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Document
DOI: 10.4230/LIPIcs.IPEC.2025.26
Complexity of Local Search for CSPs Parameterized by Constraint Difference

Authors: Aditya Anand, Vincent Cohen-Addad, Tommaso D'Orsi, Anupam Gupta, Euiwoong Lee, Debmalya Panigrahi, and Sijin Peng

Published in: LIPIcs, Volume 358, 20th International Symposium on Parameterized and Exact Computation (IPEC 2025)

Abstract
In this paper, we study the parameterized complexity of local search, whose goal is to find a good nearby solution from the given current solution. Formally, given an optimization problem where the goal is to find the largest feasible subset S of a universe U, the new input consists of a current solution P (not necessarily feasible) as well as an ordinary input for the problem. Given the existence of a feasible solution S^*, the goal is to find a feasible solution as good as S^* in parameterized time f(k)⋅n^O(1), where k denotes the distance |PΔ S^*|. This model generalizes numerous classical parameterized optimization problems whose parameter k is the minimum number of elements removed from U to make it feasible, which corresponds to the case P = U. We apply this model to widely studied Constraint Satisfaction Problems (CSPs), where U is the set of constraints, and a subset U' of constraints is feasible if there is an assignment to the variables satisfying all constraints in U'. We give a complete characterization of the parameterized complexity of all boolean-alphabet symmetric CSPs, where the predicate’s acceptance depends on the number of true literals.
Cite as

Aditya Anand, Vincent Cohen-Addad, Tommaso D'Orsi, Anupam Gupta, Euiwoong Lee, Debmalya Panigrahi, and Sijin Peng. Complexity of Local Search for CSPs Parameterized by Constraint Difference. In 20th International Symposium on Parameterized and Exact Computation (IPEC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 358, pp. 26:1-26:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{anand_et_al:LIPIcs.IPEC.2025.26,
  author =	{Anand, Aditya and Cohen-Addad, Vincent and D'Orsi, Tommaso and Gupta, Anupam and Lee, Euiwoong and Panigrahi, Debmalya and Peng, Sijin},
  title =	{{Complexity of Local Search for CSPs Parameterized by Constraint Difference}},
  booktitle =	{20th International Symposium on Parameterized and Exact Computation (IPEC 2025)},
  pages =	{26:1--26:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-407-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{358},
  editor =	{Agrawal, Akanksha and van Leeuwen, Erik Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.IPEC.2025.26},
  URN =		{urn:nbn:de:0030-drops-251586},
  doi =		{10.4230/LIPIcs.IPEC.2025.26},
  annote =	{Keywords: Constraint Satisfaction Problems, Parameterized Local Search, Optimization}
}
Document
DOI: 10.4230/LIPIcs.FSTTCS.2025.39
Improved Approximation for Pathwidth One Vertex Deletion and Parameterized Complexity of Its Variants

Authors: Satyabrata Jana, Soumen Mandal, Ashutosh Rai, and Saket Saurabh

Published in: LIPIcs, Volume 360, 45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025)

Abstract
The pathwidth of a graph is a measure of how path-like the graph is. The Pathwidth One Vertex Deletion (POVD) problem asks whether, given an undirected graph G and an integer k, one can delete at most k vertices from G so that the remaining graph has pathwidth at most one. This is a natural variation of the classical Feedback vertex Set (FVS) problem, where the deletion of at most k vertices results in a graph of treewidth at most one. In this work, we investigate POVD in the realm of approximation algorithms. We first design a 3-approximation algorithm for POVD running in polynomial time. Then, using this constant factor approximation algorithm, we obtain a randomized parameterized approximation algorithm for POVD running in time 𝒪^*((h_β)^k), that improves the fastest existing running times for approximation ratios in the range (1.76147,3). Here the constant h_β depends on the approximation factor β alone and has value 2^{(3-β)}, which lies in the range (1,2.3596), when β ∈ (1.76147,3). Taking inspiration from two extensively studied problems, namely Connected FVS and Independent FVS, we investigate two variations of the POVD problem from the perspective of parameterized algorithms. These variations are the connected variant, called Connected pathwidth One Vertex Deletion (CPOVD) and the independent variant, called Independent Pathwidth One Vertex Deletion (IPOVD). While in CPOVD the subgraph G[S] induced by the vertices to be deleted needs to be connected, in IPOVD it needs to be independent. Specifically, we show the following results. - CPOVD can be solved in {𝒪}^*(14^k) time and admits no polynomial kernel unless NP ⊆ {co-NP/poly}. - IPOVD can be solved in {𝒪}^*(7^k) time, and admits a kernel of size 𝒪(k³).
Cite as

