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

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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CoPTIC: constraint programming Translated Into C 29th

CoPTIC: Constraint Programming Translated Into C

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29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS)

作者： Lester, Martin Mariusz Univ Reading Reading Berks England

ISBN: (纸本)9783031308192;9783031308208

constraint programming systems allow a diverse range of problems to be modelled and solved. Most systems require the user to learn a new constraint programming language, which presents a barrier to novice and casual users. To address this problem, we present the CoPTIC constraint programming system, which allows the user to write a model in the well-known programming language C, augmented with a simple API to support using a guess-and-check paradigm. The resulting model is at most as complex as an ordinary C program that uses naive brute force to solve the same problem. CoPTIC uses the bounded model checker CBMC to translate the model into a SAT instance, which is solved using the SAT solver CaDiCaL. We show that, while this is less efficient than a direct translation from a dedicated constraint language into SAT, performance remains adequate for casual users. CoPTIC supports constraint satisfaction and optimisation problems, as well as enumeration of multiple solutions. After a solution has been found, CoPTIC allows the model to be run with the solution;this makes it easy to debug a model, or to print the solution in any desired format.

关键词： constraint programming bounded model checking C programming language

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An Efficient constraint programming Approach for Solving the Multi-Workshop Facility Layout Problem 10

An Efficient Constraint Programming Approach for Solving the...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Zhang, Yu Zhang, Zeqiang Chen, Feng He, Zongxing School of Mechanical Engineering Southwest Jiaotong University Chengdu610031 China Chengdu SIWI High-Tech Industrial Co. Ltd. Chengdu610031 China

ISBN: (纸本)9798331510138

The multi-workshop facility layout problem (MWFLP) focuses on the optimal distribution and placement of departments across multiple workshops to maximize material handling efficiency and space utilization in manufacturing systems. This study introduces constraint programming (CP) techniques to the facility layout problem and proposes a multi-objective optimization framework to address the MWFLP. The model employs interval variables to accurately represent facility dimensions and integrates cumulative functions to rigorously enforce non-overlapping and spatial constraints. To improve computational efficiency, symmetry-breaking constraints are introduced to minimize redundant solution spaces and accelerate the search for better layout. The effectiveness of the CP model is demonstrated through comprehensive experimental evaluations, where it consistently delivers superior solution quality and computational efficiency compared to the mixed-integer programming model. In multi-objective optimization scenarios, the CP framework outperforms the multi-objective particle swarm optimization method, offering enhanced solution precision and stability across diverse problem scales. © 2024 IEEE.

关键词： constraint programming

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A comprehensive tolerant algebraic side-channel attack over modern ciphers using constraint programming

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JOURNAL OF CRYPTOGRAPHIC ENGINEERING 2022年第2期12卷 197-228页

作者： Liu, Fanghui Cruz, Waldemar Michel, Laurent Univ Connecticut Sch Engn Comp Sci & Engn Dept Storrs CT 06269 USA Univ Connecticut Sch Engn Comp Sci & Engn Dept Synchrony Chair Cybersecur Storrs CT 06269 USA

Tolerant algebraic side-channel attack (TASCA) exploits side-channel information with an algebraic formulation of a cipher to exploit its weaknesses and recover a secret key. Its inputs consist of a side-channel trace of an encryption and the clear and cipher texts. TASCA demonstrated that pseudo-Boolean optimization can successfully recover a key with reasonable computational efforts. Unlike Boolean Satisfiability (SAT), constraint programming (CP) is an optimization technology that favors high-level, rich and expressive models that is ideal to naturally model and solve cryptanalysis challenges. It offers direct encoding of bit-wise operations and avoids costly bit-blasting formulation required by SAT and pseudo-Boolean solvers. TASCA-CP is an embodiment of TASCA and is used to attack AES-128 as well as AES-256 to recover keys when noisy side-channel measurements are available. It achieves this task orders of magnitude faster than the original TASCA approach. TASCA-CP, with its performance, enables cryptanalysts to explore larger key-sizes and probe weaknesses of ciphers. The article demonstrates, with an attack on Keeloq, that a high-level modeling approach is essential to easily adapt to different ciphers. The empirical evaluation establishes the performance of the system when compared to the original TASCA implementation on modern IP solvers and identical hardware.

