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Maximizing diversity in k-pattern set mining through constraint programming and entropy

APPLIED INTELLIGENCE 2025年第7期55卷 1-24页

作者： Douad, Mohamed El Amine Aribi, Noureddine Loudni, Samir Hien, Arnold Lebbah, Yahia Univ Oran1 Ahmed Ben Bella LITIO Lab Oran 31000 Algeria IMT Atlantique LS2N UMR CNRS 6004 F-44307 Nantes France

Extracting diverse and frequent closed itemsets from large datasets is a core challenge in pattern mining, with significant implications across domains such as fraud detection, recommendation systems, and machine learning. Existing approaches often lack flexibility and efficiency, and struggle with initial itemset selection bias and redundancy. This paper addresses these research gaps by introducing a compact and modular constraint programming model that formalizes the search for diverse patterns. Our approach incorporates a novel global constraint derived from a relaxed Overlap diversity measure, using tighter lower and upper bounds to improve filtering capabilities. Unlike traditional methods, we leverage an entropy-based optimization framework that combines joint entropy maximization with top-k pattern mining to identify the maximally k-diverse pattern set. Our approach ensures more comprehensive and informative pattern discovery by minimizing redundancy and promoting pattern diversity. Extensive experiments validate the effectiveness of the proposed method, demonstrating significant performance gains and superior pattern quality compared to state-of-the-art approaches. Implemented in both sequential and parallel versions, the framework offers an efficient and adaptable solution for anytime pattern mining tasks in various domains.

关键词： constraint programming Overlap coefficient Diversity k-pattern set mining Frequent itemsets Relaxation Entropy-based optimization Top-k

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The generalized energy-aware flexible job shop scheduling model: A constraint programming approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Terbrack, Hajo Claus, Thorsten Tech Univ Dresden Int Inst IHI Zittau Markt 23 D-02763 Zittau Germany

Taking up the ongoing shift towards green production, this article addresses energy-oriented flexible job shop scheduling. Existing approaches mainly focus on single objectives in terms of energy utilization such as minimizing energy consumption. However, production control can affect multiple energy-related criteria. Therefore, we propose a flexible job shop scheduling model to minimize real-time pricing-related energy costs, peak demand and energy-related emissions. Motivated by the reported preeminence of constraint programming (CP) for a variety of scheduling problems, we extend a CP formulation for our study. To evaluate potential contradictory between energy objectives, we present nine objective functions by means of different lexicographic orders. In addition, we enhance the proposed scheduling model to account for sequence-dependent setup and due dates. To analyze and compare the effectiveness of the different model formulations, we present computational experiments for 20 small-, medium-and large-sized problem instances. Our study indicates that productivity can be maximized while, on average, energy costs are reduced by 5.3%, peak demand by 11.8%, emissions by 8.3% compared to traditional job scheduling. However, partly conflicting objectives require the decision maker to select the objective function most suitable to the individual needs. Including setup effort and due date compliance into energy-aware scheduling is possible and needed to make the concept of energy-aware scheduling applicable to industrial practice. We show that the additional aspects limit the potential improvement. Hence, it is crucial to understand such complex scheduling systems combining energy awareness, setup and due date compliance.

关键词： Energy Flexible job shop scheduling constraint programming Real-time pricing Peak reduction Emissions

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A constraint programming model and a hybrid iterated local search algorithm for solving an aircraft recovery problem in the oil and gas industry

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年

作者： Martin, Mateus Alvarez, Aldair de la Vega, Jonathan Morabito, Reinaldo Univ Fed Sao Carlos Sorocaba Brazil Univ Fed Sao Carlos Sao Carlos Brazil

In this paper, we address a challenging problem faced by a Brazilian oil and gas company regarding the rescheduling of helicopter flights from an onshore airport to maritime units, crucial for transporting company employees. The problem arises due to unforeseen events like bad weather or mechanical failures, leading to delays or postponements in the original flight schedules, disrupting the operation of maritime units, and employee shift scheduling. To model and solve the problem, we propose a constraint programming (CP) model aimed at optimizing daily flight scheduling with minimal delay and helicopter usage, considering various constraints like rescheduling priorities and time windows. We also develop a hybrid iterated local search algorithm to handle larger instances of the problem for the case when a general-purpose CP solver may not be available. Our approaches, evaluated using real-world data, demonstrate their effectiveness in solving short-term flight rescheduling problems in the context of the oil and gas industry, in comparison to exact and heuristic approaches from the literature.

