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3rd World Congress on Formal Methods (FM)

作者： Ziat, Ghiles Marechal, Alexandre Pelleau, Marie Mine, Antoine Truchet, Charlotte Sorbonne Univ CNRS LIP6 F-75005 Paris France Univ Grenoble Alpes CNRS VERIMAG F-38000 Grenoble France Univ Cote DAzur CNRS I3S F-06100 Nice France Univ Nantes LS2N UMR 6004 F-44300 Nantes France

ISBN: (纸本)9783030549961;9783030549978

This paper investigates the use of abstract domains from Abstract Interpretation (AI) in the field of constraint programming (CP). CP solvers are generally very efficient on a specific constraint language, but can hardly be extended to tackle more general languages, both in terms of variable representation (discrete or continuous) and constraint type (global, arithmetic, etc.). For instance, linear constraints are usually solved with linear programming techniques, but non-linear ones have to be either linearized, reformulated or sent to an external solver. We approach this problem by adapting to CP a popular domain construction used to combine the power of several analyses in AI: the reduced product. We apply this product on two well-known abstract domains, Boxes and Polyhedra, that we lift to constraint solving. Finally we define general metrics for the quality of the solver results, and present a benchmark accordingly. Experiments show promising results and good performances.

关键词： constraint programming

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A Real-World Transport Scheduler Applied to Australian Sugarcane Industry 23

A Real-World Transport Scheduler Applied to Australian Sugar...

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23rd IEEE International Conference on Intelligent Transportation Systems (ITSC)

作者： Masoud, Mahmoud Kozan, Erhan Liu, Shi Qiang Elhenawy, Mohammed Corry, Paul Burdett, Robert D'Ariano, Andrea Queensland Univ Technol Ctr Accid Res & Rd Safety Queensland Brisbane Qld 4059 Australia Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Queensland Univ Technol Sch Math Sci Brisbane Qld Australia Univ Roma Tre Sez Informat & Automaz Dipartimento Ingn I-00146 Rome Italy

ISBN: (纸本)9781728141497

This paper describes a new approach to develop a real-world automated scheduler applicable for Australian sugarcane industry. In Australia, the transport sector plays a critical role in raw sugarcane harvest and accounts for over 35% of the total cost of raw sugar production. The generation of an optimised schedule can bring the following practical benefits: eliminate bin supply delays to harvesters, minimise the number of locomotives/bins, reduce the locomotive shifts, control the sugarcane age/quality, etc. To generate such a scheduler, a new optimisation approach is developed based on job shop scheduling techniques using constraint programming and mixed integer programming. The proposed approach can produce solutions for small-scale and large-scale cases in agriculture/crops transport systems in a reasonable time. Mixed integer programming focuses on objective function using linear relaxation to prune suboptimal solutions, while constraint programming focuses on the model using filtering algorithms to eliminate infeasible candidate solutions. The applicability of the developed scheduler has been validated by a real-world case study for Kalamia Mill in Queensland, Australia. Following from the validation and discussion, it is concluded that the automated scheduler would be a valuable optimisation tool for transport modellers in Australian sugarcane industry.

关键词： Sugarcane industry rail scheduler job shop scheduling mixed integer programming constraint programming

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Inductive definitions in constraint programming 13

Inductive definitions in constraint programming

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Proceedings of the Thirty-Sixth Australasian Computer Science Conference - Volume 135

作者： Rehan Abdul Aziz Peter J. Stuckey Zoltan Somogyi The University of Melbourne and National ICT Australia (NICTA)

ISBN: (纸本)9781921770203

constraint programming (CP) and answer set programming (ASP) are two declarative paradigms used to solve combinatorial problems. Many modern solvers for both these paradigms rely on partial or complete Boolean representations of the problem to exploit the extremely efficient techniques that have been developed for solving propositional satisfiability problems. This convergence on a common representation makes it possible to incorporate useful features of CP into ASP and vice versa. There has been significant effort in recent years to integrate CP into ASP, primarily to overcome the grounding bottleneck in traditional ASP solvers that exists due to their inability to handle integer variables efficiently. On the other hand, ASP solvers are more efficient than CP systems on problems that involve inductive definitions, such as reachability in a graph. Besides efficiency, ASP syntax is more natural and closer to the mathematical definitions of such concepts. In this paper, we describe an approach that adds support for answer set rules to a CP system, namely the lazy clause generation solver chuffed. This integration also naturally avoids the grounding bottleneck of ASP since constraint solvers natively support finite domain variables. We demonstrate the usefulness of our approach by comparing our new system against two competitors: the state-of-the-art ASP solver clasp, and clingcon, a system that extends clasp with CP capabilities.

关键词： answer set programming stable model semantics inductive definitions constraint programming

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Modeling Employee Flexible Work Scheduling As A Classification Problem

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Procedia computer science 2021年 192卷 3281-3290页

作者： Fred N Kiwanuka Louay Karadsheh Ja'far Alqatawna Anang Hudaya Muhamad Amin Higher Colleges of Technology Dubai Men's College UAE.

