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13th Hellenic Conference on Artificial Intelligence

作者： Balafas, Vasileios Tsouros, Dimosthenis C. Ploskas, Nikolaos Stergiou, Kostas Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (纸本)9798400709821

constraint programming provides a powerful framework for modeling and solving combinatorial problems. However, manually defining the required constraints can be a challenging task that requires a high level of expertise. constraint acquisition (CA) techniques aim to semi-automate this process by learning constraints from examples of solutions and non-solutions. One important factor that can impact the effectiveness of CA is the diversity of the example solutions provided. This paper investigates how solution diversity influences passive learning approaches for CA across three distinct problems and various diversity metrics. Our results demonstrate that solution diversity significantly influences the quality of learned constraints, highlighting the importance of diverse solution sets. In addition, we show how we can predict whether a given set of solutions will enable accurate constraint learning using a machine learning (ML) model. Our experimental evaluation shows that the ML model can accurately predict the recall of the CA system based on the solution set's diversity metrics and the number of solutions.

关键词： constraint programming constraint Acquisition Diversity of Solutions Machine Learning

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Modeling the Integrated Flexible Job-Shop and Operator Scheduling in Flexible Manufacturing Systems 18

Modeling the Integrated Flexible Job-Shop and Operator Sched...

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18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Saber, Reza Ghorbani Leyman, Pieter Aghezzaf, El-Houssaine Univ Ghent Dept Ind Engn & Prod Design Technol Pk Zwijiaarde 46 B-9052 Ghent Belgium Flanders Make UGent Ind Syst Engn Sint Martens Latemlaan 2B B-8500 Kortrijk Belgium

This paper addresses the integrated flexible job shop and operators scheduling problem, introducing shift-based constraints on operators. We investigate how the advanced modeling and solution techniques, specifically, Mixed-Integer programming (MIP) and constraint programming (CP) perform on this intricate scheduling problem. We test the effectiveness of both the MIP and CP models on an illustrative example as well as on a set of larger instances and draw conclusions, which indicate the need to integrate these two models into some approximation scheme to tackle large-scale instances for this complex problem. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Scheduling Flexible Job Shop Scheduling problem Dual-Resource Constrained scheduling constraint programming Mixed Integer programming

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A CP-Based Automatic Tool for Instantiating Truncated Differential Characteristics 24th

A CP-Based Automatic Tool for Instantiating Truncated Differ...

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24th International Conference on Cryptology in India (INDOCRYPT)

作者： Delobel, Francois Derbez, Patrick Gontier, Arthur Rouquette, Loic Solnon, Christine Univ Clermont Auvergne CNRS Mines St Etienne LIMOS Clermont Ferrand France Univ Rennes CNRS IRISA Rennes France EPITA Res Lab LRE 14-16 Rue Voltaire F-94270 Le Kremlin Bicetre France Univ Lorraine LORIA F-54000 Nancy France INSA Lyon CITI INRIA CHROMA F-69621 Villeurbanne France

ISBN: (纸本)9783031562310;9783031562327

An important criteria to assert the security of a cryptographic primitive is its resistance against differential cryptanalysis. For word-oriented primitives, a common technique to determine the number of rounds required to ensure the immunity against differential distinguishers is to consider truncated differential characteristics and to count the number of active S-boxes. Doing so allows one to provide an upper bound on the probability of the best differential characteristic with a reduced computational cost. However, in order to design very efficient primitives, it might be needed to evaluate the probability more accurately. This is usually done in a second step, during which one tries to instantiate truncated differential characteristics with actual values and computes its corresponding probability. This step is usually done either with ad-hoc algorithms or with CP, SAT or MILP models that are solved by generic solvers. In this paper, we present a generic tool for automatically generating these models to handle all word-oriented ciphers. Furthermore the running times to solve these models are very competitive with all the previous dedicated approaches.

关键词： Differential cryptanalysis constraint programming Automatic tool

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An Optimization Model of Wagon-flow Allocation Considering Pick-up and Delivery Operations at the Railway Technical Station 20

An Optimization Model of Wagon-flow Allocation Considering P...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Tao, Shumei Ma, Liang Guo, Wentao Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sichuan Engn Res Ctr Train Operat Control Technol Chengdu 611756 Peoples R China China Railway Shenyang Bur Grp Co Ltd Dispatching Off Shenyang 110001 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

Railcars arriving at the railroad technical station include transfer and local railcars. Local railcars are loaded, unloaded, and inspected through pick-up and delivery operations before being marshalled into departure trains. The technical station wagon-flow allocation problem, as the core issue of its transportation scheduling, is essentially the process of selecting and matching various wagon-flow, which has a profound impact on the overall transportation capacity of the station. Therefore, this article first establishes a constraint programming model to focus on the coordinated optimization problem of wagon-flow allocation and pick-up and delivery operations at the railway technical station. constraints such as operational sequencing, wagon-flow continuity, locomotive usage, and full axle capacity are considered. The primary objective is to maximize the total priority of successfully assembled departure trains. The other objectives include maximizing shunting and track utilization rates and minimizing the number of sources of successful assembled wagon-flow. Additionally, a fast iterative solution algorithm with initial solutions is designed based on constraint propagation and constructive search strategy, achieving hierarchical iterative solution of the entire model. Finally, using actual data from a specific district station as a case study, the verification results show that the method proposed can solve the coordination among pick-up, delivery, disassembly and assembly operations. It can achieve comprehensive utilization of wagon-flow and coordinated wagon-flow allocation of departure trains based on decision-making on pick-up and delivery order, and the algorithm's solving efficiency meets the requirements of on-site timeliness.

