Article
Computer Science, Artificial Intelligence
Biao Zhang, Quan-Ke Pan, Lei-Lei Meng, Xin-Li Zhang, Ya-Ping Ren, Jun-Qing Li, Xu-Chu Jiang
Summary: This paper introduces the issue of consistent sublots into the hybrid flowshop scheduling problem and develops a mixed integer linear programming (MILP) model and a collaborative variable neighborhood descent algorithm (CVND). The CVND shows excellent performance in local exploitation and global search, with high algorithm efficiency. Results indicate that the CVND has significant advantages in solution quality and relative percentage deviation values.
APPLIED SOFT COMPUTING
(2021)
Article
Green & Sustainable Science & Technology
Ziyue Wang, Liangshan Shen, Xinyu Li, Liang Gao
Summary: This paper addresses the problem of energy-efficient hybrid flowshop rescheduling under machine breakdown and proposes an improved multi-objective firefly algorithm to optimize production efficiency, energy consumption, and production stability.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Engineering, Industrial
Kunkun Peng, Xudong Deng, Chunjiang Zhang, Weiming Shen, Yanan Song, Jianhui Mou, Ao Liu
Summary: This paper proposes a mathematical model and a method to solve the problem of SCC rescheduling considering charge start-time delay. By designing three Tabu based neighbourhood structures and special strategies, the experimental results demonstrate the effectiveness of the proposed method.
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Biao Zhang, Chao Lu, Lei-lei Meng, Yu-yan Han, Hong-yan Sang, Xu-chu Jiang
Summary: Inspired by a real-world cellular manufacturing system, this study focuses on a reconfigurable distributed flowshop scheduling problem with grouped jobs. A mixed integer linear programming model is developed for small-scaled instances, and a nested variable neighborhood descent algorithm is proposed for larger instances. The proposed algorithm outperforms other state-of-the-art metaheuristics and the math solver CPLEX.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Industrial
Biao Zhang, Quan-ke Pan, Lei-lei Meng, Xin-li Zhang, Xu-chu Jiang
Summary: Lot streaming is a widely used technique to overlap successive operations. This study addresses the multi-objective hybrid flowshop rescheduling problem with consistent sublots (MOHFRP_CS) and proposes a multi-objective migrating birds optimisation algorithm based on decomposition (MMBO/D). The algorithm decomposes the problem into sub-problems, dynamically adjusts the weights assigned to the sub-problems, and employs a global update strategy. Experimental results demonstrate that MMBO/D outperforms other state-of-the-art multi-objective evolutionary algorithms for the addressed problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Mathematics
Chengshuai Li, Biao Zhang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao
Summary: This paper focuses on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. It proposes an improved cooperative coevolutionary algorithm (vCCEA) by integrating the variable neighborhood descent (VND) strategy. The algorithm features a two-layer encoding strategy, a novel collaborative model, special neighborhood structures, and a collaborative population restart mechanism. The computational results show that vCCEA outperforms other algorithms in solving each subproblem of HFSP_ECS effectively.
Article
Engineering, Multidisciplinary
Mohammed Abdelghany, Amr B. Eltawil, Zakaria Yahia, Kazuhide Nakata
Summary: Nurse rostering problem aims to satisfy cover requirements while considering operational needs and nurses' preferences. Known to be NP-hard, this problem has been tackled using various metaheuristics.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2021)
Article
Chemistry, Multidisciplinary
Jianguo Zheng, Yilin Wang
Summary: A hybrid bat optimization algorithm is proposed in this paper to solve a three-stage distributed assembly permutation flowshop scheduling problem, with the aim of minimizing makespan. By classifying populations, utilizing a selection mechanism, and implementing learning strategies to aid the population in jumping out of local optimal frontiers, the algorithm effectively addresses the trade-offs between convergence, diversity, exploration, and mining capacity. The simulation results show that the proposed algorithm outperforms other metaheuristic algorithms in solving the DAPFSP.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
Yan Lv, Congbo Li, Ying Tang, Yang Kou
Summary: This study focuses on achieving energy-efficient production in a dynamic environment and proposes rescheduling mechanisms for energy savings. By utilizing a mixed-integer programming model and a heuristic framework algorithm, optimization of energy-efficient flexible job shops is successfully achieved.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Mathematics
Gintaras Palubeckis, Armantas Ostreika, Jurate Platuziene
Summary: The paper focuses on the dynamic single row facility layout problem (DSRFLP) and proposes a variable neighborhood search (VNS) algorithm and a fast local search (LS) procedure. Computational experiments demonstrate the effectiveness of the proposed algorithms and show that they outperform existing methods.