Satyabrata Jana, Soumen Mandal, Ashutosh Rai, and Saket Saurabh. Improved Approximation for Pathwidth One Vertex Deletion and Parameterized Complexity of Its Variants. In 45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 360, pp. 39:1-39:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{jana_et_al:LIPIcs.FSTTCS.2025.39,
  author =	{Jana, Satyabrata and Mandal, Soumen and Rai, Ashutosh and Saurabh, Saket},
  title =	{{Improved Approximation for Pathwidth One Vertex Deletion and Parameterized Complexity of Its Variants}},
  booktitle =	{45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025)},
  pages =	{39:1--39:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-406-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{360},
  editor =	{Aiswarya, C. and Mehta, Ruta and Roy, Subhajit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2025.39},
  URN =		{urn:nbn:de:0030-drops-251192},
  doi =		{10.4230/LIPIcs.FSTTCS.2025.39},
  annote =	{Keywords: Pathwidth, Parameterized complexity, Approximation, Kernelization}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2025.53
A Parameterized Study of Secluded Structures in Directed Graphs

Authors: Jonas Schmidt, Shaily Verma, and Nadym Mallek

Published in: LIPIcs, Volume 359, 36th International Symposium on Algorithms and Computation (ISAAC 2025)

Abstract
Given an undirected graph G and an integer k, the Secluded Π-Subgraph problem asks you to find a maximum size induced subgraph that satisfies a property Π and has at most k neighbors in the rest of the graph. This problem has been extensively studied; however, there is no prior study of the problem in directed graphs. This question has been mentioned by Jansen et al. [ISAAC'23]. In this paper, we initiate the study of Secluded Subgraph problems in directed graphs by incorporating different notions of neighborhoods: in-neighborhood, out-neighborhood, and their union. Formally, we call these problems {In, Out, Total}-Secluded Π-Subgraph, where given a directed graph G and an integer k, we want to find an induced subgraph satisfying Π of maximum size that has at most k in/out/total-neighbors in the rest of the graph, respectively. We investigate the parameterized complexity of these problems for different properties Π. In particular, we prove the following parameterized results: - We design an FPT algorithm for the Total-Secluded Strongly Connected Subgraph problem when parameterized by k. - We show that the Out-Secluded ℱ-Free Subgraph problem with parameter k is W[1]-hard, where ℱ is a family of directed graphs except any subgraph of a star graph whose edges are directed towards the center. This result also implies that In/Out-Secluded DAG is W[1]-hard, unlike the undirected variants of the two problems, which are FPT. - We design an FPT-algorithm for In/Out/Total-Secluded α-Bounded Subgraph when parameterized by k, where α-bounded graphs are a superclass of tournaments. - For undirected graphs, we improve the best-known FPT algorithm for Secluded Clique by providing a faster FPT algorithm that runs in time 1.6181^k n^𝒪(1).
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Jonas Schmidt, Shaily Verma, and Nadym Mallek. A Parameterized Study of Secluded Structures in Directed Graphs. In 36th International Symposium on Algorithms and Computation (ISAAC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 359, pp. 53:1-53:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schmidt_et_al:LIPIcs.ISAAC.2025.53,
  author =	{Schmidt, Jonas and Verma, Shaily and Mallek, Nadym},
  title =	{{A Parameterized Study of Secluded Structures in Directed Graphs}},
  booktitle =	{36th International Symposium on Algorithms and Computation (ISAAC 2025)},
  pages =	{53:1--53:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-408-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{359},
  editor =	{Chen, Ho-Lin and Hon, Wing-Kai and Tsai, Meng-Tsung},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2025.53},
  URN =		{urn:nbn:de:0030-drops-249616},
  doi =		{10.4230/LIPIcs.ISAAC.2025.53},
  annote =	{Keywords: Secluded Subgraph, Parametrized Complexity, Directed Graphs, Strong Connectivity}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.16
What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts

Authors: Meilin Shi, Krzysztof Janowicz, Zilong Liu, Mina Karimi, Ivan Majic, and Alexandra Fortacz

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Inundated by the rapidly expanding AI research nowadays, the research community requires more effective research data management than ever. A key challenge lies in the evolving nature of concepts embedded in the growing body of research publications. As concepts evolve over time (e.g., keywords like global warming become more commonly referred to as climate change), past research may become harder to find and interpret in a modern context. This phenomenon, known as concept drift, affects how research topics and keywords are understood, categorized, and retrieved. Beyond temporal drift, such variations also occur across geographic space, reflecting differences in local policies, research priorities, and so forth. In this work, we introduce the notion of spatio-temporal concept drift to capture how concepts in scientific texts evolve across both space and time. Using a scientometric dataset in geographic information science, we detect how research keywords drifted across countries and years using word embeddings. By detecting spatio-temporal concept drift, we can better align archival research and bridge regional differences, ensuring scientific knowledge remains findable and interoperable within evolving research landscapes.
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Meilin Shi, Krzysztof Janowicz, Zilong Liu, Mina Karimi, Ivan Majic, and Alexandra Fortacz. What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 16:1-16:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shi_et_al:LIPIcs.GIScience.2025.16,
  author =	{Shi, Meilin and Janowicz, Krzysztof and Liu, Zilong and Karimi, Mina and Majic, Ivan and Fortacz, Alexandra},
  title =	{{What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{16:1--16:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.16},
  URN =		{urn:nbn:de:0030-drops-238450},
  doi =		{10.4230/LIPIcs.GIScience.2025.16},
  annote =	{Keywords: Concept Drift, Ontology, Large Language Models, Research Data Management}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.4
Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs

Authors: Katrina Schweikert, David K. Kedrowski, Shirly Stephen, and Torsten Hahmann

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Geospatial Knowledge Graphs (GeoKGs) represent a significant advancement in the integration of AI-driven geographic information, facilitating interoperable and semantically rich geospatial analytics across various domains. This paper explores the use of topologically enriched GeoKGs, built on an explicit representation of S2 Geometry alongside precomputed topological relations, for constructing efficient geospatial analysis workflows within and across knowledge graphs (KGs). Using the SAWGraph knowledge graph as a case study focused on enviromental contamination by PFAS, we demonstrate how this framework supports fundamental GIS operations - such as spatial filtering, proximity analysis, overlay operations and network analysis - in a GeoKG setting while allowing for the easy linking of these operations with one another and with semantic filters. This enables the efficient execution of complex geospatial analyses as semantically-explicit queries and enhances the usability of geospatial data across graphs. Additionally, the framework eliminates the need for explicit support for GeoSPARQL’s topological operations in the utilized graph databases and better integrates spatial knowledge into the overall semantic inference process supported by RDFS and OWL ontologies.
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Katrina Schweikert, David K. Kedrowski, Shirly Stephen, and Torsten Hahmann. Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schweikert_et_al:LIPIcs.GIScience.2025.4,
  author =	{Schweikert, Katrina and Kedrowski, David K. and Stephen, Shirly and Hahmann, Torsten},
  title =	{{Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{4:1--4:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.4},
  URN =		{urn:nbn:de:0030-drops-238332},
  doi =		{10.4230/LIPIcs.GIScience.2025.4},
  annote =	{Keywords: knowledge graph, GeoKG, spatial analysis, ontology, SPARQL, GeoSPARQL, discrete global grid system, S2 geometry, GeoAI, PFAS}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.34
DynamicSAT: Dynamic Configuration Tuning for SAT Solving

