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Competitions and Empirical Evaluations in Automated Reasoning (Dagstuhl Seminar 25441)

Authors: Johannes K. Fichte, Matti Järvisalo, Aina Niemetz, Andreas Niskanen, and Guido Tack

Published in: Dagstuhl Reports, Volume 15, Issue 10 (2026)

Abstract

Solver competitions and practical problem solving challenges are a cornerstone in the field of Automated Reasoning (AR). They drive innovation by providing a platform for benchmarking, empirical evaluation, standardization of robust tools and methodologies, and identify challenges from research and industry. These events not only showcase the latest advancements in solver technology but also help to establish best practices for reliability and performance assessment. Organizing such competitions presents significant challenges, including the selection of representative benchmarks, the development of fair evaluation metrics, and ensuring result reproducibility. It is widely acknowledged that continued community engagement is essential for tackling these challenges and strengthening collaboration among organizers, developers, users, and reviewers. This report documents the program and the outcomes of Dagstuhl Seminar "Competitions and Empirical Evaluations in Automated Reasoning" (25441), which centered around competition challenges, discussed questions and solutions with the aim to build a community of practice of competition organization and empirical evaluation in AR.

Cite as

Johannes K. Fichte, Matti Järvisalo, Aina Niemetz, Andreas Niskanen, and Guido Tack. Competitions and Empirical Evaluations in Automated Reasoning (Dagstuhl Seminar 25441). In Dagstuhl Reports, Volume 15, Issue 10, pp. 135-154, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{fichte_et_al:DagRep.15.10.135,
  author =	{Fichte, Johannes K. and J\"{a}rvisalo, Matti and Niemetz, Aina and Niskanen, Andreas and Tack, Guido},
  title =	{{Competitions and Empirical Evaluations in Automated Reasoning (Dagstuhl Seminar 25441)}},
  pages =	{135--154},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2026},
  volume =	{15},
  number =	{10},
  editor =	{Fichte, Johannes K. and J\"{a}rvisalo, Matti and Niemetz, Aina and Niskanen, Andreas and Tack, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.10.135},
  URN =		{urn:nbn:de:0030-drops-254112},
  doi =		{10.4230/DagRep.15.10.135},
  annote =	{Keywords: automated reasoning, competitions, constraint solving, design of empirical experiments, empirical evaluation}
}

Document

DOI: 10.4230/LIPIcs.CPM.2026.12

Maximizing Diversity in (Near-)Median String Selection

Authors: Diptarka Chakraborty, Rudrayan Kundu, Nidhi Purohit, and Aravinda Kanchana Ruwanpathirana

Published in: LIPIcs, Volume 369, 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)

Abstract

Given a set of strings over a specified alphabet, identifying a median or consensus string that minimizes the total distance to all input strings is a fundamental data aggregation problem. When the Hamming distance is considered as the underlying metric, this problem has extensive applications, ranging from bioinformatics to pattern recognition. However, modern applications often require the generation of multiple (near-)optimal yet diverse median strings to enhance flexibility and robustness in decision-making. In this study, we address this need by focusing on two prominent diversity measures: sum dispersion and min dispersion. We first introduce an exact algorithm for the diameter variant of the problem, which identifies pairs of near-optimal medians that are maximally diverse. Subsequently, we propose a (1-ε)-approximation algorithm (for any ε > 0) for sum dispersion, as well as a bi-criteria approximation algorithm for the more challenging min dispersion case, allowing the generation of multiple (more than two) diverse near-optimal Hamming medians. Our approach primarily leverages structural insights into the Hamming median space and also draws on techniques from error-correcting code construction to establish these results.

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Diptarka Chakraborty, Rudrayan Kundu, Nidhi Purohit, and Aravinda Kanchana Ruwanpathirana. Maximizing Diversity in (Near-)Median String Selection. In 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 369, pp. 12:1-12:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chakraborty_et_al:LIPIcs.CPM.2026.12,
  author =	{Chakraborty, Diptarka and Kundu, Rudrayan and Purohit, Nidhi and Ruwanpathirana, Aravinda Kanchana},
  title =	{{Maximizing Diversity in (Near-)Median String Selection}},
  booktitle =	{37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)},
  pages =	{12:1--12:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-420-8},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{369},
  editor =	{Bille, Philip and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2026.12},
  URN =		{urn:nbn:de:0030-drops-259382},
  doi =		{10.4230/LIPIcs.CPM.2026.12},
  annote =	{Keywords: Diversity maximization, Hamming median, diameter, dispersion, approximation algorithms}
}