Article
Mathematics
Pablo Valledor, Alberto Gomez, Javier Puente, Isabel Fernandez
Summary: This paper proposes a hybrid dynamic non-dominated sorting genetic algorithm for solving multi-objective rescheduling problems in dynamic permutation flow shop contexts. The algorithm can find the optimal Pareto front and performs well under different types of disruptions.
Article
Computer Science, Artificial Intelligence
Teena Mittal
Summary: A hybrid optimization technique integrating MFO and VNS has been proposed for searching optimal coefficients of IIR filters. It outperforms state-of-the-art techniques in minimizing objective function and improving desirable attributes for both low-pass and high-pass IIR filter designs.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zhifeng Liu, Junlong Wang, Caixia Zhang, Hongyan Chu, Guozhi Ding, Lu Zhang
Summary: This paper introduces a novel variable neighbourhood descent hybrid genetic algorithm (VNDhGA) to improve the convergence speed and accuracy of genetic algorithms for solving the flexible job shop scheduling problem (FJSSP). The algorithm integrates multiple techniques and demonstrates superior performance compared to existing algorithms on benchmark cases.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Amin Vahedian Khezerlou, Xun Zhou, Ling Tong, Yanhua Li, Jun Luo
Summary: Researchers proposed a Dynamic Hybrid model (DH-VIGO-TKDE) to predict urban gathering events, addressing the limitations of previous models. The experiments showed that the model significantly improved accuracy and timeliness in forecasting, with an average precision of 0.91 and recall of 0.67 compared to H-VIGO-GIS's 0.74 and 0.50.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Thermodynamics
Hanqing Xia, Ningfei Wang, Junsen Yang, Yi Wu
Summary: The dynamic mixing combustion characteristics in variable thrust hybrid rocket motors (HRMs) have been investigated using a two-dimensional transient numerical model. The model accurately predicts the regression behavior of fuel grains in HRMs and simulations of variable thrust processes were performed. The results show that recirculation zones directly affect the temperature distribution and fuel regression rate along the axial direction. Fluctuations in the fuel regression rate and a decrease in combustion efficiency were observed during the thrust variation process. A slight thrust loss was observed at all stages of the variable thrust process.
COMBUSTION AND FLAME
(2023)
Article
Automation & Control Systems
Sicheng Xie, Xinyu Li, Liang Gao, Ling Fu, Li Jing, Weifeng Xu
Summary: This study proposes an online whole-stage gait planning method to enhance the bipedal walking performance. A new template model called Variable Spring-Loaded Inverted Pendulum with Finite-sized Foot (VSLIP-FF) model is applied, considering the role of ankles. A Finite State Machine (FSM)-based gait pattern with corresponding bio-inspired gait strategies is established. Furthermore, an online gait generator based on a neural network is applied for real-time gait planning. Experimental results demonstrate the effectiveness of the proposed method.
Article
Computer Science, Information Systems
Zan Yang, Haobo Qiu, Liang Gao, Danyang Xu, Yuanhao Liu
Summary: This paper proposes a general framework of surrogate-assisted evolutionary algorithms (GF-SAEAs) to adaptively arrange search strategies based on actual simulation cost differences. It classifies all constraints and designs a level-by-level feasible region-driven local search strategy to locate potential sub-feasible regions. Three different search mechanisms are employed to explore and exploit these located regions. Experimental studies show that GF-SAEAs outperform other state-of-the-art algorithms.
INFORMATION SCIENCES
(2023)
Article
Electrochemistry
Maokun Xiong, Ningbo Wang, Wei Li, Akhil Garg, Liang Gao
Summary: This work investigates the impact of pin-fins on the heat dissipation capability of BTMS. The findings demonstrate that square-section pin-fins offer better heat dissipation than other shapes. Increasing the number of pin-fins decreases the maximum battery temperature but increases the pressure drop. Uniform distribution of pin-fins has a superior heat dissipation effect compared to other distribution schemes.
Article
Engineering, Multidisciplinary
Jie Gao, Xiaomeng Wu, Mi Xiao, Vinh Phu Nguyen, Liang Gao, Timon Rabczuk
Summary: This study proposes a Multi-Patch Isogeometric Topology Optimization (MP-ITO) method for the design of periodic or graded cellular structures. The method applies Nitsche's method to couple non-conforming meshes and conducts multi-patch isogeometric analysis. A multi-patch topology description model is developed to improve smoothness and continuity of boundaries at interfaces within adjacent subdomains. The effectiveness and capabilities of the MP-ITO method are demonstrated through numerical examples.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Hang Li, Hao Li, Liang Gao, Yongfeng Zheng, Jiajing Li, Peigen Li
Summary: This paper presents a topology optimization method for a multi-phase shell-infill composite, accounting for the coated shell and graded multi-phase microstructural infill. The method combines general structural configuration design with detailed design of the graded multi-phase infill architecture. It avoids scale separation, ensures microstructure connectivity, and is applicable for additive manufacturing.