Many organizations have adapted flexible working arrangements during COVID19 pandemic because of restrictions on the number of employees required on site at any time. Unfortunately, current employee scheduling methods are more suited for compressed working arrangements. The problem of automating compressed employee scheduling has been studied by many researchers and is widely adopted by many organizations in an attempt to achieve high quality scheduling. During process of employee scheduling many constraints may have to be considered and may require negotiating a large dimension of constraints like in flexible working. These constraints make scheduling a challenging task in these working arrangements. Most scheduling algorithms are modeled as constraint optimization problems and suited for compressed work but for flexible working with large constraint dimensions, achieving accurate scheduling is even more challenging. In this research, we propose a machine learning approach that takes advantage of mining user-defined constraints or soft constraints and transform employee scheduling into a classification problem. We propose automatically extracting employee personal schedules like calendars in order to extract their availability. We then show how to use the extracted knowledge in a multi-label classification approach in order to generate a schedule for faculty staff in a University that supports flexible working. We show that the results of this approach are comparable to that of a constraint satisfaction and optimization method that is commonly used in literature. Results show that our approach achieved accuracy of 93.1% of satisfying constraints as compared to 92.7% of a common constraint programming approach.

关键词： constraint programming Data mining Employee Scheduling Machine Learning

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Optimizing constraint Satisfaction Problems by Regularization for the Sample Case of the Warehouse Location Problem 43rd

Optimizing Constraint Satisfaction Problems by Regularizatio...

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43rd German Conference on Artificial Intelligence (KI)

作者： Loeffier, Sven Liu, Ke Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Cottbus Germany

ISBN: (纸本)9783030582845;9783030582852

The performance of a constraint problem can often be improved by converting a subproblem into a single regular constraint. We describe a new approach to optimize constraint satisfaction (optimization) problems using constraint transformations from different kinds of global constraints to regular constraints, and their combination. Our transformation approach has two aims: 1. to remove redundancy originating from semantically overlapping constraints over shared variables and 2. to remove origins of backtracks in the search during the solution process. Based on the case study of the Warehouse Location Problem we show that our new approach yields a significant speed-up.

关键词： constraint programming CSP Refinement Optimizations Regular membership constraint Warehouse Location Problem

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Systematic methodology for generation and design of hybrid vehicle powertrains: Méthodologie de génération systématique et de conception des chaines de traction de véhicules hybrides

Systematic methodology for generation and design of hybrid v...

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作者： Kabalan, Bilal

学位级别：博士

Pour répondre aux objectifs de consommation des flottes de véhicules, au normes d’émissions de polluants et aux nouvelles demandes de l’usager, les constructeurs automobiles doivent développer des motorisations hybrides et électriques. Réaliser une chaine de traction hybride reste cependant une tâche difficile. Ces systèmes sont complexes et possèdent de nombreuses variables réparties sur différents niveaux : architecture, technologie des composants, dimensionnement et contrôle/commande. L’industrie manque encore d’environnements et d’outils pouvant aider à l’exploration de l’ensemble de l’espace de dimensionnement et à trouver la meilleure solution parmi tous ces niveaux. Cette thèse propose une méthodologie systématique pour répondre au moins partiellement à ce besoin. Partant d’un ensemble de composants, cette méthodologie permet de générer automatiquement tous les graphes d’architectures possibles en utilisant la technique de programmation par contraintes. Une représentation dédiée est développée pour visualiser ces graphes. Les éléments de boites de vitesse (embrayages, synchroniseurs) sont représentés avec un niveau de détails approprié pour générer de nouvelles transmission mécaniques sans trop complexifier le problème. Les graphes obtenus sont ensuite transformés en d’autres types de représentation : 0ABC Table (décrivant les connections mécaniques entre les composants), Modes Table (décrivant les modes de fonctionnement disponibles dans les architectures) et Modes Table + (décrivant pour chaque mode le rendement et le rapport de réduction global des chemins de transfert de l’énergie entre tous les composants). Sur la base de cette représentation, les nombreuses architectures générées sont filtrées et seules les plus prometteuses sont sélectionnées. Elles sont ensuite automatiquement évaluées et optimisées avec un modèle général spécifiquement développé pour calculer les performances et la consommation de toute les architectures générées. Ce modèle est insér

关键词： Dimensionnement de chaine de traction Véhicules électriques hybrides Optimisation Algorithmes génétiques Programmation dynamique Programmation par contraintes Génération automatique d’architectures Powertrain design,hybrid electric vehicles Optimization Sizing Genetic algorithms Dynamic programming constraint programming Architecture generation 620

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Maximizing Reliability of Data-Intensive Workflow Systems with Active Fault Tolerance Schemes in Cloud 13

Maximizing Reliability of Data-Intensive Workflow Systems wi...

