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Documents authored by Iosif, Panteleimon

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DOI: 10.4230/artifacts.26942
pkiosif/Modeling-pDDs

Authors: Panteleimon Iosif

Abstract
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Panteleimon Iosif. pkiosif/Modeling-pDDs (Software, Source Code & Benchmarks). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@misc{dagstuhl-artifact-26942,
   title = {{pkiosif/Modeling-pDDs}}, 
   author = {Iosif, Panteleimon},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:5105a6637530142f3a61bc1acb95b862eb5b98e6;origin=https://github.com/pkiosif/Modeling-pDDs;visit=swh:1:snp:e6039deeca752727cce06e21fca507ac27e660f0;anchor=swh:1:rev:1da9da1453c5f140b017727ca3ed97ab6752bc89}{\texttt{swh:1:dir:5105a6637530142f3a61bc1acb95b862eb5b98e6}} (visited on 2026-07-13)},
   url = {https://github.com/pkiosif/Modeling-pDDs},
   doi = {10.4230/artifacts.26942},
}
Document
DOI: 10.4230/LIPIcs.CP.2026.30
Modeling the p-Dispersion Problem with Distance Constraints

Authors: Panteleimon Iosif, Nikolaos Ploskas, Kostas Stergiou, and Dimos Tsouros

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract
We study the p-dispersion problem with distance constraints (pDD), a variant of the well-known p-dispersion problem. In a pDD, the goal is to locate a set of facilities so as to maximize the minimum distance between any two of them, subject to additional constraints specifying minimum allowed distances. Two CP models for the pDD have recently been proposed. The first is a typical model that includes the global constraints Minimum and Element and explicitly represents the objective function, connecting it to the decision variables. However, as problem size grows, this model becomes increasingly inefficient. The second model adopts a simplistic approach that only uses binary constraints, essentially treating the pDD as a satisfaction problem. In this paper, after demonstrating the deficiencies of these models, we propose a new compact model that captures the problem through ternary constraints, instead of global or binary ones. We prove that, rather surprisingly, the pruning of the decision variables' domains achieved in our new model is equivalent to that achieved in the model with global constraints, resulting in the same search tree under the same variable and value ordering. Experiments demonstrate that our new model is by far superior to the existing ones, both in terms of solution quality and run times.
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Panteleimon Iosif, Nikolaos Ploskas, Kostas Stergiou, and Dimos Tsouros. Modeling the p-Dispersion Problem with Distance Constraints. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 30:1-30:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{iosif_et_al:LIPIcs.CP.2026.30,
  author =	{Iosif, Panteleimon and Ploskas, Nikolaos and Stergiou, Kostas and Tsouros, Dimos},
  title =	{{Modeling the p-Dispersion Problem with Distance Constraints}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{30:1--30:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.30},
  URN =		{urn:nbn:de:0030-drops-266629},
  doi =		{10.4230/LIPIcs.CP.2026.30},
  annote =	{Keywords: Modeling, facility location, distance constraints, propagation, optimization}
}
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}
}
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