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)
Article
Computer Science, Artificial Intelligence
Deming Lei, Bin Su
Summary: This study proposes a multi-class teaching-learning-based optimization (MTLBO) to minimize makespan and maximum tardiness simultaneously for the distributed hybrid flow shop scheduling problem (DHFSP) with sequence-dependent setup times. A two-string representation is adopted, and s classes are formed to improve search efficiency with a reward and punishment mechanism. Class evaluation, two teacher phases, and one learner phase are introduced for the evolution of each class. An elimination process is applied to the worst class to avoid wasting computing resources. Computational results from experiments demonstrate that MTLBO is a highly competitive method for DHFSP.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Industrial
Zhifeng Liu, Jun Yan, Qiang Cheng, Hongyan Chu, Jigui Zheng, Caixia Zhang
Summary: An adaptive selection multi-objective optimization algorithm with preference (ASMOAP) was developed to address the challenges in production scheduling involving continuous and discrete processing stages in a hybrid flow shop. The algorithm transforms mandatory constraints of continuous processing stages into optimization objectives and includes an optimization preference to improve scheduling efficiency. The results show that the proposed algorithm is effective in solving hybrid flow shop scheduling problems and can lead to better feasible solutions while reducing energy consumption in practical production processes.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Chao Lu, Yuanxiang Huang, Leilei Meng, Liang Gao, Biao Zhang, Jiajun Zhou
Summary: Energy-efficient scheduling of distributed production systems is essential for large companies in the context of economic globalization and green manufacturing. This paper presents a collaborative multi-objective optimization algorithm (CMOA) to address the Distributed Permutation Flow-Shop Problem with Limited Buffers (DPFSP-LB), aiming to minimize makespan and total energy consumption. The experimental results demonstrate the effectiveness of CMOA in solving the energy-efficient DPFSP-LB, achieving competitive results compared to other well-known multi-objective optimization algorithms.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Operations Research & Management Science
Guangchen Wang, Xinyu Li, Liang Gao, Peigen Li
Summary: This paper proposes a multi-objective scheduling method based on a multi-objective whale swarm algorithm to optimize the energy efficiency of distributed welding flow shop. By solving the problem of allocating jobs among factories, scheduling jobs in each factory, and determining the number of machines for each job, the proposed method shows superior performance in real-life cases. The experimental results demonstrate the effectiveness of the proposed algorithm.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Mathematics
Shuo Zhang, Jianyou Xu, Yingli Qiao
Summary: In this paper, an integrated distributed flow shop and distribution scheduling problem is studied, and a mathematical model is provided. An effective solution is designed by using a multi-objective Q-learning-based brain storm optimization to minimize makespan and total weighted earliness and tardiness. Numerical experimental results suggest that the proposed method outperforms its competitors in handling the problem.
Article
Computer Science, Information Systems
Cunli Song
Summary: This study investigates an energy-oriented scheduling problem derived from a hybrid flow shop with unrelated parallel machines. A hybrid multi-objective teaching-learning based optimization algorithm is proposed, which effectively reduces standby and turning on/off energy consumption, improves algorithm convergence speed, and enhances exploration and exploitation capabilities. Experimental results across 15 cases verify the effectiveness and superiority of the proposed algorithm.
Article
Computer Science, Information Systems
Kai Zhang, Chaonan Shen, Juanjuan He, Gary G. Yen
Summary: The proposed MMO-EvoKnee algorithm incorporates MCDM strategy to efficiently search for a complete set of global knee solutions for MMOPs. It outperforms existing state-of-the-art MMOEAs and provides decision makers with well-converged alternative solutions.
INFORMATION SCIENCES
(2021)
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
Multidisciplinary Sciences
Jidong Zhang, Jingcao Cai
Summary: A dual-population genetic algorithm with Q-learning is proposed to solve multi-objective distributed hybrid flow shop scheduling problems. Multiple crossover and mutation operators are used, and only one search strategy combination is selected in each iteration. Experimental results show the competitiveness of the proposed algorithm.