Document

DOI: 10.4230/OASIcs.ATMOS.2025.13

Multi-Criteria Route Planning with Little Regret

Authors: Carina Truschel and Sabine Storandt

Published in: OASIcs, Volume 137, 25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025)

Abstract

Multi-criteria route planning arises naturally in real-world navigation scenarios where users care about more than just one objective - such as minimizing travel time while also avoiding steep inclines or unpaved surfaces or toll routes. To capture the possible trade-offs between competing criteria, many algorithms compute the set of Pareto-optimal paths, which are paths that are not dominated by others with respect to the considered cost vectors. However, the number of Pareto-optimal paths can grow exponentially with the size of the input graph. This leads to significant computational overhead and results in large output sets that overwhelm users with too many alternatives. In this work, we present a technique based on the notion of regret minimization that efficiently filters the Pareto set during or after the search to a subset of specified size. Regret minimizing algorithms identify such a representative solution subset by considering how any possible user values any subset with respect to the objectives. We prove that regret-based filtering provides us with quality guarantees for the two main query types that are considered in the context of multi-criteria route planning, namely constrained shortest path queries and personalized path queries. Furthermore, we design a novel regret minimization algorithm that works for any number of criteria, is easy to implement and produces solutions with much smaller regret value than the most commonly used baseline algorithm. We carefully describe how to incorporate our regret minimization algorithm into existing route planning techniques to drastically reduce their running times and space consumption, while still returning paths that are close-to-optimal.

Cite as

Carina Truschel and Sabine Storandt. Multi-Criteria Route Planning with Little Regret. In 25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025). Open Access Series in Informatics (OASIcs), Volume 137, pp. 13:1-13:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{truschel_et_al:OASIcs.ATMOS.2025.13,
  author =	{Truschel, Carina and Storandt, Sabine},
  title =	{{Multi-Criteria Route Planning with Little Regret}},
  booktitle =	{25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025)},
  pages =	{13:1--13:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-404-8},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{137},
  editor =	{Sauer, Jonas and Schmidt, Marie},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2025.13},
  URN =		{urn:nbn:de:0030-drops-247698},
  doi =		{10.4230/OASIcs.ATMOS.2025.13},
  annote =	{Keywords: Pareto-optimality, Regret minimization, Contraction Hierarchies}
}

Document

DOI: 10.4230/LIPIcs.ESA.2025.97

A Fast and Simple Algorithm for the Resource Constrained Shortest Path Problem

Authors: Saman Ahmadi, Andrea Raith, and Mahdi Jalili

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract

Constrained pathfinding is a classic yet challenging network optimization problem with broad applicability across many real-world domains. The Resource-Constrained Shortest Path (RCSP) problem focuses on finding cost-optimal paths that satisfy multiple resource constraints. In this paper, we propose a novel heuristic-guided search framework that accelerates constrained search in large-scale networks, including those with negative costs and resources, by leveraging efficient queuing and pruning strategies. Experimental results on real-world benchmark maps show that our framework achieves up to two orders of magnitude speedup over state-of-the-art methods, demonstrating its effectiveness in solving challenging RCSP instances within limited time.

Cite as

Saman Ahmadi, Andrea Raith, and Mahdi Jalili. A Fast and Simple Algorithm for the Resource Constrained Shortest Path Problem. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 97:1-97:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ahmadi_et_al:LIPIcs.ESA.2025.97,
  author =	{Ahmadi, Saman and Raith, Andrea and Jalili, Mahdi},
  title =	{{A Fast and Simple Algorithm for the Resource Constrained Shortest Path Problem}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{97:1--97:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2025.97},
  URN =		{urn:nbn:de:0030-drops-245668},
  doi =		{10.4230/LIPIcs.ESA.2025.97},
  annote =	{Keywords: constrained pathfinding, shortest path problem, heuristic search}
}

