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13th Hellenic Conference on Artificial Intelligence

作者： Iosif, Panteleimon Ploskas, Nikolaos Stergiou, Kostas Univ Western Macedonia Kozani Greece

ISBN: (纸本)9798400709821

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. In the p-median problem we seek to minimize the sum of distances between demand points and their nearest facility, whereas in the p-dispersion problem we seek to maximize the closest distance between any two facilities. Recently, a variant of p-dispersion where distance constraints exist between facilities was studied from a constraint programming (CP) and Integer Linear programming (ILP) perspective. An incomplete CP solver that uses a greedy heuristic to prune branches during search was shown to significantly outperform the ILP solver Gurobi and the CP solver OR-Tools in terms of execution time. Following that work, we consider a variant of the p-median problem where distance constraints exist between facilities and between facilities and demand points. This problem can be used to model the requirements that arise when locating semi-obnoxious facilities. We first introduce ILP and CP models and implement them in Gurobi and OR-Tools. Then, we demonstrate how a heuristic CP solver can be developed and applied on the p-median problem with distance constraints, comparing it to Gurobi and OR-Tools.

关键词： constraint programming facility location distance constraints optimization

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Efficiently Mining Closed Interval Patterns with constraint programming 1

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21st International Conference on the Integration of constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Bekkoucha, Djawad Ouali, Abdelkader Boizumault, Patrice Cremilleux, Bruno Normandie Univ UNICAEN ENSICAEN CNRSGREYC Caen France

ISBN: (数字)9783031605970

ISBN: (纸本)9783031605963;9783031605970

constraint programming (CP) has become increasingly prevalent in recent years for performing pattern mining tasks, particularly on binary datasets. While numerous CP models have been designed for mining on binary data, there does not exist any model designed for mining on numerical datasets. Therefore these kinds of datasets need to be pre-processed to fit the existing methods. Afterward a post-processing is also required to recover the patterns into a numerical format. This paper presents two CP approaches for mining closed interval patterns directly from numerical data. Our proposed models seamlessly execute pattern mining tasks without any loss of information or the need for preor post-processing steps. Experiments conducted on different numerical datasets demonstrate the effectiveness of our proposed CP models compared to other methods.

关键词： constraint programming Pattern Mining Numerical Data

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A new variable neighbourhood search with a constraint programming search strategy for the open shop scheduling problem with operation repetitions

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ENGINEERING OPTIMIZATION 2022年第9期54卷 1563-1582页

作者： de Abreu, Levi Ribeiro Guimaraes Araujo, Kennedy Anderson Prata, Bruno de Athayde Nagano, Marcelo Seido Moccellin, Joao Vitor Univ Sao Paulo Dept Prod Engn Sao Carlos Brazil Univ Sao Paulo Dept Appl Math Sao Paulo Brazil Univ Fed Ceara Dept Ind Engn Fortaleza Ceara Brazil

This article presents a new variant for the open shop scheduling problem, the open shop scheduling problem with repetitions (OSSPR), where the jobs can be processed on any machine more than once (operation by operation). Thereby, all the jobs can be scheduled in an unconstrained way, substantially increasing the number of feasible solutions in comparison with the classical open shop. The OSSPR has many applications in automotive and maintenance actives. To solve the problem, a mixed-integer linear programming model is presented and a new constraint programming model is proposed. Since the problem under study is NP-hard, a new efficient variable neighbourhood search is proposed using variable search strategies through the proposed constraint programming model. The objective function is makespan minimization, and it uses the lower bound deviation as performance criterion. Computational results show very good performance of the proposed metaheuristic on the instances tested.

关键词： Production sequencing makespan constraint programming exact methods

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Best of Both Worlds: Solving the Cyclic Bandwidth Problem by Combining Pre-existing Knowledge and constraint programming Techniques 24th

Best of Both Worlds: Solving the Cyclic Bandwidth Problem by...