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper studies a multiobjective distributed hybrid flow shop scheduling problem (MDHFSP) and proposes a multi-objective evolutionary algorithm to solve it, optimizing solutions effectively through multiple neighborhoods local search operators and an adaptive weight updating mechanism.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Alireza Goli, Ali Ala, Mostafa Hajiaghaei-Keshteli
Summary: This study investigates the energy awareness of non-permutation flow-shop scheduling and lot-sizing problems and proposes a hybrid algorithm to optimize them. The proposed algorithm is validated and evaluated for efficiency using mathematical modeling and meta-heuristic algorithms, showing it can find optimal solutions and outperform other algorithms in terms of time and quality.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Youjun An, Xiaohui Chen, Yinghe Li, Yaoyao Han, Ji Zhang, Haohao Shi
Summary: With the proposal of an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm, this paper aims to solve the (hybrid) multi objective flexible job-shop scheduling problem. By introducing the V-dominance principle, HVNS structure and ESS strategy, the algorithm's performance has been enhanced and shows better performance compared to other intelligent algorithms.
APPLIED SOFT COMPUTING
(2021)
Correction
Computer Science, Information Systems
Sheng-Long Jiang, Long Zhang
Summary: The grant number of the National Natural Science Foundation of China (NSFC) is incorrect in the above article.
Article
Multidisciplinary Sciences
Mazen Farid, Heng Siong Lim, Chin Poo Lee, Rohaya Latip
Summary: One of the challenging aspects in scheduling operations on virtual machines in a multi-cloud environment is finding a near-optimal permutation. This study introduces a novel multi-objective minimum weight approach and expands the FR-MOS multi-objective scheduling algorithm using particle swarm optimization. The proposed method effectively discovers the best Pareto solutions.
Article
Engineering, Multidisciplinary
Yingjun Wang, Mi Xiao, Zhaohui Xia, Peigen Li, Liang Gao
Summary: This paper proposes a novel design mode, called human-aided design (HAD), to replace conventional computer-aided design (CAD). In HAD, computers can automatically complete product design using a new isogeometric topology optimization (ITO), while humans assist in making slight modifications. An embedded domain ITO is introduced for designing complex models with irregular domains, and editable geometric models of optimized results can be generated automatically. Experimental results on three different examples demonstrate the potential of the HAD mode to deliver high-quality optimized models, suggesting it as a revolutionary technology to transform the current design mode.
Article
Thermodynamics
Fan Yang, Dewen Liu, Min Lei, Yanping Zheng, Tiange Zhao, Liang Gao
Summary: The inter-story isolated structure is an effective and feasible seismic technology and system. However, most studies on inter-story isolated structures only focus on the mainshock, ignoring the potential damage caused by aftershocks. This study uses the incremental dynamic analysis method to analyze the vulnerability of an inter-story isolated structure under both the mainshock and main-aftershock sequences. The results show that aftershocks increase the exceedance probability of each substructure, and an appropriate isolation layer design can reduce the influence of aftershocks on the entire structure.
ADVANCES IN MECHANICAL ENGINEERING
(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
Instruments & Instrumentation
Min Wang, Songquan Liao, Yan Peng, Jiheng Ding, Yi Sun, Huayan Pu, Shaorong Xie, Jun Luo, Zhongjie Li, Zhengbao Yang
Summary: In order to improve the vibration isolation performance, a conventional passive two-parameter vibration isolator often adds constant damping for energy dissipation. However, there is a trade-off between suppressing resonance peak and rapid attenuation of transmissibility in the high-frequency band. A potential solution for this problem is a four-parameter vibration isolator with frequency-dependent damping. By establishing theoretical models, comparing vibration isolation performance, testing mechanical properties, and evaluating damping effect, it was found that the four-parameter-VI exhibits frequency-dependent damping and achieves a high vibration isolation ratio.