关键词： Short-term flight rescheduling aircraft recovery problem helicopters and flights re-assignments constraint programming iterated local search

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Hybridizing constraint programming and meta-heuristics for multi-mode resource-constrained multiple projects scheduling Problem

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JOURNAL OF HEURISTICS 2025年第1期31卷 35-37页

作者： Ahmeti, Arben Musliu, Nysret TU Wien Inst Log & Computat Christian Doppler Lab Artificial Intelligence & Op DBAI Vienna Austria

The Multi-Mode Resource-Constrained Multiple Projects Scheduling Problem (MMRCMPSP) is an important combinatorial optimization problem for both real-world situations in industry and academic research. Its objective is to find the best schedule for activities across multiple projects that can be executed in different modes. The schedule must consider shared resource availability and satisfy precedence and time constraints. To tackle this problem, we propose a hybrid approach that combines constraint programming (CP) with meta-heuristic algorithms. We introduce and assess a CP model that incorporates all MMRCMPSP constraints. By leveraging the strengths of CP and meta-heuristics, our approach yields new upper bounds for various MMRCMPSP benchmark instances. Additionally, we evaluate our method using existing benchmark instances for single-project scheduling problems with multiple modes and provide improved solutions for many of them.

关键词： Project scheduling Meta-heuristic algorithms constraint programming Hybrid approach

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A particle swarm optimization and constraint programming-based approach for integrated process planning and scheduling with lot streaming problem

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Zhang, Mengya Li, Xinyu Gao, Liang Liu, Qihao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper studies the integrated process planning and scheduling with lot streaming (IPPS-LS) problem, which consists of lot splitting, process planning, and shop scheduling. Although the IPPS-LS problem is common in the manufacturing of flexible process products, it has not been extensively studied due to its high complexity. Hence, this study develops an enhanced particle swarm optimization algorithm based on constraint programming (CP) to minimize makespan. The proposed algorithm employs finite condition and relaxation models for particle reconfiguration and re-optimization. To achieve it, two types of relaxation models are constructed by decomposing the multiple constraints of the CP model. The algorithm dynamically updates particle encoding sequences based on model accuracy, effectively reducing invalid searches and accelerating the search process. The proposed algorithm is compared with models and other metaheuristic algorithms on 120 test instances. The impact of the relaxed CP strategy and particle swarm optimization algorithm on the proposed algorithm performance is also analyzed. Finally, a significance of difference validation is performed. Computational experiments demonstrate the efficiency of the proposed algorithm in solving the IPPS-LS problem of varying scales. In addition, the relaxed CP strategy exhibits a more significant improvement effect for medium-scale problems compared to small and large-scale problems.

关键词： Integrated process planning and scheduling Lot streaming constraint programming Particle swarm optimization algorithm Relaxation model

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Balancing and scheduling human-robot collaborative assembly lines with heterogeneous robots and limited resources: constraint programming approach and fruit fly optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Zheng, Chenyu Li, Zixiang Janardhanan, Mukund Zhang, Zikai Zhang, Liping Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Univ Warwick Int Digital Lab Coventry England

Collaborative robots are increasingly utilized to assist the human workers to assemble tasks or complete the assembly tasks solely in assembly lines. This study considers the human-robot collaborative assembly lines with heterogeneous collaborative robots and limited resources to optimize cycle time where human workers and collaborative robots can operate the different tasks in parallel. A constraint programming model is formulated that was able to achieve the optimal solutions for small-sized instances. An improved fruit fly optimization algorithm is developed to tackle the large-sized instances. The proposed algorithm proposes two vectors for encoding, where task assignment vector tackles task allocation sub-problem and process alternative vector tackles process alternative allocation sub-problem. This algorithm utilizes a decoding procedure with a constraint programming approach to achieve an optimal scheduling scheme of the station with human worker and collaborative robot. The lower bound and upper bound of completion time of single station and the earliest and latest processing time of tasks are added to the constraint programming model to speed up the search process. Meanwhile, improved fruit fly optimization algorithm utilizes the improved olfactory phase, improved visual phase and restart phase to accelerate the evolution of the whole swarm and avoid being trapped in local optimum. Computational study demonstrates that constraint programming approach outperforms the current mixed integer programming approach in objective value and solution time. The decoding procedure with constraint programming outperforms the current decoding procedure with mixed integer programming. Comparative study demonstrates that the proposed method outperforms the original fruit fly optimization algorithm and achieves promising performance in comparison with other methods. Finally, the proposed method is applied on scheduling of a gear box assembly line.

关键词： Assembly line balancing Human-robot collaboration constraint programming Fruit fly optimization algorithm

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Advanced constraint programming formulations for additive manufacturing machine scheduling problems

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年第3期76卷 590-605页

作者： Cakici, Eray Kucukkoc, Ibrahim Akdemir, Mustafa IBM Deutschland GmbH Ehningen Germany Balikesir Univ Balikesir Turkiye

In comparison with traditional subtractive manufacturing techniques, additive manufacturing (AM) enables fabricating complex parts through a layer-by-layer process. AM makes it possible to produce one-piece and lightweight functional products, which are traditionally made from several parts. This paper introduces constraint programming (CP) models to minimise makespan in single, parallel identical and parallel unrelated AM machine scheduling environments for selective laser melting. Alternative CP formulations are explored to improve efficiency. The proposed CP model significantly benefits from the introduction of interval variables to replace binary assignment variables, and pre-definitions to narrow the search space, resulting in increased search performance. A computational study has been conducted to compare the performance of our proposed CP model with both a mixed-integer programming and a genetic algorithm from existing literature, evaluating improvements made to its search capability. Computational results indicate that the proposed CP model can obtain high-quality solutions in a timely manner even for several large-size instances.

