Article
Engineering, Industrial
Biao Han, Quan-Ke Pan, Liang Gao
Summary: This paper addresses a serial distributed permutation flowshop scheduling problem (SDPFSP) inspired by a printed circuit board assembly process. A cooperative iterated greedy (CIG) algorithm is developed to optimize the solution. Problem-specific operators and computational experiments are conducted to verify the effectiveness of the proposed algorithm and its superiority over existing methods.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Shuai Chen, Quan-Ke Pan, Liang Gao
Summary: Production scheduling is crucial in intelligent decision support systems and optimization technologies, especially in the era of globalization. This study focuses on the distributed blocking flowshop scheduling problem and proposes various heuristics and algorithms to minimize makespan.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Ying-Ying Huang, Quan-Ke Pan, Jiang-Ping Huang, P. N. Suganthan, Liang Gao
Summary: This paper proposes an improved iterative greedy algorithm based on groupthink for solving the distributed assembly permutation flowshop scheduling problem with total flowtime criterion, and experimental results show that the proposed algorithm significantly outperforms other algorithms in comparison.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yuan-Zhen Li, Quan-Ke Pan, Ruben Ruiz, Hong-Yan Sang
Summary: This paper studies the distributed assembly mixed no-idle permutation flowshop scheduling problem (DAMNIPFSP) with the objective of minimizing total tardiness. An improved Iterated Greedy algorithm named RIG (Referenced Iterated Greedy) is proposed, which includes two novel destruction methods, four new reconstruction methods, and six new local search methods based on the characteristics of DAMNIPFSP. Experimental results show that RIG algorithm is a state-of-the-art procedure for DAMNIPFSP with the total tardiness criterion.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics
Chenyao Zhang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao
Summary: A distributed blocking flowshop scheduling problem with no buffer and setup time constraints is studied. A mixed integer linear programming model is constructed and verified for correctness. An iterated greedy algorithm is presented to optimize the makespan criterion and collaborative interactions are considered to improve the exploration and exploitation of the algorithm.
Article
Automation & Control Systems
Hui Zhao, Quan-Ke Pan, Kai-Zhou Gao
Summary: This paper studies the distributed permutation flowshop group scheduling problem (DPFGSP) and proposes a cooperative population-based iterated greedy (CPIG) algorithm to minimize total flowtime (TF). The CPIG algorithm divides DPFGSP into two sub-problems and uses advanced technologies to address them. Experimental evaluation shows that the CPIG algorithm outperforms five state-of-the-art metaheuristics in the literature.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Chao Lu, Jun Zheng, Lvjiang Yin, Renyi Wang
Summary: This study tackles the distributed hybrid flowshop scheduling problem (DHFSP) with the objective of minimizing the makespan. To solve the DHFSP, a mixed-integer linear programming model is formulated and an improved iterated greedy (IIG) algorithm is proposed. The IIG algorithm incorporates a new initialization strategy, a hybrid operator, and a new local search method to enhance the quality of the initial solution, improve global search ability, and strengthen exploitation capability. Experimental results demonstrate the effectiveness of the IIG algorithm in addressing DHFSP.
ENGINEERING OPTIMIZATION
(2023)
Article
Engineering, Industrial
Qin Zhang, Yu Liu, Tangfan Xiahou, Hong-Zhong Huang
Summary: This article proposes a new maintenance scheduling framework for a fleet of military aircraft to maximize fleet readiness. The limited maintenance capacities and uncertainties associated with breaks are considered. Two heuristic algorithms are introduced to solve the optimization problem efficiently.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Yuan-Zhen Li, Quan-Ke Pan, Jun-Qing Li, Liang Gao, M. Fatih Tasgetiren
Summary: This research focuses on distributed permutation flow shop scheduling problem with mixed no-idle constraints, using a mixed-integer linear programming model and an Adaptive Iterated Greedy algorithm with restart strategy. The algorithm shows excellent performance in large-scale experiments.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Yuhang Wang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao, Yiping Liu
Summary: The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Three issues of DFGSP, including assigning groups to factories, arranging group sequences in each factory, and scheduling job sequences in each group, need to be solved due to its strong coupling. To solve these problems, a mixed-integer linear programming model is constructed and verified, and two rapid evaluation methods are designed based on group insertion and job insertion. An effective two-stage iterated greedy algorithm (tIGA) is proposed, which includes cooperative neighborhood search strategies and enhanced search strategies to improve the search breadth and depth. Experimental results show that the proposed algorithm outperforms other algorithms in terms of objective values and demonstrates the effectiveness of the proposed tIGA.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yuan-Zhen Li, Quan-Ke Pan, Xuan He, Hong-Yan Sang, Kai-Zhou Gao, Xue-Lei Jing