SMART MATERIALS AND STRUCTURES
(2023)
Article
Computer Science, Interdisciplinary Applications
Zan Yang, Haobo Qiu, Liang Gao, Liming Chen, Xiwen Cai
Summary: English Summary: This study proposes a constraint boundary Pursuing-based Surrogate-Assisted Differential Evolution (PSADE) method to solve complex optimization problems with mixed constraints, including both inequality and equality constraints. By using Trial Vector Generation Mechanism (TVGM) and Expected Improvement-based Local Search (EILS), PSADE maintains a good balance between convergence and diversity when considering both constraints and objective. Experimental results show that PSADE is highly competitive in solving ECOPs with mixed constraints under an acceptable computational cost.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(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
Engineering, Multidisciplinary
Yan Zhang, Mi Xiao, Zhe Ding, Manman Xu, Guozhang Jiang, Liang Gao
Summary: Compared with conventional symmetric sandwich structures, geometrically asymmetric sandwich structures (GASSs) have better dynamic performance due to expanded design space. This paper proposes a dynamic response-oriented multiscale topology optimization method for GASSs, optimizing the thicknesses of face-sheets, distribution of cores, and their topological configurations to minimize dynamic compliance.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Xiliang Liu, Liang Gao, Mi Xiao
Summary: This paper proposes a multiscale concurrent topology optimization method for design of hierarchal multi-morphology lattice structures. The method utilizes Kriging metamodel and sigmoid function based hybrid transition strategy to achieve smooth transition between multi-morphology lattice unit cells. It also employs KUMMI model to couple the design variables and optimize the relative densities of lattice unit cells. Numerical examples demonstrate the effectiveness and applicability of the proposed method, showing rational distribution of hierarchal multi-morphology lattice unit cells and superior structural performance.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Zan Yang, Haobo Qiu, Liang Gao, Liming Chen, Jiansheng Liu
Summary: This paper proposes an adaptive surrogate-assisted MOEA/D framework (ASA-MOEA/D) for efficiently solving expensive constrained multi-objective optimization problems. With three specific search strategies, ASA-MOEA/D achieved targeted searches for different subproblems based on their optimization states. The framework maintained feasibility, convergence, and diversity through the use of RBF surrogates and exploration of unexplored subregions. Empirical studies showed that ASA-MOEA/D with tchebycheff approach outperformed four state-of-the-art algorithms.
INFORMATION SCIENCES
(2023)
Article
Engineering, Manufacturing
Yiping Gao, Liang Gao, Xinyu Li
Summary: Human-robot collaboration has the potential for surface defect inspection in smart manufacturing. To overcome the bottleneck of sample selection and improve inspection results, a two-stage Transformer model with focal loss is proposed, allowing workers to collaborate and recheck defects.
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
(2023)
Article
Engineering, Industrial
Jiang-Ping Huang, Liang Gao, Xin-Yu Li, Chun-Jiang Zhang
Summary: This paper proposes a novel Priority Dispatch Rules (PDRs) generation method based on Graph Neural Network (GNN) and Reinforcement Learning (RL) for the Distributed Job-shop Scheduling Problem (DJSP). The method can self-learn and self-evolve by interacting with the scheduling environment. A new solution representation based on disjunctive graph is designed to combine DJSP with GNN closely. Comprehensive experiments show that the proposed method performs better than other classical PDRs, metaheuristics, and RL-based methods in terms of effectiveness, generalizability, and stability.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Dapeng Wang, Haobo Qiu, Liang Gao, Danyang Xu, Chen Jiang
Summary: In this paper, a new single-loop active learning Kriging method with probability of rejecting classification is proposed for solving time-dependent system reliability analysis problems. The method makes full use of the response information of all potential failure time instants or failure modes to improve the sampling efficiency and algorithm interpretability. An effective active learning strategy is developed to identify the new training sample and the target Kriging model to be updated corresponding to a certain failure mode. The proposed method demonstrates excellent efficiency and computational accuracy in three examples.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Engineering, Electrical & Electronic
Lei Zuo, Hongyong Xiao, Long Wen, Liang Gao
Summary: Surface defect detection is a crucial task in the smart industry. This research proposes a pixel-level segmentation convolutional neural network, MMPA-Net, based on multi-scale features, global mapping, feature pyramid, and attention mechanisms. MMPA-Net achieves state-of-the-art results on three public SDD datasets, outperforming other deep learning methods in terms of intersection over union (IoU).
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(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
Chemistry, Physical
Lu Zhang, Yan Li, Run Hu, Jie Yin, Qinglei Sun, Xiaodong Li, Liang Gao, Huasheng Wang, Wei Xiong, Liang Hao
Summary: Selective laser melting (SLM) is utilized to fabricate metal matrix composites (MMCs) meta-materials with triply periodic minimal surface (TPMS) structures, which exhibit exceptional mechanical properties. These materials have great potential in energy conversion, heat management and lightweight applications.
JOURNAL OF ALLOYS AND COMPOUNDS
(2023)