关键词： Algebraic side-channel attack AES Cryptography Block cipher constraint programming Optimization

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Solving the time-discrete winter runway scheduling problem: A column generation and constraint programming approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期299卷 674-689页

作者： Pohl, Maximilian Artigues, Christian Kolisch, Rainer Tech Univ Munich TUM Sch Management Arcisstr 21 D-80333 Munich Germany CNRS Lab Anal & Architecture Syst 7 Ave Colonel Roche F-31031 Toulouse France

We address the runway scheduling problem under consideration of winter operations. During snowfall, runways have to be temporarily closed in order to clear them from snow, ice and slush. We propose an integrated optimization model to simultaneously plan snow removal for multiple runways and to assign runways and take-off and landing times to aircraft. For this winter runway scheduling problem, we present a time-discrete binary model formulation using clique inequalities and an equivalent constraint programming model. To solve the winter runway scheduling problem optimally, we propose an exact solution methodology. Our start heuristic based on constraint programming generates a feasible initial start solution. We use a column generation scheme, which we initialize with a heuristic solution, to identify all variables of the binary program which are required to solve it optimally. Finally, we apply a branch-andbound procedure to our resulting binary program. Additionally, we present an enhanced time discretization method to balance model size and solution quality. We apply our algorithm to realistic instances from a large international airport. An analysis of resulting model sizes proves the ability of our approach to significantly reduce the number of required variables and constraints of the time-discrete binary program. We also show that our method computes optimal schedules in a short amount of time and often outperforms a time-continuous formulation as well as a pure constraint programming approach. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Scheduling Airport operations Column generation constraint programming

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Generative magic and designing magic performances with constraint programming

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constraintS 2022年第3期27卷 168-191页

作者： de Azevedo Silveira, Guilherme Alura Rua Vergueiro 3185 BR-04101300 Sao Paulo SP Brazil

Professional magicians employ the use of interesting properties of a deck of cards to create magical effects. These properties were traditionally discovered through trial and error, the application of heuristics or analytical proofs. Those proofs come from diverse mathematical areas such as the set, number and graph theory. We discuss the limitations of relying on humans for such methods and present how professional magicians can use constraint programming as a computer-aided design tool (CAD) to search for desired properties in a deck of cards. Furthermore, we implement a solution in Python by making use of generative magic to design a new effect, demonstrating how this process broadens the level of freedom a magician can decree to their volunteers while retaining control of the outcomes of the magic. Finally, we demonstrate that the model can be easily adapted to multiple languages by presenting multiple variations of the effect supporting American English and Brazilian Portuguese.

关键词： constraint Generative design Computer aided design constraint programming Generative magic Magical performance

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New mathematical and constraint programming models for U-type assembly line balancing problems with assignment restrictions

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ENGINEERING OPTIMIZATION 2022年第8期54卷 1289-1304页

作者： Pinarbasi, Mehmet OSTIM Tech Univ Dept Ind Engn Ankara Turkey

In this study, the U-type assembly line balancing problem (UALBP) with assignment restrictions (AR-UALBP) is considered. Linked and incompatible distance and station restrictions are taken into account. New mathematical programming (MP) and constraint programming (CP) models are proposed. The objective function of the models is minimizing the cycle time for a given number of workstations. In addition to this objective, line efficiency and CPU time are also considered as other performance measurements. Numerical experiments on some problems from the literature are performed to evaluate the effectiveness of the proposed models. The results are compared with the results of the CP model of the straight ALBP with assignment restrictions and also the results from MP and CP models are compared with each other. The numerical results indicate that the proposed CP and MP models are more effective in obtaining better and optimal results when solving the AR-UALBP.

关键词： Assembly line balancing U-type problem assignment restrictions mathematical programming constraint programming

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Mixed-integer linear programming, constraint programming and column generation approaches for operating room planning under block strategy

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APPLIED MATHEMATICAL MODELLING 2022年 105卷 438-453页

作者： Ghandehari, Najmeh Kianfar, Kamran Univ Isfahan Fac Adm Sci & Econ Dept Management Esfahan *** Iran Univ Isfahan Fac Engn Esfahan *** Iran

The planning of operating rooms under block strategy is addressed in this study. The decisions are about opening the operating rooms and assigning specialties and surgeons to blocks at the tactical level, and sequencing the surgeries at the operational level. This problem aims to minimize the costs of opening the operating rooms and their overtime, the waiting costs of patients, and the surgeons' idle costs. We propose two mixed integer linear programming models, a constraint programming (CP) model and a constraint programming-based column generation (CPCG) method for handling the problem. The performance of the models is evaluated by random test instances. The results indicate that CP and CPCG models are more efficient than the linear programming models in terms of computational time, and the number of variables and constraints. The proposed method CPCG generates optimal solutions for problem instances of up to 30 surgeries in less than 4 min. The CP model finds the optimal solutions in about one minute but proving the optimality of the found solutions is time-consuming in some instances. The maximum optimality gap for the proposed two-step linear programming model is 2%, while its run time is less than 20 s. A sensitivity analysis is done on the main parameters of the problem like objectives' weights, opening cost of ORs, unit waiting cost of patients, and the maximum available time in surgery blocks. Among the three objectives, the unit waiting cost of patients has the most sensitivity to variations of the objective function weights.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Operating room planning Block strategy Master surgery schedule Mixed-integer linear programming constraint programming Column generation

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