Article
Mathematics
Mei Li, Gai-Ge Wang, Helong Yu
Summary: This paper studies the fuzzy hybrid green shop scheduling problem with fuzzy processing time, aiming to minimize makespan and total energy consumption. By proposing a discrete artificial bee colony algorithm, it achieves higher diversity and convergence speed.
Article
Automation & Control Systems
Jiang-Ping Huang, Quan-Ke Pan, Zhong-Hua Miao, Liang Gao
Summary: The study focuses on the DPFSP problem with SDST, proposing three constructive heuristics and a DABC algorithm. The heuristics are based on greedy rule and local search, while the DABC algorithm balances local and global exploration with six composite neighborhood operators. A problem-oriented local search method is introduced to improve the best individual in the population. The proposed methods are shown to be effective compared to existing algorithms in solving the problem.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yibing Li, Cheng Liao, Lei Wang, Yu Xiao, Yan Cao, Shunsheng Guo
Summary: This paper proposes a hybrid algorithm RL-ABC, which combines Reinforcement Learning and Artificial Bee Colony, to solve the difficult and unstable FJSP-LS problem. The algorithm divides the solution into two stages and uses initialization, local search strategies, and reinforcement learning to determine the best dispatch and sublot schemes. Experimental results show that RL-ABC algorithm outperforms other compared algorithms in terms of effectiveness and robustness.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Jing Wang, Hongtao Tang, Deming Lei
Summary: This study proposes a solution for the distributed assembly flow shop scheduling problem (DAFSP) with Pm-+ 1 layout, factory eligibility, transportation capacity, and setup time. The Q-learning artificial bee colony (Q-LABC) algorithm is introduced to minimize makespan and total tardiness. Experimental results demonstrate the effectiveness of the new strategies and show that Q-LABC outperforms comparative algorithms on at least 83.33% and 94.44% instances, respectively.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Xiangqi Liu, Jie Chen, Xiaoling Huang, Shanshan Guo, Shuai Zhang, Mengjiao Chen
Summary: With the emergence of the remanufacturing industry, scholars have shown considerable interest in scheduling problems related to remanufacturing systems. A new job shop scheduling method with job families (JSS-JF) has been proposed to handle the selection of appropriate execution modes for reprocessing components with different damaged conditions, aiming to minimize the total completion time and total cost of job families. The proposed method uses an extended artificial bee colony algorithm with a new three-dimensional encoding scheme and integrates crossover and mutation operators, local search, and elite replacement strategy to find a near-optimal solution.
Article
Computer Science, Artificial Intelligence
Hernan Diaz, Juan J. Palacios, Ines Gonzalez-Rodriguez, Camino R. Vela
Summary: In this paper, a new Artificial Bee Colony algorithm is proposed to solve a variant of the Job Shop Scheduling Problem with uncertain processing times. The algorithm incorporates a diversification strategy based on the seasonal behavior of bees to avoid premature convergence. A thorough parametric analysis and comparison of different seasonal models are conducted, showing the improved performance of the proposed algorithm. Additionally, an assessment of the solutions' robustness under different ranking operators and a sensitivity analysis on the effect of uncertainty levels are performed.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2023)
Article
Engineering, Chemical
Xiaojun Long, Jingtao Zhang, Kai Zhou, Tianguo Jin
Summary: This paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve the dynamic flexible job-shop scheduling problem. By arranging the processing sequence of jobs and the relationship between operations and machines, the algorithm improves the economic benefit of the job-shop and the utilization rate of processing machines. Experimental results demonstrate the effectiveness of the proposed algorithm.