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24th International Conference on Computational Science (ICCS)

作者： Fertin, Guillaume Monfroy, Eric Vasconcellos-Gaete, Claudia Nantes Univ Ecole Cent Nantes CNRS LS2NUMR 6004 F-44000 Nantes France Univ Angers LERIA Angers France

ISBN: (纸本)9783031637742;9783031637759

Given an optimization problem, combining knowledge from both (i) structural or algorithmic known results and (ii) new solving techniques, helps gain insight and knowledge on the aforementioned problem by tightening the gap between lower and upper bounds on the sought optimal value. Additionally, this gain may be further improved by iterating (i) and (ii) until a fixed point is reached. In this paper, we illustrate the above through the classical Cyclic Bandwidth problem, an optimization problem which takes as input an undirected graph G = (V, E) with |V| = n, and asks for a labeling. of V in which every vertex v takes a unique value phi(v) is an element of [1;n], in such a way that B-c(G, phi) = max{min(uv)is an element of E(G){|phi(u)-phi(v)|, n-|phi(u)-phi(v)|}}, called the cyclic bandwidth of G, is minimized. Using the classic benchmark from the Harwell-Boeing sparse matrix collection introduced in [16], we show how to combine (i) previous results from the Cyclic Bandwidth literature, and (ii) new solving techniques, which we first present, and then implement, starting from the best results obtained in step (i). We show that this process allows us to determine the optimal cyclic bandwidth value for half of the instances of our benchmark, and improves the best known bounds for a large number of the remaining instances.

关键词： Graph Labeling Cyclic Bandwidth Lower Bounds Upper Bounds constraint programming Solver

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A collection of constraint programming models for the three-dimensional stable matching problem with cyclic preferences

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constraintS 2022年第3期27卷 249-283页

作者： Cseh, Agnes Escamocher, Guillaume Genc, Begum Quesada, Luis Ctr Econ & Reg Studies Inst Econ Budapest Hungary Univ Coll Dublin Insight Ctr Data Analyt Dublin 4 Cork Dublin Ireland Univ Coll Cork Sch Comp Sci & Informat Technol Cork Ireland Confirm Ctr Smart Mfg Cork V94 C928 Ireland

We introduce five constraint models for the 3-dimensional stable matching problem with cyclic preferences and study their relative performances under diverse configurations. While several constraint models have been proposed for variants of the two-dimensional stable matching problem, we are the first to present constraint models for a higher number of dimensions. We show for all five models how to capture two different stability notions, namely weak and strong stability. Additionally, we translate some well-known fairness notions (i.e. sex-equal, minimum regret, egalitarian) into 3-dimensional matchings, and present how to capture them in each model. Our tests cover dozens of problem sizes and four different instance generation methods. We explore two levels of commitment in our models: one where we have an individual variable for each agent (individual commitment), and another one where the determination of a variable involves pairing the three agents at once (group commitment). Our experiments show that the suitability of the commitment depends on the type of stability we are dealing with, and that the choice of the search heuristic can help improve performance. Our experiments not only brought light to the role that learning and restarts can play in solving this kind of problems, but also allowed us to discover that in some cases combining strong and weak stability leads to reduced runtimes for the latter.

关键词： Three-dimensional stable matching with cyclic preferences 3DSM-cyc constraint programming Fairness

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Job Scheduling for HPC Clusters: constraint programming vs. Backfilling Approaches 18

Job Scheduling for HPC Clusters: Constraint Programming vs. ...

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18th International Conference on Distributed and Event-Based Systems (DEBS)

作者： Goponenko, Alexander, V Lamar, Kenneth Allan, Benjamin A. Brandt, James M. Dechev, Damian Univ Cent Florida Orlando FL 32816 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9798400704437

Scheduling problems are naturally formulated in terms of constraint programming (CP), yet the application of CP-based approaches for job scheduling on High-Performance Computing (HPC) clusters remains underexplored. This study aims to bridge this gap by analyzing the scheduling of diverse real workload traces using IBM ILOG CP Optimizer. The analysis considers not just the basic metrics Average Bounded Slowdown and Average Response Time, but also Area-Weighted Average Response Time and Level-2 Priority-Weighted Specific Time, which measure packing efficiency and fairness. For each workload trace and metric combination, schedules produced through optimizing the metric with CP Optimizer are compared against schedules generated by the variants of the list scheduling with backfilling that are most suitable for the metric. The CP-based scheduling improves scheduling quality in most of the examined cases. The analysis of the metrics that represent different scheduling goals uncovers several non-trivial insights. Presently, CP Optimizer still encounters scalability issues with large wait queues and non-linear objective functions. Nonetheless, it often demonstrates improvements in packing efficiency, which make CP techniques attractive, particularly in scenarios where high-maintenance clusters run a moderate number of large, rigid, well-characterized jobs.