Document

DOI: 10.4230/LIPIcs.CP.2025.35

Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems

Authors: Konstantin Sidorov, Imko Marijnissen, and Emir Demirović

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

Abstract

Constraint programming solvers have seen much success in scheduling problems owing to their efficient reasoning over constraints to solve complex problems in practice. Many algorithms have been proposed for propagating information from a single constraint. However, inferring and exchanging information across multiple constraints can provide deeper insight into the global structure of a problem. In this work, we propose to exchange information amongst constraints by inferring the disjointness of tasks in scheduling problems from many constraints. We do this by (i) augmenting existing propagators, such as the Cumulative and nogoods, to report when pairs of tasks are disjoint, and (ii) leveraging this information by introducing the SelectiveDisjunctive propagator which generates a lower bound on the earliest completion time of cliques of disjoint tasks to determine conflicts. This allows us to aggregate disjointness information spanning multiple constraints to gain a better global overview of the problem, as well as more precise local information. We also identify a problem structure where an LCG solver reasoning over Cumulative constraints separately, without any reformulations, requires an exponential amount of time to prove infeasibility, which we both justify theoretically and show empirically; on the other hand, our approach solves those instances in polynomial time. On particular known RCPSP and RCPSP/max benchmarks, our approach significantly reduces the number of conflicts required to prove optimality when resource contention is high. Additionally, we discover new lower bounds for 16 RCPSP/max instances (closing six of them) and four RCPSP instances (closing one), as well as new upper bounds for two RCPSP/max instances and four RCPSP instances. Furthermore, we empirically analyse our proposed approach to determine which features are beneficial for performance, showing that finding cliques is one of the main bottlenecks and that detecting disjointness during search can lead to improved bounds on certain instances, but it generally negatively impacts learning. This work paves the way for reasoning over the disjointness of tasks inferred from a variety of standard constraints to discover novel information sourced from multiple constraints during search.

Cite as

Konstantin Sidorov, Imko Marijnissen, and Emir Demirović. Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 35:1-35:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sidorov_et_al:LIPIcs.CP.2025.35,
  author =	{Sidorov, Konstantin and Marijnissen, Imko and Demirovi\'{c}, Emir},
  title =	{{Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{35:1--35:24},
  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.2025.35},
  URN =		{urn:nbn:de:0030-drops-238969},
  doi =		{10.4230/LIPIcs.CP.2025.35},
  annote =	{Keywords: Constraint Programming, Lazy Clause Generation, Propagation, Scheduling, Cumulative, Disjunctive}
}

Document

DOI: 10.4230/LIPIcs.CP.2025.33

Constraint-Based In-Station Train Dispatching

Authors: Andreas Schutt, Matteo Cardellini, Jip J. Dekker, Daniel Harabor, Marco Maratea, and Mauro Vallati

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

Abstract

In-station dispatching is the problem of planning the movements of scheduled trains inside a railway station. Effective solutions for in-station dispatching are important for maximising the utilisation of railway infrastructure and for mitigating the impact of incidents and delays in the broader network. In this paper, we explore a constraint-based approach to perform in-station train dispatching. Our extensive empirical analysis of multiple modelling, search strategy, and solver choices, performed over synthetically generated, yet realistic, data, shows that our method outperforms the existing planning-based state-of-the-art approach. In addition, we present different optimisation criteria, which can be effortless defined thanks to the constraint-based approach.

Cite as

Andreas Schutt, Matteo Cardellini, Jip J. Dekker, Daniel Harabor, Marco Maratea, and Mauro Vallati. Constraint-Based In-Station Train Dispatching. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 33:1-33:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schutt_et_al:LIPIcs.CP.2025.33,
  author =	{Schutt, Andreas and Cardellini, Matteo and Dekker, Jip J. and Harabor, Daniel and Maratea, Marco and Vallati, Mauro},
  title =	{{Constraint-Based In-Station Train Dispatching}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{33:1--33:24},
  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.2025.33},
  URN =		{urn:nbn:de:0030-drops-238941},
  doi =		{10.4230/LIPIcs.CP.2025.33},
  annote =	{Keywords: in-station train dispatching, train scheduling, railway scheduling, constraint programming, mixed-integer programming}
}

Document

Short Paper

DOI: 10.4230/LIPIcs.CP.2025.41

Modeling and Solving a Composite Structure Design Problem with Constraint Programming (Short Paper)

Authors: Miguel Antoons, Augustin Delecluse, Samih Zein, and Pierre Schaus

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

Abstract

Composite structures are composed of plies (layers) of carbon fibers. For each ply, one must decide its orientation from the set of possible angles: -45°, 0°, 45°, and 90°. The stack of plies must follow strict constraints on the chosen orientations to achieve mechanical properties of the composite, such as sufficient buckling load. The design problem becomes more complex when determining the stack of plies for a complete surface material, that does not require the same number of plies in every region of the surface. Not only must the orientations be selected in each region, but it is also necessary to decide which plies are discontinued between adjacent regions. Thanks to its declarative nature, Constraint Programming (CP) offers an elegant modeling of the constraints, making it easy for designers to activate or deactivate them as needed. We propose a CP model, implemented in MiniZinc. The performance of this model on synthetic yet realistic instances when solved by different exact solvers, including Mixed Integer Programming (MIP) solvers, demonstrates the superiority of CP over MIP on our MiniZinc model, and over a commercial solution implemented by an industrial partner. It opens up the adoption of CP as an efficient building block of Computer-Aided Design tools for composite structures. By making the model and instances publicly available, we also hope to facilitate the inclusion of this problem in CP solver competitions and stimulate further research in this area.