Article
Automation & Control Systems
Hanxiao Li, Kaizhou Gao, Pei-Yong Duan, Jun-Qing Li, Le Zhang
Summary: This work proposes an improved artificial bee colony algorithm with Q-learning, named QABC, for solving the permutation flow-shop scheduling problem. Experimental results demonstrate the superiority of QABC over other algorithms in solving the concerned problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xujin Zhang, Hongyan Sang, Zhongkai Li, Biao Zhang, Leilei Meng
Summary: This paper proposes an efficient discrete artificial bee colony (DABC) algorithm to solve a new automatic guided vehicle (AGV) scheduling problem with delivery and pickup in a matrix manufacturing workshop. The goal is to minimize the total cost of AGV transportation solution. The proposed DABC algorithm has high performance in solving the delivery and pickup problem.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yue Xu, Xiuli Wang
Summary: This paper proposes a two-stage approach to solve the staff scheduling problem in call centers. The approach utilizes the artificial bee colony algorithm and integer programming to generate and optimize shift schedules. Experimental results demonstrate the effectiveness and efficiency of the proposed method in providing good solutions for large-scale problems. Additionally, guidance is provided on balancing employees' working preferences and labor costs with staff satisfaction.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Biotechnology & Applied Microbiology
Xiulin Li, Jiansha Lu, Chenxi Yang, Jiale Wang
Summary: This study examined the flexible assembly job-shop scheduling problem with lot streaming, by considering splitting batches into sub-batches of unequal and consistent sizes to allow efficient processing in the two-stage system.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yue Xu, Xiuli Wang
Summary: The paper proposes an enhanced artificial bee colony (EABC) algorithm to address the workforce scheduling problem in call centres, showing superior performance compared to hybrid artificial bee colony and simulated annealing. It achieves (sub-)optimal solutions for large-scale problems and examines the impact of weekend-off fairness on labor cost.
APPLIED SOFT COMPUTING
(2021)
Article
Multidisciplinary Sciences
Yandi Zuo, Zhun Fan, Tierui Zou, Pan Wang
Summary: In this study, the energy-efficient hybrid flow shop scheduling problem with a variable speed constraint is investigated, and a novel multi-population artificial bee colony algorithm is developed. The algorithm aims to minimize makespan, total tardiness, and total energy consumption simultaneously. The results show that the algorithm can achieve outstanding performance on three metrics for the considered problem.
Article
Computer Science, Artificial Intelligence
Yandi Zuo, Pan Wang, Zhun Fan, Ming Li, Xinhua Guo, Shijie Gao
Summary: In this study, an efficient artificial bee colony algorithm is proposed for solving the distributed assembly flow shop scheduling problem. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the majority of instances.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Yandi Zuo, Pan Wang, Ming Li
Summary: This study investigates an assembly hybrid flow shop scheduling problem considering energy consumption and proposes a population diversity-based artificial bee colony algorithm to minimize the makespan and total energy consumption. The algorithm outperforms other state-of-the-art algorithms in terms of IGD and c metrics on over 70% of the instances tested.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Chunjiang Zhang, A. K. Qin, Weiming Shen, Liang Gao, Kay Chen Tan, Xinyu Li
Summary: This article proposes a new adaptive epsilon control method and incorporates it into a basic differential evolution algorithm to solve constrained optimization problems. Compared with traditional methods, the proposed adaptive method prevents the algorithm from being trapped in local optima and retains near-optimal solutions in the infeasible region.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Kaipu Wang, Liang Gao, Xinyu Li, Peigen Li
Summary: Robot disassembly of End-of-life (EOL) products is an effective method to improve economic benefits and reduce environmental pollution. Parallel disassembly can shorten the makespan, and by establishing a multiobjective model and algorithm, high-quality disassembly schemes can be obtained.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Yaozu Liu, Junxia Ren, Yujie Wang, Xin Zhu, Xinyu Guan, Zisheng Wang, Yida Zhou, Liangkui Zhu, Shilun Qiu, Shengxiong Xiao, Qianrong Fang
Summary: A stable carbazole-based sp2 carbon fluorescence covalent organic framework (COF) nanosheet, named JUC-557 nanosheet, was developed for highly sensitive and selective molecular detection. The nanosheet exhibited high absolute quantum yield, excellent sensing performance, and retained strong luminescence and sensitive recognition even under harsh conditions.