关键词： high-performance computing parallel job scheduling schedule quality constraint programming CP Optimizer packing efficiency fairness weighted average response time

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Solving the integrated process planning and scheduling problem using an enhanced constraint programming-based approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第18期60卷 5505-5522页

作者： Shi, Ganquan Yang, Zhouwang Xu, Yang Quan, Yuchen Univ Sci & Technol China Sch Math Sci Hefei Peoples R China Univ Sci & Technol China Sch Data Sci Hefei Peoples R China

Due to various factors of flexibility introduced into manufacturing systems, researchers have gradually shifted their focus to the integrated process planning and scheduling (IPPS) problem to improve productivity. The previous literature rarely associates IPPS with constraint programming, even though constraint programming has achieved success in the scheduling field. Furthermore, existing approaches are usually customized to certain types of IPPS problems and cannot handle the general problem. In this paper, with a view to obtaining the optimal AND/OR graph automatically, a depth first search generating algorithm is designed to convert the type-1 IPPS problem into our approach's standard input format. Moreover, we propose an approach based on enhanced constraint programming to cope with the general problem, employing advanced schemes to enhance the constraint propagation and improve the search efficiency. Our approach is implemented on ORTOOLS, and its superiority is verified by testing on 15 benchmarks with 50 instances. Experimental results indicate that 41 instances are solved optimally, among which the optimality of the solutions for 20 instances is newly confirmed, and the solutions of six instances are improved. Our approach is the first method to reach the overall optimum in the most influential benchmark with 24 instances.

关键词： integrated process planning and scheduling constraint programming AND OR graph graph-based model depth first search

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A Machine Learning Method to Reveal Closed Sets of Common Features of Objects Using constraint programming

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AUTOMATION AND REMOTE CONTROL 2022年第12期83卷 1995-2005页

作者： Zuenko, A. A. Kola Sci Ctr Russian Acad Sci Apatity 184209 Murmansk Russia

solve machine learning problems, we have developed a method to identify closed sets of common features of objects (patterns) of the training sample. The novelty of the method lies in the fact that it is implemented within the concept of constraint programming and uses a new type of table constraints-compressed tables of the D-type-for internal representation and processing of the training sample. Search reduction is achieved by applying the proposed method of branching the search tree and using partial order relations on sets of objects (features) to prune unpromising branches. The method has a computational complexity estimate that for some types of input data is better than the estimates obtained for the studied prototypes.

关键词： machine learning constraint programming table constraint closed pattern formal concept

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constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2023年 117卷

作者： Zhu, Xuedong Son, Junbo Zhang, Xi Wu, Jianguo Peking Univ Coll Engn Dept Ind Engn & Management Beijing 100871 Peoples R China Univ Delaware Alfred Lerner Coll Business & Econ Newark DE 19716 USA

The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving de-sirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, sev-eral approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS prob-lem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches. & COPY;2022 Elsevier Ltd. All rights reserved.

关键词： Integrated process planning and scheduling constraint programming Logic -based Benders decomposition Forbidden intervals

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How constraint programming can help chemists to generate Benzenoid structures and assess the local Aromaticity of Benzenoids

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constraintS 2022年第3期27卷 192-248页

作者： Carissan, Yannick Hagebaum-Reignier, Denis Prcovic, Nicolas Terrioux, Cyril Varet, Adrien Aix Marseille Univ ISM2 Cent Marseille CNRS Marseille France Aix Marseille Univ Univ Toulon LIS CNRS Marseille France

Benzenoids are a subfamily of hydrocarbons (molecules that are only made of hydrogen and carbon atoms) whose carbon atoms form hexagons. These molecules are widely studied in theoretical chemistry and have a lot of concrete applications. Then, there is a lot of problems relative to this subject, like the enumeration of all its Kekule structures (i.e. all valid configurations of double bonds). In this article, we focus our attention on two issues: the generation of benzenoid structures and the assessment of the local aromaticity. On the one hand, generating benzenoids that have certain structural and/or chemical properties (e.g. having a given number of hexagons or a particular structure from a graph viewpoint) is an interesting and important problem. It constitutes a preliminary step for studying their chemical properties. In this paper, we show that modeling this problem in Choco Solver and just letting its search engine generate the solutions is a fast enough and very flexible approach. It can allow to generate many different kinds of benzenoids with predefined structural properties by posting new constraints, saving the efforts of developing bespoke algorithmic methods for each kind of benzenoids. On the other hand, we want to assess the local aromaticity of a given benzenoid. This is a central issue in theoretical chemistry since aromaticity cannot be measured. Nowadays, computing aromaticity requires quantum chemistry calculations that are too expensive to be used on medium to large-sized molecules. In this article, we describe how constraint programming can be useful in order to assess the aromaticity of benzenoids. Moreover, we show that our method is much faster than the reference one, namely NICS.

关键词： constraint programming Graph variables and constraints Modeling Theoretical chemistry

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