Article
Computer Science, Artificial Intelligence
Zhongshi Shao, Weishi Shao, Dechang Pi
Summary: In this paper, a distributed mixed permutation blocking flow-shop scheduling problem (DMBPFSP) is investigated, and an improved NEH heuristic (NEH_P) and efficient iterated greedy (EIG) algorithm are proposed to address the problem effectively. The computational results show that both NEH_P and EIG are very efficient for solving the considered problem.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Feige Liu, Guiling Li, Chao Lu, Lvjiang Yin, Jiajun Zhou
Summary: This article studies a distributed hybrid flow shop scheduling problem with blocking constraints and proposes an algorithm based on its characteristics. By designing an active decoding strategy, a framework of multiple iterative solutions, a heuristic rule based on blocking constraint, and an insertion-based search strategy, the goal of optimizing scheduling is achieved.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Mathematics
Yong Wang, Yuting Wang, Yuyan Han
Summary: This paper studies the hybrid flow shop scheduling problem with blocking constraints (BHFSP). It constructs a mixed integer linear programming (MILP) model and uses the Gurobi solver to demonstrate its correctness. A hybrid decoding strategy is proposed to select the minimal objective function value by combining both forward decoding and backward decoding. The VIG algorithm, which includes a new reconstruction mechanism based on the hybrid decoding strategy and a swap-based local reinforcement strategy, is used to solve the MILP model.
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
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
Computer Science, Artificial Intelligence
Haizhu Bao, Quanke Pan, Ruben Ruiz, Liang Gao
Summary: This paper investigates the energy-aware scheduling problem in a distributed blocking flow-shop with sequence-dependent setup times. It proposes a cooperative iterated greedy algorithm based on Q-learning (CIG) to minimize makespan and total energy consumption. Experimental results show that CIG outperforms other competitors in terms of improvement percentages.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Hao-Xiang Qin, Yu-Yan Han, Biao Zhang, Lei-Lei Meng, Yi-Ping Liu, Quan-Ke Pan, Dun-Wei Gong
Summary: With the development of national economies, attention has been drawn to the issues of energy consumption and pollution emissions in manufacturing. Most existing research has focused on reducing economic costs and energy consumption, with limited studies on the energy-efficient hybrid flow shop scheduling problem, especially with blocking constraints. This paper presents a mathematical model for the blocking hybrid flow shop problem with an energy-efficient criterion and proposes a modified Iterative Greedy algorithm to optimize the model.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Haoxiang Qin, Yuyan Han, Yuting Wang, Yiping Liu, Junqing Li, Quanke Pan
Summary: This paper introduces a new flow shop combinatorial optimization problem, called the blocking hybrid flow shop group scheduling problem (BHFGSP). The proposed algorithm, a novel iterated greedy algorithm, is effective in solving the BHFGSP. Experimental results demonstrate the algorithm's performance.
KNOWLEDGE-BASED SYSTEMS
(2022)
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
Operations Research & Management Science
Ahmed Missaoui, Younes Boujelbene
Summary: This study addresses the hybrid flow shop scheduling problem and introduces a new metaheuristic approach based on the iterated greedy method. Experimental results demonstrate the effectiveness and high solution quality of the proposed algorithm.
RAIRO-OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Wen-Qiang Zou, Quan-Ke Pan, M. Fatih Tasgetiren
Summary: This paper addresses the multi-compartment automatic guided vehicle scheduling problem in a matrix manufacturing workshop, proposing a mixed-integer linear programming model and a novel iterated greedy algorithm. Comparative experiments demonstrate that the proposed algorithm outperforms existing algorithms in solving the problem effectively.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Fuqing Zhao, Zesong Xu, Ling Wang, Ningning Zhu, Tianpeng Xu, J. Jonrinaldi
Summary: This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP) and proposes a population-based iterated greedy algorithm (PBIGA) to address the problem. The PBIGA is shown to be effective and outperforms state-of-the-art algorithms in terms of minimizing total flowtime. Experimental results on large-scale benchmark instances demonstrate the superiority of the proposed PBIGA.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Victor Fernandez-Viagas
Summary: In recent decades, numerous approximate algorithms have been proposed for flow-shop-based scheduling problems, with speed-up procedures playing a crucial role in algorithm efficiency. This paper introduces a speed-up procedure for makespan minimisation and embeds it in a traditional iterated greedy algorithm to outperform the best metaheuristic for the problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Iyad Abu Doush, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Sharif Naser Makhadmeh, Mohammed El-Abd
Summary: This paper proposes an island neighboring heuristics harmony search algorithm (INHS) to solve blocking flow-shop scheduling problem. The algorithm enhances its performance by diversifying the population using the island model and improving solution quality using neighboring heuristics. Experimental results demonstrate the efficiency and competitiveness of the proposed algorithm in solving instances from different datasets.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Fehmi Burcin Ozsoydan, Mujgan Sagir
Summary: The paper presents a learning iterated greedy search metaheuristic algorithm to minimize the maximum completion time in a hybrid flexible flowshop problem. Through four main phases, the algorithm adaptively learns and promotes efficient low-level heuristics, leading to significant improvements demonstrated by statistical tests compared to eight other algorithms in related literature.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Zhongshi Shao, Weishi Shao, Dechang Pi
Summary: In this paper, a distributed mixed permutation blocking flow-shop scheduling problem (DMBPFSP) is investigated, and an improved NEH heuristic (NEH_P) and efficient iterated greedy (EIG) algorithm are proposed to address the problem effectively. The computational results show that both NEH_P and EIG are very efficient for solving the considered problem.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper investigates the distributed flow shop scheduling problem in heterogeneous multi-factories and proposes a mixed-integer linear programming model and a multi-local search algorithm to solve it. The effectiveness and efficiency of the proposed methods are demonstrated through experiments.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper studies a distributed heterogeneous hybrid flow shop lot-streaming scheduling problem (DHHFLSP) with the minimization of makespan. The mixed-integer linear programming model (MILP) of DHHFLSP is established, and eighteen constructive heuristics and an iterated local search algorithm (ILS) are designed to solve the problem. The comparisons with several related algorithms on extensive testing instances demonstrate the effectiveness and efficiency of the ILS algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This article studies a distributed heterogeneous hybrid flow shop scheduling problem and proposes an ACO_MOEA/D algorithm for solving the problem, which considers the optimization objectives from the view of production and management and is validated through various experiments for its efficiency and effectiveness.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper proposes a multi-objective memetic algorithm with a two-level encoding scheme to solve the energy-efficient distributed flexible flow shop scheduling problem. Through comprehensive experiments, the effectiveness of the algorithm in optimizing total weighted tardiness and energy consumption is verified.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Shile Qi, Jing Sui, Godfrey Pearlson, Juan Bustillo, Nora Perrone-Bizzozero, Peter Kochunov, Jessica A. Turner, Zening Fu, Wei Shao, Rongtao Jiang, Xiao Yang, Jingyu Liu, Yuhui Du, Jiayu Chen, Daoqiang Zhang, Vince D. Calhoun
Summary: Schizophrenia is a highly heritable psychiatric disorder characterized by widespread brain abnormalities. This study identifies a multimodal frontotemporal network associated with schizophrenia polygenic risk, serving as a specific brain biomarker for schizophrenia.