关键词： constraint programming

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Solving Industrial Production Scheduling Challenges in the Era of Industry 4.0 and Green Manufacturing 7th

Solving Industrial Production Scheduling Challenges in the E...

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7th International Conference on Logistics Operations Management (GOL)

作者： Mimouni, Hafsa Jalid, Abdelilah Mohammed V Univ Rabat ENSAM Rabat Morocco

ISBN: (纸本)9783031686337;9783031686344

The evolution of production scheduling in the context of Industry 4.0 and green manufacturing has become a focal point in contemporary industrial research. Efficient scheduling, essential for optimal resource utilization and cost-effectiveness, faces new challenges due to the digital revolution and the green industry paradigm. This study conducts a comprehensive analysis of existing literature on scheduling problems, incorporating the dual perspectives of Industry 4.0 and green production. The research delves into diverse problem formulations and solution approaches, ranging from mathematical programming techniques to heuristic methods and machine learning concepts. The review reveals a scarcity of studies addressing the complexities arising from the integration of Industry 4.0 technologies and ecological considerations in production scheduling. Bridging this gap is crucial, urging further research at the intersection of Industry 4.0 and sustainable manufacturing practices. The study emphasizes the significance of addressing these challenges, considering the complexities of real-world scheduling scenarios, where practical problems involve operations ranging from thousands to hundreds of thousands. By exploring advanced solutions, encompassing machine learning, constraint programming, and metaheuristic methods, the research underscores the need for continued exploration and development of robust strategies applicable to large-scale, practical settings. This interdisciplinary approach is pivotal in shaping the future of industrial scheduling, ensuring efficiency, sustainability, and resilience in the face of evolving technological landscapes and environmental imperatives.

关键词： Production Scheduling Problems Industry 4.0 Green Manufacturing constraint programming Metaheuristic Methods

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Deterministic Partitioning Strategy to Parallelize the constraint programming Search Space 14

Deterministic Partitioning Strategy to Parallelize the Const...

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Simulation Multi-Conference

作者： Tarek Menouer Bertrand Le Cun University of Versailles Saint-Quentin-en-Yvelines France

ISBN: (纸本)9781632662163

This paper presents a deterministic parallelization to explore a constraint programming search space. This work is an answer to an industrial project named PAJERO, which is in need of a parallel constraint solver which always responds with the same solution whether using sequential or parallel machines. It is well known that parallel tree search changes the order in which the exploration of solution space is done. In the context where the first solution found is returned, using a different number of cores may change the returned solution. In the literature, several non deterministic strategies have been proposed to parallelize the exploration of constraint programming search space. Most of them are based on the Work Stealing technique used to partition the constraint programming search space on demand and during the execution of the search algorithm. Our study focuses on the determinism of the parallel search versus the sequential one. We consider that the sequential search algorithm is deterministic, then propose an elegant and simple solution introducing a total order on the nodes in which the parallel algorithm always gives the same solution as the sequential one regardless of the number of cores used. To evaluate this deterministic strategy, we ran tests using the Google OR-Tools constraint programming solver on top of our parallel Bobpp framework. The performances are illustrated by solving constraint programming problems modeled in FlatZinc format.

关键词： Parallelism constraint programming Work Stealing Determinism

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Optimizing Service Replication and Placement for IoT Applications in Fog Computing Systems 30th

Optimizing Service Replication and Placement for IoT Applica...

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30th European Conference on Parallel and Distributed Processing (Euro-Par)

作者： Ait-Salaht, Farah Rebai, Maher Izri, Nora Leonard de Vinci Pole Univ Res Ctr F-92916 Courbevoie France

ISBN: (纸本)9783031695766;9783031695773

Fog Computing extends Cloud Computing to the network edge, enhancing distributed computing to meet the growing needs of Internet of Things (IoT) applications requiring real-time or near-real-time analysis. This research focuses on efficiently managing the vast amounts of data generated by IoT devices and the continuous data streams they produce, employing an advanced replication and placement strategy for application components across distributed Fog Computing nodes. This approach enables scalable and parallel data processing to adapt to demand fluctuations, prevent over-provisioning, and maintain low response times, making it particularly effective for the dynamic nature of data stream processing in IoT applications. In this paper, we propose an Optimal IoT Service Replication and Placement (SRP) model, formulated as a constraint satisfaction problem, that considers the diverse requirements of IoT applications and the available infrastructure resources. Our model is designed to be adaptive and extensible, addressing the challenge of workload variability through real-time optimization. Numerical evaluations confirm the superior performance and scalability of our model over existing methods, while maintaining quality of service constraints. This highlights the potential of our approach to improve efficiency and resource management in Fog Computing environments.