Authors: Zhengyuan Shi, Wentao Jiang, Xindi Zhang, Jin Luo, Yun Liang, Zhufei Chu, and Qiang Xu

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Boolean Satisfiability (SAT) problem serves as a foundation for solving numerous real-world challenges. As problem complexity increases, so does the demand for sophisticated SAT solvers, which incorporate a variety of heuristics tailored to optimize performance for specific problem instances. However, a major limitation persists: a configuration that performs well on one instance may lead to inefficiencies on others. While previous approaches to automatic algorithm configuration set parameters prior to runtime, they fail to adapt to the dynamic evolution of problem characteristics during the solving process. We introduce DynamicSAT, a novel SAT solver framework that dynamically tunes configuration parameters during solving process. By adjusting parameters on-the-fly, DynamicSAT adapts to changes arising from clause learning, elimination, and other transformations, thus improving efficiency and robustness across diverse SAT instances. We demonstrate that DynamicSAT achieves significant performance gains over the state-of-the-art solver on 2024 SAT Competition Benchmark.
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Zhengyuan Shi, Wentao Jiang, Xindi Zhang, Jin Luo, Yun Liang, Zhufei Chu, and Qiang Xu. DynamicSAT: Dynamic Configuration Tuning for SAT Solving. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 34:1-34:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shi_et_al:LIPIcs.CP.2025.34,
  author =	{Shi, Zhengyuan and Jiang, Wentao and Zhang, Xindi and Luo, Jin and Liang, Yun and Chu, Zhufei and Xu, Qiang},
  title =	{{DynamicSAT: Dynamic Configuration Tuning for SAT Solving}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{34:1--34:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.34},
  URN =		{urn:nbn:de:0030-drops-238952},
  doi =		{10.4230/LIPIcs.CP.2025.34},
  annote =	{Keywords: Boolean satisfiability problem, configuration tuning, multi-armed bandit}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.29
From Prediction to Action: A Constraint-Based Approach to Predictive Policing

Authors: Younes Mechqrane and Ismail Elabbassi

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Crime prevention in urban environments demands both accurate crime forecasting and the efficient deployment of limited law enforcement resources. In this paper, we present an integrated framework that combines a machine learning module (i.e. PredRNN++ [Wang et al., 2018]) for spatiotemporal crime prediction with a constraint programming module for patrol route optimization. Our approach operates within the ICON loop framework [Bessiere et al., 2017], facilitating iterative refinement of predictions and immediate adaptation of patrol strategies. We validate our method using the City of Chicago Crime Dataset. Experimental results show that routes informed by crime predictions significantly outperform strategies relying solely on historical patterns or operational constraints. These findings illustrate how coupling predictive analytics with constraint programming can substantially enhance resource allocation and overall crime deterrence.
Cite as

Younes Mechqrane and Ismail Elabbassi. From Prediction to Action: A Constraint-Based Approach to Predictive Policing. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 29:1-29:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{mechqrane_et_al:LIPIcs.CP.2025.29,
  author =	{Mechqrane, Younes and Elabbassi, Ismail},
  title =	{{From Prediction to Action: A Constraint-Based Approach to Predictive Policing}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{29:1--29:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.29},
  URN =		{urn:nbn:de:0030-drops-238902},
  doi =		{10.4230/LIPIcs.CP.2025.29},
  annote =	{Keywords: Inductive Constraint Programming (ICON) Loop, Next Frame Prediction, PredRNN++}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.4
Conflict Analysis Based on Cutting-Planes for Constraint Programming

Authors: Robbin Baauw, Maarten Flippo, and Emir Demirović

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
This paper introduces a novel constraint learning mechanism for Constraint Programming (CP) solvers that integrates cutting planes reasoning into the conflict analysis procedure. Drawing inspiration from Lazy Clause Generation (LCG), our approach, named Lazy Linear Generation (LLG), can generate linear integer inequalities to prune the search space, rather than propositional clauses as in LCG. This combines the strengths of constraint programming (strong propagation through global constraints) with cutting-planes reasoning. We present linear constraint explanations for various arithmetic constraints and the element constraint. An experimental evaluation shows that the improved generality of linear constraints has a practical impact on a CP solver by reducing the number of encountered conflicts in 45% of our benchmark instances. Our analysis and prototype implementation show promising results and are an important step towards a new paradigm to make constraint programming solvers more effective.
Cite as