Cite as

Miguel Antoons, Augustin Delecluse, Samih Zein, and Pierre Schaus. Modeling and Solving a Composite Structure Design Problem with Constraint Programming (Short Paper). In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 41:1-41:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{antoons_et_al:LIPIcs.CP.2025.41,
  author =	{Antoons, Miguel and Delecluse, Augustin and Zein, Samih and Schaus, Pierre},
  title =	{{Modeling and Solving a Composite Structure Design Problem with Constraint Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{41:1--41:9},
  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.2025.41},
  URN =		{urn:nbn:de:0030-drops-239022},
  doi =		{10.4230/LIPIcs.CP.2025.41},
  annote =	{Keywords: Constraint Programming, Composite Structures, Design Rules, MiniZinc}
}

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.CP.2025.24

The Work Task Variation Problem

Authors: Mikael Z. Lagerkvist and Magnus Rattfeldt

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

Abstract

This paper introduces the Work Task Variation (WTV) problem, a novel scheduling post-processing challenge focused on improving worker shift quality by rearranging tasks within their assigned time slots. The objective is to avoid excessively short or long durations of specific task types, creating smoother and more ergonomic work patterns. We present RosterLogic Variation, a constraint-based local search (CBLS) inspired solver originally developed at Optischedule and successfully deployed in real-world retail settings. This solver rapidly improves existing schedules using tailored invariants and heuristics. We also provide a complete MiniZinc model and a set of generated realistic publicly available benchmark instances. We compare our solver’s performance with that of modern CP solvers using the MiniZinc model. Contemporary state-of-the-art CP solvers are approaching the interactive performance of our CBLS solver for coarse planning, representing a significant advancement since the original design and implementation of our solver.

Cite as

Mikael Z. Lagerkvist and Magnus Rattfeldt. The Work Task Variation Problem. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 24:1-24:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{lagerkvist_et_al:LIPIcs.CP.2025.24,
  author =	{Lagerkvist, Mikael Z. and Rattfeldt, Magnus},
  title =	{{The Work Task Variation Problem}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{24:1--24:23},
  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.2025.24},
  URN =		{urn:nbn:de:0030-drops-238850},
  doi =		{10.4230/LIPIcs.CP.2025.24},
  annote =	{Keywords: Constraint-Based Local Search, Constraint Programming, Metaheuristics, Scheduling}
}

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.2025.7

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.2025.10

Unit Types for MiniZinc

Authors: Jip J. Dekker, Jason Nguyen, Peter J. Stuckey, and Guido Tack

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

Abstract

Discrete optimization models often reason about discrete sets of objects, but discrete optimization solvers only deal with integers. One of the key challenges when building models for discrete optimization problems is avoiding bugs. Because the model only defines constraints, decisions, and an objective that are then run on a solver, bugs in the model can be very difficult to track down. Hence, modelling languages should have strong type systems to detect as many bugs as possible at the modelling level. In this paper, we propose unit types for MiniZinc. Unit types allow us to differentiate between different integers appearing in the model. Almost all integer decisions in models are either about a set of objects or some measurable resource type. Using unit types, we can add more type safety to our models by avoiding confusion of decisions on different resource types. Compared to other programming languages, unit types in our proposal are unusual. MiniZinc models often deal with multiple levels of granularity of the same resource, e.g., scheduling to the minute, but doing resource allocation on the half day, or use an unspecified granularity, e.g., the same job-shop scheduling model could use task durations given in minutes or days. Our proposed unit types also differentiate between coordinate unit types, e.g., the time when an event occurred, and the usual delta unit types, e.g., the time difference between two events. Errors arising from mixing coordinate and delta types can be very challenging to debug, so we extend the type system to track this for us.