ENGINEERING WITH COMPUTERS
(2023)
Article
Computer Science, Information Systems
Xiwen Cai, Gan Ruan, Bo Yuan, Liang Gao
Summary: This study proposes an efficient surrogate-assisted differential evolution algorithm by hybridizing two complementary strategies to optimize expensive multi-objective problems with limited computational resources. One strategy is an improved local search method based on maximin angle-distance sequential sampling. The other strategy is prescreening based on a diversity-enhanced expected improvement matrix infill criterion.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Yuanhao Liu, Zan Yang, Danyang Xu, Haobo Qiu, Liang Gao
Summary: This paper proposes a surrogate-assisted differential evolution algorithm (SADE-MI) for solving expensive constrained optimization problems with mixed-integer variables. It overcomes the challenges caused by mixed-integer variables through adaptive pre-screening operation and population diversity maintenance operation, and outperforms other classical algorithms in benchmark problems and engineering optimization cases.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Zhaohui Xia, Haobo Zhang, Ziao Zhuang, Chen Yu, Jingui Yu, Liang Gao
Summary: In this paper, an isogeometric topology optimization method based on deep neural networks is proposed, which effectively reduces the computational time of optimization while ensuring high accuracy. The machine-learning dataset is obtained during early iterations with the IGA-FEA two-resolution SIMP method. Online dataset generation significantly reduces data collection time and enhances relevance to the design problem. The proposed model's generality and reliability have been verified through a series of 2D and 3D design examples, and its time-saving advantage becomes more pronounced as the design scale increases. Furthermore, controlled experiments have studied the impacts of neural network parameters on the results.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Engineering, Civil
Kang Liu, Wei Chen, Jihong Ye, Liang Gao, Jian Jiang
Summary: This study quantifies the width development of joint induced by fire and its impact on the fire performance of CFS walls. Six mid-scale CFS walls with artificial board joints were tested under ISO 834 fire conditions. The study provides important data for investigating heat transfer numerical simulation of CFS walls with joints in fire.
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
Energy & Fuels
Bibaswan Bose, Su Shaosen, Wei Li, Liang Gao, Kexiang Wei, Akhil Garg
Summary: This paper proposes a Cloud-BMS based health-aware battery fast charging (HABFC) architecture with an error correction strategy to reduce charging time and increase battery cycle life. By reviewing various techniques and computational methods, the best method for each state estimation technique is determined. Experimental results show that the proposed architecture significantly increases battery cycle life compared to regular fast charging and HABFC without cloud BMS.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Review
Computer Science, Information Systems
Arkaprabha Basu, Sandip Paul, Sreeya Ghosh, Swagatam Das, Bhabatosh Chanda, Chakravarthy Bhagvati, Vaclav Snasel
Summary: Digitized methodologies have revolutionized various fields, including the restoration of buildings with historical significance. This interdisciplinary field attracts computer scientists who use computerized tools to reconstruct the values of these structures. The wear of time has endangered significant historical values, but this survey explores the use of 3D reconstruction, image inpainting, IoT-based methods, genetic algorithms, and image processing to restore cultural heritage. Machine Learning, Deep Learning, and Computer Vision-based methods are discussed, offering insights into faster, cheaper, and more beneficial techniques for image reconstruction in the near future.
Article
Energy & Fuels
Bibaswan Bose, Saladi Sairam Teja, Akhil Garg, Liang Gao, Wei Li, Surinder Singh, B. Chitti Babu
Summary: In this article, an optimized health-aware battery-fast-charging approach using multistep constant-current constant-voltage (MSCCCV) technology is proposed. The thermal-aging cell model (TACM) is developed to generate a simulated cell model, and a cycle life predictor based on multi-input elastic net regression is developed. An adaptive MSCCCV-charging strategy is devised and optimized using whale optimization algorithm. The superiority of the MSCCCV technique is demonstrated through comparison with benchmark techniques.
Review
Materials Science, Multidisciplinary
Jie Gao, Xiaofei Cao, Mi Xiao, Zhiqiang Yang, Xiaoqiang Zhou, Ying Li, Liang Gao, Wentao Yan, Timon Rabczuk, Yiu-Wing Mai
Summary: This paper provides a comprehensive overview of the significant advances in Mechanical Metamaterials (MMs), including different scales of critical focuses, forward and inverse design mechanisms, micro architectures, and spatial tessellations. It emphasizes the importance of unique structures, micro unit cells, and mechanisms in MMs. The study demonstrates that inverse design can achieve unprecedented properties and plays a crucial role in material and multiscale design. Finally, several challenging yet promising research topics in design formulations, micro architectures, spatial tessellations, and industrial applications are proposed.
MATERIALS SCIENCE & ENGINEERING R-REPORTS
(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
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)