Robbin Baauw, Maarten Flippo, and Emir Demirović. Conflict Analysis Based on Cutting-Planes for Constraint Programming. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 4:1-4:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{baauw_et_al:LIPIcs.CP.2025.4,
  author =	{Baauw, Robbin and Flippo, Maarten and Demirovi\'{c}, Emir},
  title =	{{Conflict Analysis Based on Cutting-Planes for Constraint Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{4:1--4:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.4},
  URN =		{urn:nbn:de:0030-drops-238655},
  doi =		{10.4230/LIPIcs.CP.2025.4},
  annote =	{Keywords: constraint programming, learning, conflict analysis}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.6
Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem

Authors: Ignace Bleukx, Ryma Boumazouza, Tias Guns, Nadine Laage, and Guillaume Poveda

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
We present an industrial case on workforce allocation and scheduling in the aircraft manufacturing industry, where available teams need to be assigned to logistical operations. This application presents several challenges such as the scale of the problem, the need for fair workload distribution, and the need for methods for mitigating unforeseen disruptions due to technical malfunctions or incompatible weather conditions. We compare different Constraint Programming (CP) models for the allocation and scheduling problems, with extra focus on modeling the workload balancing component. Additionally, we investigate different techniques for explaining infeasibility of a disrupted schedule, such as conflict computation using Minimal Unsatisfiable Subsets (MUSes) and feasibility restoration using Minimal Correction Subsets (MCSes) or constraint relaxations. Our experimental results show that by using appropriate modeling techniques, the problem can be solved in reasonable time, thereby producing fair schedules. Additionally, we show how invalidated schedules can be explained and restored efficiently to help human operators in solving disruptions to the schedule.
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Ignace Bleukx, Ryma Boumazouza, Tias Guns, Nadine Laage, and Guillaume Poveda. Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 6:1-6:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bleukx_et_al:LIPIcs.CP.2025.6,
  author =	{Bleukx, Ignace and Boumazouza, Ryma and Guns, Tias and Laage, Nadine and Poveda, Guillaume},
  title =	{{Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{6:1--6:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.6},
  URN =		{urn:nbn:de:0030-drops-238670},
  doi =		{10.4230/LIPIcs.CP.2025.6},
  annote =	{Keywords: modeling, scheduling, fairness, explanations, feasibility restoration}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.7
Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability

Authors: Manuel Chastenay, Xavier Zwingmann, Claude-Guy Quimper, and Jonathan Gaudreault

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
In industrial settings, cutting predefined pieces from one or multiple sheets of material is a common optimization challenge. This problem can be formulated as a variant of the 2D bin packing problem, where the edges of the pieces define the cut lines. This paper presents a constraint programming model developed in collaboration with an industrial partner in construction to minimize scrap waste generated when cutting insulation pieces. The model introduces an objective function designed to maximize the reusability of leftover material. To fully leverage the model’s efficiency, an initial process transforms irregular insulation pieces into rectangles using one of four processing methods. A comparative analysis is conducted to evaluate the impact of these methods, as well as to benchmark the model’s results against the partner’s manual approach.
Cite as

Manuel Chastenay, Xavier Zwingmann, Claude-Guy Quimper, and Jonathan Gaudreault. Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 7:1-7:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chastenay_et_al:LIPIcs.CP.2025.7,
  author =	{Chastenay, Manuel and Zwingmann, Xavier and Quimper, Claude-Guy and Gaudreault, Jonathan},
  title =	{{Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{7:1--7:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.7},
  URN =		{urn:nbn:de:0030-drops-238685},
  doi =		{10.4230/LIPIcs.CP.2025.7},
  annote =	{Keywords: Combinatorial optimization, constraint programming, 2D bin packing}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.9
Constraint Models for Klondike