关键词： Fog Computing Service replication Service placement IoT applications constraint programming

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Air-rail timetable synchronisation: Improving passenger connections in Europe within and across transportation modes

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2024年 115卷

作者： Buire, Clara Marzuoli, Aude Delahaye, Daniel Mongeau, Marcel Univ Toulouse ENAC Toulouse France Georgia Inst Technol North Ave NW Atlanta GA USA

This study addresses the integration of the railway and airline scheduling problems, in order to offer passengers smooth transfers between rail and air. This paper focuses on optimising the air and rail timetables at 18 major European airports including three hubs and their associated train stations. A multimodal passenger demand simulation, using constraint programming and based on real data, is proposed. A typical week, from Monday to Saturday, of December 2019 is analysed. Ten passenger demand simulations are run for each day, resulting in 60 test instances that are publicly released. The air-rail timetable synchronisation is applied to these 60 instances. Three scenarios are tested in which each operator agrees to change its schedule or not. Results show that changing the schedule of only 13% of European flights by 11 min, and half of trains scheduled to stop at the three hubs of 17 min, on average, could increase the number of suitable connections for passengers by 60%. In addition, if both airlines and railway operators adapt their schedules, passenger comfort is improved and operator costs are reduced, even more so than with unilateral changes.

关键词： Air-rail integrated timetable Optimisation Passengers Multimodal demand Mixed-Integer Linear programming constraint programming

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Hierarchical Optimization for Operationally-Constrained Resource Planning 2

Hierarchical Optimization for Operationally-Constrained Reso...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Shengkai, Chen Yu, Wang Savitha, Ramasamy Yin, Cheryl Wong Sze ASTAR Inst Infocomm Res I2R Singapore Singapore

ISBN: (纸本)9798350354102;9798350354096

Effective resource planning presents a broad problem across industries due to inner operational constraints. Existing methods are hard for generalization because they entail either unique models or customized specific solutions. To address this challenge, we pose the Operationally-Constrained Resource Planning Problem (OCRPP), which abstract assets and resources using job concepts to decouple the entangled constraints to describe the resource planning problem in a standard way. Meanwhile, we propose the Hierarchical Allocation Optimizer (HAO), a meta-heuristic solving framework containing 3 phases, to speed up feasible solution search with lower bound estimation. Experiments show HAO's superiority in terms of time and objectives compare to the alternatives, which adopts rule-based heuristic or general meta-heuristic. HAO's rapid response and flexibility are demonstrated to show its capability to adapt to various business scenarios and applications such as airlines, logistics companies, and so on.

关键词： Airline Assets Optimization constraint programming Hierarchical Optimizer Meta-Heuristic

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Two-stage and one-group two-dimensional guillotine cutting problems with defects: a CP-based algorithm and ILP formulations

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第6期60卷 1854-1873页

作者： Martin, Mateus Morabito, Reinaldo Munari, Pedro Univ Fed Sao Paulo Inst Sci & Technol Sao Paulo Brazil Univ Fed Sao Carlos Dept Prod Engn Via Washington Luiz Km 235 BR-13565905 Sao Carlos SP Brazil

We address two variants of the two-dimensional guillotine cutting problem that appear in different manufacturing settings that cut defective objects. Real-world applications include the production of flat glass in the glass industry and the cutting of wooden boards with knotholes in the furniture industry. These variants assume that there are several defects in the object, but the items cut should be defective-free;the cutting pattern is limited to two guillotine stages;and the maximum number of copies per item type in the pattern can be limited. The first variant deals with exact 2-stage patterns, while the second with exact 1-group patterns. To effectively solve these problems, we propose a constraint programming (CP) based algorithm as well as different Integer Linear programming (ILP) formulations. The first presented formulations are extensions of the modelling approach of [Martin, M., E. G. Birgin, R. D. Lobato, R. Morabito, and P. Munari. 2020. "Models for the Two-Dimensional Rectangular Single Large Placement Problem with Guillotine Cuts and Constrained Pattern." International Transactions in Operational Research 27: 767-793. doi:] for the case with defects, while the others are novel and more elaborate formulations based on the relative position of the items. We evaluate these three approaches with computational experiments using a set of benchmark instances from the literature. The results show that the approaches find optimal and near-optimal solutions in short processing times for several types of problem instances.

关键词： Cutting and packing guillotine cuts defects 2-stage and 1-group patterns constraint programming iInteger linear programmingt

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