54:1--54:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.54},
  URN =		{urn:nbn:de:0030-drops-153452},
  doi =		{10.4230/LIPIcs.CP.2021.54},
  annote =	{Keywords: Constraint acquisition, modeling, learning}
}
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