Authors: Nguyen Dang, Ian P. Gent, Peter Nightingale, Felix Ulrich-Oltean, and Jack Waller

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Klondike is the most famous single-player card game, and remains a challenging search problem even in the "thoughtful" variant where all card locations are known. We consider the full game of Klondike except for one restriction that the unusual move of "worrying back" is disallowed. This model is able to determine the winnability of all instances of the game and in practice does so in less than 2000 secs for 10,000 instances we tested, which no other known algorithm can achieve. On some instances, however, other techniques can produce answers more quickly. We use constraint modelling to produce schedules for running our constraint model in combination with other techniques. The combination outperforms any single solver across a range of time limits. Using this combination we are able to significantly improve the best estimate of winnability of Klondike without worrying back. Finally we show how we can use this work to also improve the estimate of winnability of the regular game of Klondike.
Cite as

Nguyen Dang, Ian P. Gent, Peter Nightingale, Felix Ulrich-Oltean, and Jack Waller. Constraint Models for Klondike. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 9:1-9:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dang_et_al:LIPIcs.CP.2025.9,
  author =	{Dang, Nguyen and Gent, Ian P. and Nightingale, Peter and Ulrich-Oltean, Felix and Waller, Jack},
  title =	{{Constraint Models for Klondike}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{9:1--9:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.9},
  URN =		{urn:nbn:de:0030-drops-238702},
  doi =		{10.4230/LIPIcs.CP.2025.9},
  annote =	{Keywords: AI Planning, Modelling, Constraint Programming, Solitaire and Patience Games}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.12
An Expansion-Based Approach for Quantified Integer Programming

Authors: Michael Hartisch and Leroy Chew

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Quantified Integer Programming (QIP) bridges multiple domains by extending Quantified Boolean Formulas (QBF) to incorporate general integer variables and linear constraints while also generalizing Integer Programming through variable quantification. As a special case of Quantified Constraint Satisfaction Problems (QCSP), QIP provides a versatile framework for addressing complex decision-making scenarios. Additionally, the inclusion of a linear objective function enables QIP to effectively model multistage robust discrete linear optimization problems, making it a powerful tool for tackling uncertainty in optimization. While two primary solution paradigms exist for QBF - search-based and expansion-based approaches - only search-based methods have been explored for QIP and QCSP. We introduce an expansion-based approach for QIP using Counterexample-Guided Abstraction Refinement (CEGAR), adapting techniques from QBF. We extend this methodology to tackle multistage robust discrete optimization problems with linear constraints and further embed it in an optimization framework, enhancing its applicability. Our experimental results highlight the advantages of this approach, demonstrating superior performance over existing search-based solvers for QIP in specific instances. Furthermore, the ability to model problems using linear constraints enables notable performance gains over state-of-the-art expansion-based solvers for QBF.
Cite as

Michael Hartisch and Leroy Chew. An Expansion-Based Approach for Quantified Integer Programming. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 12:1-12:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hartisch_et_al:LIPIcs.CP.2025.12,
  author =	{Hartisch, Michael and Chew, Leroy},
  title =	{{An Expansion-Based Approach for Quantified Integer Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{12:1--12:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.12},
  URN =		{urn:nbn:de:0030-drops-238736},
  doi =		{10.4230/LIPIcs.CP.2025.12},
  annote =	{Keywords: Quantified Integer Programming, Quantified Constraint Satisfaction, Robust Discrete Optimization, Expansion, CEGAR}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.38
Reducing Quantum Circuit Synthesis to #SAT

Authors: Dekel Zak, Jingyi Mei, Jean-Marie Lagniez, and Alfons Laarman

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Quantum circuit synthesis is the task of decomposing a given quantum operator into a sequence of elementary quantum gates. Since the finite target gate set cannot exactly implement any given operator, approximation is often necessary. Model counting, or #SAT, has recently been demonstrated as a promising new approach for tackling core problems in quantum circuit analysis. In this work, we show for the first time that the universal quantum circuit synthesis problem can be reduced to maximum model counting. We formulate a #SAT encoding for exact and approximate depth-optimal quantum circuit synthesis into the Clifford+T gate set. We evaluate our method with an open-source implementation that uses the maximum model counter d4Max as a backend. For this purpose, we extended d4Max with support for complex and negative weights to represent amplitudes. Experimental results show that existing classical tools have potential for the quantum circuit synthesis problem.
Cite as