关键词： Scheduling constraint programming production

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The dual-resource-constrained re-entrant flexible flow shop a constraint programming approach and a hybrid genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第5期63卷 1803-1824页

作者： Mlekusch, Johanna Hartl, Richard F. Univ Vienna Dept Business Decis & Analyt A-1010 Vienna Austria

The dual-resource-constrained re-entrant flexible flow shop scheduling problem represents a specialised variant of the flow shop scheduling problem, inspired by real-world scenarios in screen printing industries. Besides the well-known flow shop structure, stages consist of identical parallel machines and operations may re-enter the same stage multiple times before completion. Moreover, each machine must be operated by a skilled worker, making it a dual-resource-constrained problem according to the existing literature. The objective is to minimise the total length of the production schedule. To address this problem, our study employs two methods: a constraint programming model and a hybrid genetic algorithm with a single-level solution representation and an efficient decoding heuristic. To evaluate the performance of our methods, we conducted a computational study using different problem instances. Our findings demonstrate that the proposed hybrid genetic algorithm consistently delivers high-quality solutions, particularly for large instances, while also maintaining a short computational time. Additionally, our methods improve existing benchmark results for instances from the literature for a subclass of the problem. Furthermore, we provide managerial insights into how dual-resource constraints affect the solution quality and the efficiency associated with different workforce configurations in the described production setting.

关键词： Production flexible re-entrant flow shop scheduling dual-resource hybrid genetic algorithm constraint programming

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constraint programming-based layered method for integrated process planning and scheduling in extensive flexible manufacturing

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Zhang, Mengya Li, Xinyu Gao, Liang Liu, Qihao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

Current extensive flexible manufacturing systems are characterized by high flexibility and large problem sizes, which present significant challenges to manufacturing efficiency. The integrated process planning and scheduling (IPPS) is a significant issue in this context. Due to the complexity of the integration problem, various approximation algorithms have been developed to tackle it, though this often demands considerable designer expertise and parameter tuning. This paper proposes a constraint programming (CP)-based method that can solve the large-scale IPPS problem in extensive flexible manufacturing. Firstly, this paper proposes a CP model which enriches the variable decision-making for flexible processes. Based on this, this paper presents a hybrid layered constraint programming (HLCP) method, which decomposes the complete CP model into multiple models of subproblems and solves these models iteratively to reduce the solution difficulty. It contains multiple sets of model relaxation and repair stages. Experiments on benchmark instances confirm that the proposed method reaches all optimal solutions, and surpasses previous results on 9 instances. Next, the proposed methods are tested on 35 sets of large-scale instances with up to 8000 operations, and the results show that the minimum gap can be obtained compared to the existing methods. Moreover, the proposed HLCP method is able to reduce the gap by an average of 9.03% within a reasonable time compared to the single-model approach.

关键词： Integrated process planning and scheduling constraint programming Hybrid layered method Large-scale problem Extensive flexible manufacturing

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A constraint programming Approach for the Preference Tourist Trip Design Problem 7th

A Constraint Programming Approach for the Preference Tourist...

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7th International Conference on Optimization and Learning

作者： Riff, Stephanie Castro, Carlos Monfroy, Eric Univ Tecn Feder Santa Maria Valparaiso Chile Univ Nantes Nantes France

ISBN: (纸本)9783031779404;9783031779411

Currently, tourists seek to optimize their time when planning a trip to another country to visit attractions and places that match their tastes and preferences. Among these preferences is slow or relaxed tourism, which demands visiting less popular places and having in mind conscious relaxed tourism. Linear programming has been used in some studies to solve optimization problems related to tourist routes, but its use is limited due to the complexity of the constraints in these problems. In contrast, constraint programming can handle complex constraints more naturally, allowing for better constraint modeling and more efficient problem solving. This paper addresses this problem by using constraint programming techniques for the optimization of tourist routes. constraint programming has been proven to be an effective technique for solving optimization problems related to tourist routes given its ability to model complex constraints and conflicts in solutions naturally. The results obtained in this article demonstrate that constraint programming using complete search techniques provides better results compared to linear programming. In particular, the proposed technique achieved the optimal solution for 70% of the tested instances, surpassing the results obtained by state-of-the-art studies and highlighting its efficiency in execution time. In summary, it is concluded that constraint programming is a more effective and efficient technique than linear programming in optimizing tourist routes in view of its ability to naturally model complex constraints and conflicts in solutions.

关键词： Tourism constraint programming Route planning

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