Article
Engineering, Electrical & Electronic
Zongwei Du, Liang Gao, Xinyu Li
Summary: Surface defect recognition is crucial in intelligent manufacturing, and deep learning is a popular method for this task. However, the lack of available defective samples poses challenges for deep learning methods, so generative adversarial networks are used to generate synthetic samples. To improve training and image quality, a new GAN called contrastive GAN is proposed, which generates diverse defects with limited samples. Experimental results show that the proposed GAN generates higher quality defective images and improves the accuracy of DL networks.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Yang Li, Xinyu Li, Liang Gao
Summary: This article proposes an improved simulated annealing algorithm based on solution space clipping for large-scale PFSP. By preordering and combining the processed jobs, the solution space is significantly reduced. A hybrid release strategy based on the Palmer algorithm is developed, and key operators of the SA algorithm are improved. Experimental results show that the proposed method outperforms other algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Qian Wan, Liang Gao, Xinyu Li, Long Wen
Summary: Image anomaly detection and segmentation are crucial for automatic product quality inspection in intelligent manufacturing. This article proposes a novel framework, pretrained feature mapping (PFM), for unsupervised image anomaly detection and segmentation. The proposed framework achieves better results compared to state-of-the-art methods and is also superior in terms of computing time.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yiping Gao, Liang Gao, Xinyu Li
Summary: This paper proposes a hierarchical training-CNN with feature alignment for vision-based defect recognition in steel. The method achieves improved performance by introducing a feature alignment and a hierarchical training strategy. It outperforms other CNNs in recognition results and has been successfully applied in a real-world case with significant improvement.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Computer Science, Interdisciplinary Applications
You-Jie Yao, Qi-Hao Liu, Xin-Yu Li, Liang Gao
Summary: This paper studies the integrated scheduling of machines and mobile robots, proposing a novel mixed integer linear programming (MILP) model to minimize the makespan. The proposed model is the first MILP model to obtain optimal solutions for all instances. The comparison results verify the effectiveness and superior computational performance of the proposed model.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Chemistry, Multidisciplinary
Rui Zhu, Yangyang Pan, Hongbo Yu, Chengxin Huang, Hanxiao Tian, Tian Wang, Jingjing Xu, Shengxiong Xiao
Summary: We designed a series of isomeric tetraphenylethylene-pyridines and investigated the influence of the position of N atoms in the pyridine subunit on the photophysical property of the whole molecule. All compounds exhibited typical aggregation-induced emission properties, and the meta pyridyl isomer showed the highest solid photoluminescence quantum yield. Further analysis suggested that the dihedral angles of the TPE subunit's C=C bond played a vital role in their emission and quantum yield properties. This work provides underlying principles for the design of pyridyl-based tetraphenylethylene molecules with high photoluminescent performance in the future.
CHEMISTRY-AN ASIAN JOURNAL
(2023)
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
Engineering, Industrial
Hanghao Cui, Xinyu Li, Liang Gao, Chunjiang Zhang
Summary: Distributed manufacturing is becoming a future trend. This study focuses on the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines. An improved multi-population genetic algorithm is proposed to solve the problem. Experimental results show that the proposed method outperforms existing algorithms and achieves significant improvements in a real-world manufacturing case.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Mechanical
Lin Gui, Xinyu Li, Liang Gao, Cuiyu Wang
Summary: This paper explores the domain knowledge of the job-shop scheduling problem (JSP) and proposes sufficient and necessary constraint conditions to find all feasible neighbourhood solutions, allowing thorough local search. A new neighbourhood structure is designed and a fast calculation method for all feasible neighbourhood solutions is provided. Experimental results show that the calculation method is effective and the new neighbourhood structure outperforms other famous and influential structures.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Bin Wang, Long Wen, Xinyu Li, Liang Gao
Summary: This article proposes a new adaptive class center generalization network (ACCGN) to learn invariant feature representations of orientation signals from multiple source domains. ACCGN optimizes the data features from interclass and intraclass simultaneously, and has been tested on two famous bearing datasets, showing its effectiveness on the CWRU and JNU datasets.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Long Wen, Yang Zhang, Liang Gao, Xinyu Li, Min Li
Summary: This article proposes a new multiscale multiattention convolutional neural network (MSMA-SDD) for fine-grained surface defect detection. It uses features from different layers to match defects with different sizes and generates compact attention maps to focus on tiny defects. Experimental results show that MSMA-SDD outperforms the current most advanced method, with accuracy rates of 100%, 99.59%, and 99.57% on different datasets.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(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)