Summary: This paper investigates a new problem in the field of DPFSP, focusing on optimizing task scheduling by maximizing total payoff. The authors propose a mathematical model, explore the problem characteristics, and propose new heuristic algorithms to improve efficiency. Experimental results demonstrate the effectiveness of the presented algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Management
Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Bastien Pasdeloup, Patrick Meyer
Summary: This paper aims to integrate machine learning techniques into meta-heuristics for solving combinatorial optimization problems. It develops a novel efficient iterated greedy algorithm based on reinforcement learning, and evaluates its performance through experiments on the permutation flowshop scheduling problem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Zhi-Yuan Wang, Quan-Ke Pan, Liang Gao, Yu -Long Wang
Summary: This paper proposes a method to solve the distributed flowshop group scheduling problem with sequence-dependent setup time. By establishing a mathematical model and using a two-stage iterated greedy algorithm, this method can effectively address the problem. Experimental results show that the proposed method outperforms other algorithms in terms of the relative deviation index values.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zhi-Yuan Wang, Quan-Ke Pan, Liang Gao, Xue-Lei Jing, Qing Sun
Summary: This paper addresses a distributed flowshop group robust scheduling problem with uncertain processing times. The proposed cooperative iterated greedy (CIG) algorithm outperforms other competitors in terms of average relative deviation index. The CIG algorithm utilizes modified sequence rules, dummy scenario method, and specific operators to optimize the family and job scheduling sub-problems, and employs a cooperation mechanism to emphasize their coupling relationship.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Industrial
Xueyan Sun, Weiming Shen, Birgit Vogel-Heuser
Summary: This paper addresses the distributed hybrid blocking flowshop scheduling problem with makespan criterion and proposes a hybrid genetic algorithm for solving it. Experimental results show that the proposed algorithm performs well on benchmarks and the local search method has a strong search capability.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Engineering, Industrial
Guokai Liu, Weiming Shen, Liang Gao, Andrew Kusiak
Summary: Smart manufacturing system aims to automate modeling algorithms for industrial applications in dynamic environments. The prevalent deep transfer learning (DTL) has shown promising results in cross-domain fault diagnosis, but most DTL algorithms are dataset-specific and require hyperparameter optimization (HPO) with prior knowledge. To address this issue, an automated broad-transfer learning algorithm (AutoBTL) is proposed to improve predictive modeling for cross-domain tasks. AutoBTL includes a broad classifier, an active estimator, and a hyperparameter optimizer to solve the HPO problem in cross-domain fault diagnosis.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Editorial Material
Engineering, Multidisciplinary
Peigen Li, Xinyu Li, Liang Gao, Akhil Garg, Weiming Shen
Article
Computer Science, Information Systems
Thien Pham, Loi Truong, Hung Bui, Thang Tran, Akhil Garg, Liang Gao, Tho Quan
Summary: 5G is the fifth generation of cellular networks and has been widely used in various fields. To ensure stable power supply, a 5G system requires either a lithium-ion battery or AC main modernization. The lithium-ion battery approach is preferred due to its simplicity and maintainability. A smart power supply with temporal monitoring and estimation is highly desired for 5G systems.
Article
Computer Science, Artificial Intelligence
Jiajun Zhou, Liang Gao, Chao Lu
Summary: Industrial internet platform is an emerging infrastructure for increasing manufacturing efficiency through resource sharing. This study proposes a mechanism for jointly optimizing multiple manufacturing cloud service allocation problems using transfer learning. Two novel transfer learning strategies are integrated into a bee colony algorithm framework to enhance the solution quality and search speed. Experimental results demonstrate the superior performance of the proposed method compared to other state-of-the-art approaches.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Thermodynamics
Qixuan Zhong, Parthiv K. Chandra, Wei Li, Liang Gao, Akhil Garg, Song Lv, K. Tai
Summary: This article focuses on the problem of fluctuating cooling system flow caused by different working states during the operation of electric vehicles. The authors propose a two-dimensional topology optimization method for obtaining cooling plates with different topological structures. The results indicate that the optimized cooling plate structure under low flow conditions has better heat dissipation performance.