Cite as

Jip J. Dekker, Jason Nguyen, Peter J. Stuckey, and Guido Tack. Unit Types for MiniZinc. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dekker_et_al:LIPIcs.CP.2025.10,
  author =	{Dekker, Jip J. and Nguyen, Jason and Stuckey, Peter J. and Tack, Guido},
  title =	{{Unit Types for MiniZinc}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{10:1--10:20},
  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.2025.10},
  URN =		{urn:nbn:de:0030-drops-238718},
  doi =		{10.4230/LIPIcs.CP.2025.10},
  annote =	{Keywords: Modelling, Type Safety, Unit Types}
}

Document

DOI: 10.4230/LIPIcs.CP.2025.13

Disjunctive Scheduling in Tempo

Authors: Emmanuel Hebrard

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

Abstract

In this paper we introduce a constraint programming lazy clause and literal generation solver embarking ideas from SAT Modulo theories. A key aspect of the solver are Boolean variables with an associated semantic in difference logic, i.e., systems of binary numeric difference constraints or edges, making it particularly adapted to scheduling and other temporal problems. We apply this solver to disjunctive scheduling problems, where edges are used as branching variables, can be inferred via the edge finding rule as well as by transitivity reasoning, and can in turn strengthen propagation via temporal graph reasoning. Our experiments on job-shop scheduling show that a deep integration of these techniques makes our solver competitive with state-of-the-art approaches on these problems.

Cite as

Emmanuel Hebrard. Disjunctive Scheduling in Tempo. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hebrard:LIPIcs.CP.2025.13,
  author =	{Hebrard, Emmanuel},
  title =	{{Disjunctive Scheduling in Tempo}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{13:1--13:22},
  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.2025.13},
  URN =		{urn:nbn:de:0030-drops-238746},
  doi =		{10.4230/LIPIcs.CP.2025.13},
  annote =	{Keywords: Scheduling, Constraint solvers, Clause-learning}
}

Document

DOI: 10.4230/LIPIcs.SAT.2025.30

Bridging Language Models and Symbolic Solvers via the Model Context Protocol

Authors: Stefan Szeider

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

Abstract

This paper presents the MCP Solver, a system that bridges large language models with symbolic solvers through the Model Context Protocol (MCP). The system includes a server and a client component. The server provides an interface to constraint programming (via MiniZinc Python), propositional satisfiability and maximum satisfiability (both via PySAT), and SAT modulo Theories (via Python Z3). The client contains an agent that connects to the server via MCP and uses a language model to autonomously translate problem statements (given in English) into encodings through an incremental editing process and runs the solver. Our experiments demonstrate that this neurosymbolic integration effectively combines the natural language understanding of language models with robust solving capabilities across multiple solving paradigms.

Cite as

Stefan Szeider. Bridging Language Models and Symbolic Solvers via the Model Context Protocol. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 30:1-30:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{szeider:LIPIcs.SAT.2025.30,
  author =	{Szeider, Stefan},
  title =	{{Bridging Language Models and Symbolic Solvers via the Model Context Protocol}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{30:1--30:12},
  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.2025.30},
  URN =		{urn:nbn:de:0030-drops-237649},
  doi =		{10.4230/LIPIcs.SAT.2025.30},
  annote =	{Keywords: Large Language Models, Agents, Constraint Programming, Satisfiability Solvers, Maximum Satisfiability, SAT Modulo Theories, Model Context Protocol}
}

Document

DOI: 10.4230/OASIcs.SLATE.2025.8

An Architecture for Composite Combinatorial Optimization Solvers

Authors: Khalil Chrit, Jean-François Baffier, Pedro Patinho, and Salvador Abreu

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract

In this paper, we introduce elements for MoSCO, a framework for building hybrid metaheuristic-based solvers from a collection of reusable base components. The framework is implemented in Julia and provides a modular architecture for composing solvers through a pipeline-based approach. The modular design of MoSCO supports the creation of reusable components and adaptable solver strategies for various Constraint Satisfaction Problems (CSPs) and Constraint Optimization Problems (COPs). We validate MoSCO’s utility through practical examples, demonstrating its effectiveness in reconstructing established metaheuristics and enabling the creation of novel solver configurations. This work lays the foundation for future developments in automated solver construction and parameter optimization.

Cite as

Khalil Chrit, Jean-François Baffier, Pedro Patinho, and Salvador Abreu. An Architecture for Composite Combinatorial Optimization Solvers. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chrit_et_al:OASIcs.SLATE.2025.8,
  author =	{Chrit, Khalil and Baffier, Jean-Fran\c{c}ois and Patinho, Pedro and Abreu, Salvador},
  title =	{{An Architecture for Composite Combinatorial Optimization Solvers}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{8:1--8:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.8},
  URN =		{urn:nbn:de:0030-drops-236885},
  doi =		{10.4230/OASIcs.SLATE.2025.8},
  annote =	{Keywords: Hybrid Metaheuristics, DSL}
}

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