NATURE COMMUNICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Qi Zhu, Heyang Wang, Bingliang Xu, Zhiqiang Zhang, Wei Shao, Daoqiang Zhang
Summary: Multi-modal imaging data fusion is important in medical data analysis for more accurate analysis with complementary information. This paper proposes a novel method for epilepsy diagnosis by fusing data from functional MRI and diffusion tensor imaging, capturing complementary information and discriminative features. Experimental results show that the proposed method is significantly superior to other approaches.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Engineering, Biomedical
Guidan Fu, Yueying Zhou, Peiliang Gong, Pengpai Wang, Wei Shao, Daoqiang Zhang
Summary: Sleep staging is crucial for evaluating sleep quality and diagnosing sleep-related diseases. Most existing automatic sleep staging methods focus on time-domain information and overlook the transformation relationship between sleep stages. To address these issues, we propose a deep neural network model called TSA-Net, which incorporates temporal-spectral fusion and attention mechanism for automatic sleep staging. Our evaluation on two public datasets shows that TSA-Net can optimize the performance of sleep staging and outperforms state-of-the-art methods.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper investigates the distributed heterogeneous hybrid flow shop scheduling problem and proposes a solution based on a network memetic algorithm. The algorithm includes a probability network model and a learning-based local search, guiding the search using multiple objective weight vectors and effectively solving the production scheduling problem.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zhongshi Shao, Weishi Shao, Dechang Pi
Summary: As economic globalization advances, distributed manufacturing has become common in modern industries. This paper proposes a method to solve the problem of distributed heterogeneous hybrid blocking flow-shop scheduling and demonstrates its effectiveness through experiments.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Engineering, Biomedical
Peng Wan, Haiyan Xue, Chunrui Liu, Fang Chen, Wei Shao, Jing Qin, Wentao Kong, Daoqiang Zhang
Summary: This article introduces a novel transport-based anatomical-functional metric learning (T-AFML) method for quantifying the similarity of ultrasound images for liver cancer diagnosis. By using a temporally regularized optimal transport to align local enhancement patterns and adopting a selector-based metric integration mechanism, the method achieves superior diagnostic accuracy and sensitivity in quantifying multi-modal ultrasonic findings similarity for primary liver cancer diagnosis.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Review
Automation & Control Systems
Mei-Ling Wang, Wei Shao, Xiao-Ke Hao, Dao-Qiang Zhang
Summary: Brain imaging genomics is an interdisciplinary field that aims to evaluate and characterize genetic variants that influence phenotypic measures derived from brain imaging data. This technique can reveal the complex mechanisms underlying cognition and psychiatric disorders. Machine learning is a powerful tool for data-driven association studies, utilizing intercorrelated structure information to identify the association between risk genes and brain structure or function. This paper provides a comprehensive review of the background, methods, and future prospects in imaging genomics.
MACHINE INTELLIGENCE RESEARCH
(2023)
Article
Biochemical Research Methods
Xiaoyu Guan, Zhongnian Li, Yueying Zhou, Wei Shao, Daoqiang Zhang
Summary: Nanopore sequencing, as a high-throughput sequencing technology for DNA, RNA, and proteins, faces the challenge of high labeling costs for the enormous generated data. This study introduces active learning to select samples that need to be labeled, reducing the labeling costs significantly. Experimental results demonstrate that active learning can greatly reduce the labeling amount while achieving the best baseline performance for nanopore data analysis.
Proceedings Paper
Computer Science, Information Systems
Zongxiang Pei, Daoqiang Zhang, Wei Shao
Summary: This study proposes an efficient Metric learning with Graph Transformer method to help extract distinguished tumor features and apply them to CRC staging. The results show the method's superiority in CRC classification performance.
2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zongxiang Pei, Yingli Zuo, Liang Sun, Meiling Wang, Daoqiang Zhang, Wei Shao
Summary: In this paper, a Block-based Multi-View Graph Convolutional Network (BMVGCN) is proposed for lung cancer diagnosis, which integrates multiple types of image features to improve diagnostic performance. Experimental results demonstrate that our method outperforms other methods in lung cancer classification tasks.
ARTIFICIAL INTELLIGENCE, CICAI 2022, PT II
(2022)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
