Article
Computer Science, Artificial Intelligence
Yuting Wang, Yuyan Han, Quan-ke Pan, Huan Li, Yuhang Wang
Summary: In this study, 48 available MILP models and an efficient CP model are constructed by categorizing the constraints. The experimental results show that models 24 and 48 exhibit superior performance, highlighting the effectiveness of the hybrid modeling approach.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Industrial
Andy Ham
Summary: This study introduces a novel constraint programming method for simultaneous scheduling of production and material transfer, outperforming traditional benchmark methods. It also proposes a medium-scale benchmark instance for further research and testing.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Management
Janis Brammer, Bernhard Lutz, Dirk Neumann
Summary: This study presents a novel reinforcement learning approach for the permutation flow shop problem (PFSP) with multiple lines and demand plans. The approach generates job sequences iteratively and optimizes them using local search, outperforming existing methods on multi-line problems with short cutoff times.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Eyup Ensar Isik, Seyda Topaloglu Yildiz, Ozge Satir Akpunar
Summary: Proper scheduling of jobs is essential for modern production systems to work effectively. The study addresses the hybrid flow shop scheduling problem (HFSP) and develops constraint programming (CP) models for it, outperforming mixed-integer linear programming models in finding high-quality solutions.
Article
Computer Science, Interdisciplinary Applications
Carla Juvin, Laurent Houssin, Pierre Lopez
Summary: This paper focuses on exact methods for solving the preemptive flexible job-shop scheduling problem with makespan minimization objective function. Mathematical and constraint programming models can solve the problem for small instances. However, as an NP-hard problem, the solving cost increases rapidly for larger instances. In this regard, a logic-based Benders decomposition is proposed, relying on an efficient branch-and-bound procedure to solve the subproblem representing a pure (non-flexible) preemptive job-shop scheduling problem. Computational experiments demonstrate the very good performance of the proposed methods.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Wenwu Han, Qianwang Deng, Guiliang Gong, Like Zhang, Qiang Luo
Summary: This study focuses on a new realistic hybrid flow shop scheduling problem with worker constraint (HFSSPW) and proposes seven multi-objective evolutionary algorithms to solve the problem, incorporating the earliest due date (EDD) rule into the heuristic decoding methods. The computational results demonstrate the excellent performance of the proposed algorithms in terms of makespan objective.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Manufacturing
Gregory A. Kasapidis, Dimitris C. Paraskevopoulos, Panagiotis P. Repoussis, Christos D. Tarantilis
Summary: This paper investigates flexible job shop scheduling problems with arbitrary precedence graphs, proposing rigorous mixed integer and constraint programming models as well as an evolutionary algorithm. Through the creation of a new heuristic solution framework and theorems, it addresses the challenges of considering makespan and precedence graph flexibility in scheduling.
PRODUCTION AND OPERATIONS MANAGEMENT
(2021)
Article
Chemistry, Multidisciplinary
Christos Gogos
Summary: This paper investigates the permutation flow-shop scheduling problem and its distributed version, proposing constraint programming models and a novel heuristic to solve them. Experimental results demonstrate the effectiveness of the approach and highlight the significance of the number of jobs in problem complexity.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Yiping Huang, Libao Deng, Jianlei Wang, Weiwei Qiu, Jinfeng Liu
Summary: This study considers uncertainty factors and applies two-stage stochastic programming to model and solve a hybrid flow-shop scheduling problem. A two-stage scenario tree is used to describe the uncertain factors, and a mixed-integer linear programming model is formulated using stochastic programming theory. A novel variant algorithm (H-PDDE) is proposed, which effectively solves the problem by adopting a permutation scheduling decoding method and introducing three new improvement strategies. Computational experiments validate the proposed model and algorithm, showing that the H-PDDE outperforms existing algorithms and conventional PDDE variants in solving the problem more effectively. The stochastic programming model is increasingly superior to the deterministic model in an uncertain environment. The source code and data files of the H-PDDE algorithm can be found at https://github.com/huangyiping-ai/H-PDDE-algorithm.git.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Francisco Yuraszeck, Gonzalo Mejia, Jordi Pereira, Mariona Vila
Summary: This study addresses a specific case of the group shop scheduling problem and proposes a novel heuristic procedure to improve the solution. Experimental results demonstrate that the proposed algorithm performs significantly better on large-size instances.
Article
Engineering, Industrial
Levi R. Abreu, Marcelo S. Nagano, Bruno A. Prata
Summary: This paper examines a variant of the open shop scheduling problem where intermediate storage is not allowed between two adjacent production stages. The authors propose a new exact method using a two-stage constraint programming approach. Computational experiments demonstrate the effectiveness of the proposed method in solving large-sized instances.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Jing-Jing Wang, Ling Wang
Summary: In this article, the distributed hybrid flow-shop scheduling problem is addressed with an optimization framework comprising a mixed integer linear programming model and a bi-population cooperative memetic algorithm (BCMA). Collaborative initialization and intensification search are used to generate diverse solutions and balance exploration and exploitation. Extensive computational tests show the effectiveness of the BCMA in solving the DHFSP and verifying the optimization capabilities of the specific designs.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2021)
Article
Multidisciplinary Sciences
Lingxuan Liu, Leyuan Shi
Summary: This paper addresses the two-stage hybrid flow shop scheduling problem with a batch processor in the first stage and a discrete processor in the second stage, considering incompatible job families and limited buffer size. The automatic design of efficient heuristics based on genetic programming method successfully generates scheduling rules to minimize total completion time. The proposed genetic programming with cooperative co-evolution approach outperforms both constructive heuristic and meta-heuristic algorithms in producing high-quality schedules within seconds.
Article
Computer Science, Interdisciplinary Applications
Vincent Boyer, Jobish Vallikavungal, Xavier Cantu Rodriguez, M. Angelica Salazar-Aguilar
Summary: This study introduces a generalized flexible job-shop scheduling problem with additional hard constraints, inspired by a real manufacturing situation. Mathematical models and a metaheuristic algorithm are proposed to address the problem, with experimental results showing the effectiveness of the algorithm in handling large instances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Review
Management
Janis S. Neufeld, Sven Schulz, Udo Buscher
Summary: This article presents the research progress on multi-objective hybrid flow shop scheduling problems, identifies important features in optimization algorithms, and provides a framework and test instances for evaluating algorithm suitability. The article is of great theoretical and practical significance for solving multi-objective optimization problems.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)