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IEEE 13th International Conference on Cloud Computing (CLOUD)

作者： Li, Weiling Sun, Xiaoning Liao, Kewen Xia, Yunni Chen, Feifei He, Qiang Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Chongqing Univ Sch Comp Sci Chongqing Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia Australian Catholic Univ Peter Faber Business Sch Discipline Informat Technol Sydney NSW Australia Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9781728187808

Most existing researches on cloud workflow systems have focused on resource scheduling with the aims to minimize system delay under budget constraints or optimize system cost under deadline constraints. However, cloud providers cannot guarantee a failure-free cloud environment, a compact scheduling plan is prone to failure, thus, workflow system reliability has been identified as a critical and challenging issue in the volatile cloud environment. With the ability of cloud, it is easy for users to implement the active fault tolerance schemes, e.g., Scale-Out. However, it will lead to issues like security problem and extra management cost. In this paper, we first investigate Scale-Up and Scale-Hybrid schemes to fully explore the possibilities offered by the ability of cloud. We formally model the problem of optimizing the reliability of a cloud workflow system under budget constraints with these three fault-tolerance schemes. These optimization problems are discrete and non-convex. Thus, we propose a genetic algorithm based method for workflow fault tolerance (GA4WFT). Finally, we evaluate the effectiveness and efficiency of proposed GA4WFT with three different fault-tolerance schemes through experiments conducted on Amazon EC2 data.

关键词： Fault-tolerance data-intensive cloud workflow reliability constraint programming genetic algorithm

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Makespan Optimization in Open Shop Scheduling 1st

Makespan Optimization in Open Shop Scheduling

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1st International Conference on Emerging Trends in Mechanical Engineering, ICETME 2018

作者： Kesarawani, Prakash Kumar, Neeraj Mishra, Abhishek Department of Mechanical Engineering National Institute of Technology Delhi New Delhi110040 India

ISBN: (纸本)9789813299306

In today’s rapid production scenario, scheduling plays a dynamic role in planning. In this work, open shop scheduling problem related to a copper flexible braids manufacturing company is considered. In a scheduling problem, the purpose is to find the orders of jobs on specific machines with an objective to optimize the makespan. Scheduling can be either manual or automatic. Manual scheduling of operations is a difficult task. Hence, computational methods are used to automate and simplify the process. In this work, integer programming and constraint programming-based mathematical models are developed to tackle this problem. The branch-and-bound (B&B) algorithm is applied for integer programming model, and branch-and-cut (B&C) algorithm is applied for constraint programming model in order to get optimized makespan. A comparison of results obtained from both mathematical models is done for selection of optimized makespan and identification of model. © 2020, Springer Nature Singapore Pte Ltd.

关键词： constraint programming Integer programming Open shop scheduling (OSS)

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Exploring formal defeasible reasoning of large language models: A Chain-of-Thought approach

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Knowledge-Based Systems 2025年 319卷

作者： Li, Zhaoqun Chen, Chen Li, Mengze Liao, Beishui School of Philosophy Zhejiang University China College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence China

Defeasible reasoning, critical for commonsense reasoning and uncertainty handling, has garnered significant attention in AI community. This interest is particularly pronounced in the development and evaluation of Large Language Models (LLMs), which often involve reconciling inconsistent and incomplete knowledge. However, it remains uncertain whether LLMs possess generalizable defeasible reasoning abilities. Besides, the lack of a formal defeasible reasoning benchmark and appropriate evaluations limits further exploration in this domain. In this study, we aim to investigate the capacity of LLMs for defeasible reasoning, particularly within the framework of formal defeasible logic. Specifically, we select the popular defeasible logic framework, DeLP, as the basis for evaluating the LLMs’ defeasible logical reasoning capabilities. We initially create a synthetic dataset comprising logical programs that encompass a variety of DeLP programs with differing depths of reasoning. To address the challenges encountered during inference, we introduce a Chain-of-Thought (CoT) framework that prompts LLMs to conduct formal deduction and engage in multi-step defeasible reasoning, thereby enhancing problem-solving performance. Employing this argumentative solving approach, we observe that LLMs struggle to manage defeasible information effectively. This observation raises questions about whether contemporary LLMs possess reasoning abilities comparable to human intelligence, challenging the reliable deployment and advancement of AI systems in real-world scenarios. © 2025

关键词： constraint programming

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CONVJSSP: Convolutional Learning for Job-Shop Scheduling Problems 19

CONVJSSP: Convolutional Learning for Job-Shop Scheduling Pro...

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19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Wang, Tianze Payberah, Amir H. Vlassov, Vladimir Kth Royal Institute of Technology Department of Computer Science Stockholm Sweden

ISBN: (纸本)9781728184708

The Job-Shop Scheduling Problem (JSSP) is a well-known optimization problem with plenty of existing solutions. Although remarkable progress has been made in addressing the problem, most of the solutions require input from human experts. Deep Learning techniques, on the other hand, have proven successful in acquiring knowledge from data without using step-by-step instructions from humans. In this work, we propose a novel solution, called ConvJSSP, by applying Deep Learning to speed up the solving process of JSSPs and to reduce the need for human involvement. In ConvJSSP, we train a Convolutional Neural Network model for predicting the optimal makespan of JSSPs, and use the predicted makespan to accelerate the JSSP solving schema. Through the experiments, we compare several JSSP solving methods based on ConvJSSP approach with a state-of-the-art solution as a baseline, and show that ConvJSSP speeds up the problem solving up to 9% compared to the baseline method. © 2020 IEEE.

关键词： constraint programming

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