Dekel Zak, Jingyi Mei, Jean-Marie Lagniez, and Alfons Laarman. Reducing Quantum Circuit Synthesis to #SAT. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 38:1-38:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zak_et_al:LIPIcs.CP.2025.38,
  author =	{Zak, Dekel and Mei, Jingyi and Lagniez, Jean-Marie and Laarman, Alfons},
  title =	{{Reducing Quantum Circuit Synthesis to #SAT}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{38:1--38:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.38},
  URN =		{urn:nbn:de:0030-drops-238997},
  doi =		{10.4230/LIPIcs.CP.2025.38},
  annote =	{Keywords: Maximum weighted model counting, quantum circuit synthesis}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.20
Dependency-Curated Large Neighbourhood Search

Authors: Frej Knutar Lewander, Pierre Flener, and Justin Pearson

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
In large neighbourhood search (LNS), an incumbent initial solution is incrementally improved by selecting a subset of the variables, called the freeze set, and fixing them to their values in the incumbent solution, while a value for each remaining variable is found and assigned via solving (such as constraint programming-style propagation and search). Much research has been performed on finding generic and problem-specific LNS selection heuristics that select freeze sets that lead to high-quality solutions. In constraint-based local search (CBLS), the relations between the variables via the constraints are fundamental and well-studied, as they capture dependencies of the variables. In this paper, we apply these ideas from CBLS to the LNS context, presenting the novel dependency curation scheme, which exploits them to find a low-cardinality set of variables that the freeze set of any selection heuristic should be a subset of. The scheme often improves the overall performance of generic selection heuristics. Even when the scheme is used with a naïve generic selection heuristic that selects random freeze sets, the performance is competitive with more elaborate generic selection heuristics.
Cite as

Frej Knutar Lewander, Pierre Flener, and Justin Pearson. Dependency-Curated Large Neighbourhood Search. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 20:1-20:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{knutarlewander_et_al:LIPIcs.CP.2025.20,
  author =	{Knutar Lewander, Frej and Flener, Pierre and Pearson, Justin},
  title =	{{Dependency-Curated Large Neighbourhood Search}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{20:1--20:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.20},
  URN =		{urn:nbn:de:0030-drops-238810},
  doi =		{10.4230/LIPIcs.CP.2025.20},
  annote =	{Keywords: Combinatorial Optimisation, Large Neighbourhood Search (LNS), Constraint-Based Local Search (CBLS)}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.15
RustSAT: A Library for SAT Solving in Rust

Authors: Christoph Jabs

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
State-of-the-art Boolean satisfiability (SAT) solvers constitute a practical and competitive approach for solving various real-world problems. To encourage their widespread adoption, the relatively high barrier of entry following from the low level syntax of SAT and the expert knowledge required to achieve tight integration with SAT solvers should be further reduced. We present RustSAT, a library with the aim of making SAT solving technology readily available in the Rust programming language. RustSAT provides functionality for helping with generating (Max)SAT instances, writing them to, or reading them from files. Furthermore, RustSAT includes interfaces to various state-of-the-art SAT solvers available with a unified Rust API. Lastly, RustSAT implements several encodings for higher level constraints (at-most-one, cardinality, and pseudo-Boolean), which are also available via a C and Python API.
Cite as

Christoph Jabs. RustSAT: A Library for SAT Solving in Rust. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 15:1-15:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{jabs:LIPIcs.SAT.2025.15,
  author =	{Jabs, Christoph},
  title =	{{RustSAT: A Library for SAT Solving in Rust}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{15:1--15:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.15},
  URN =		{urn:nbn:de:0030-drops-237498},
  doi =		{10.4230/LIPIcs.SAT.2025.15},
  annote =	{Keywords: Rust, library, SAT solvers, constraint encodings}
}
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