APPLIED THERMAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Jiang-Ping Huang, Liang Gao, Xin-Yu Li, Chun-Jiang Zhang
Summary: This paper studies the Distributed Job-shop Scheduling Problem (DJSP) with random job arrivals and explores a multi-agent method based on Deep Reinforcement Learning (DRL). The effectiveness of the proposed method is proven through independent utility tests and comparison tests, and its practical value in actual production is demonstrated through a case study.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Yiping Gao, Xinyu Li, Liang Gao
Summary: Intelligent defect recognition is crucial for quality control and decision-making in smart manufacturing systems. The current methods need improvement in terms of recognition performance and interpretability. Transformer (ViT) shows potential in intelligent defect recognition with its outstanding performance and interpretability in image recognition. However, the requirement for a large number of samples impedes the application of ViT, especially in small-sample cases. To address this issue, a multi-scale spatial feature fusion-based ViT is proposed, which achieves improved performance on small-sample defect recognition and provides explicable results for defect analysis.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2023)
Article
Thermodynamics
Sanjeet Patra, K. Parthiv Chandra, Wei Li, Jianhui Mou, Liang Gao, Quan Zhou, A. Garg
Summary: Topology optimization is used to optimize material distribution in a design domain for improved performance. This study compared three cooling plate designs and found that the topology optimized double-outlet design showed better performance in terms of temperature and pressure compared to the single-outlet design and conventional straight-channel design.
INTERNATIONAL JOURNAL OF GREEN ENERGY
(2023)
Article
Engineering, Industrial
Weixiong Jiang, Jun Wu, Haiping Zhu, Xinyu Li, Liang Gao
Summary: A novel health evaluation method is proposed based on paired ensemble and group knowledge measurement to accurately identify specific faults and evaluate the health condition of wind turbine gearboxes. The method utilizes paired ensemble for compound fault diagnosis, and a fuzzy derivation method called group knowledge measurement to estimate fault influence weights. The proposed method is shown to be competitive in terms of diagnostic accuracy and evaluation reliability compared to existing methods.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Engineering, Mechanical
Zihao Wu, Zhenzhong Chen, Ge Chen, Xiaoke Li, Chen Jiang, Xuehui Gan, Haobo Qiu, Liang Gao
Summary: A new probabilistic feasible region strategy, called PFR-vstd, is proposed in this study to improve the accuracy of the PFR method in solving RBDO problems with varying standard deviation. It establishes a new probabilistic feasible region in the original design space. The results of four applications demonstrate the high accuracy and sufficient efficiency of the PFR-vstd method for resolving RBDO problems with varying standard deviation.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2023)
Article
Multidisciplinary Sciences
Jinhao Zhang, Mi Xiao, Liang Gao, Andrea Alu, Fengwen Wang
Summary: The authors have designed and realized self-bridging metamaterials with Poisson's ratios exceeding the theoretical limits. This finding is of great significance for expanding the range of achievable Poisson's ratios in mechanical systems, with implications for medical stents and soft robots.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Civil
Dongdong Wang, Suying Pan, Jin Zhou, Quanke Pan, Zhonghua Miao, Jiangke Yang
Summary: This paper addresses the distributed event-triggered formation control problem of networked nonholonomic mobile robots (NNMRs) in a leader-follower-based framework. An event-triggered mechanism (ETM) is introduced for the design of the kinematic controller using an auxiliary reference vector and combined with backstepping technique and sliding mode approach to propose a unified integrated dynamic controller. The designed event-triggered condition is derived based on the local communication among robots utilizing the nonholonomic property of NNMRs, ensuring the exclusion of Zeno behavior before achieving the desired formation configuration. The theoretical results are validated through simulation analysis and implemented on a real-time physical NNMR experimental platform, demonstrating the key feature of the ETM integral formation scheme in effectively reducing communication resource usage and energy consumption while maintaining comparable performance to the conventional periodic communication mechanism (PCM).
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Hongjin Wu, Ruoshan Lei, Yibing Peng, Liang Gao
Summary: Machining feature recognition (MFR) is an important step in computer-aided process planning that infers manufacturing semantics from CAD models. Deep learning methods like AAGNet overcome the limitations of traditional rule-based methods by learning from data and preserving geometric and topological information with a novel representation. AAGNet outperforms other state-of-the-art methods in accuracy and complexity, showing potential as a flexible solution for MFR in CAPP.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Zhou, Shijie Rao, Liang Gao
Summary: This article introduces a novel bandit-mechanism-based ensemble method for determining the proper domain adaptation strategy online, and adjusting the intensity of cross-task knowledge transfer based on historical experiences. Experimental results demonstrate the superiority of this approach in multi-task problem solving.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Industrial
Jin Xie, Xinyu Li, Liang Gao, Lin Gui
Summary: This paper proposes a hybrid genetic tabu search algorithm for the distributed flexible job-shop scheduling problem, which outperforms other comparison algorithms in terms of solution quality and computation efficiency.
JOURNAL OF MANUFACTURING SYSTEMS
(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)
