Article
Mathematics
Juan Carlos Seck-Tuoh-Mora, Nayeli Jazmin Escamilla-Serna, Leonardo Javier Montiel-Arrieta, Irving Barragan-Vite, Joselito Medina-Marin
Summary: This paper proposes a new algorithm for FJSSP with fuzzy processing times, which explores solutions using global neighborhood handling and hill-climbing algorithm. Experimental results show that it is competitive with state-of-the-art algorithms.
Article
Management
Karim Tamssaouet, Stephane Dauzere-Peres
Summary: This article presents a framework that unifies and generalizes well-known literature results on local search for job-shop and flexible job-shop scheduling problems. The proposed framework focuses on quickly ruling out infeasible moves and evaluating the quality of feasible neighbors, which are crucial for the success of local search approaches. It can be applied to any scheduling problem with an appropriate defined neighborhood structure. The proposed framework introduces novel procedures for evaluating feasibility and estimating the value of objective functions for neighbor solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Jun-qing Li, Zheng-min Liu, Chengdong Li, Zhi-xin Zheng
Summary: This article proposes an improved artificial immune system (IAIS) algorithm to solve a special case of the flexible job shop scheduling problem (FJSP), where the processing time of each job is a nonsymmetric triangular interval T2FS (IT2FS) value. The algorithm shows enhanced abilities in handling high levels of uncertainty and asymmetric triangular interval values. Through novel affinity calculation methods, problem-specific initialization heuristics, local search approaches, and population diversity heuristics, the algorithm demonstrates improved exploitation and exploration capabilities.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Nayeli Jazmin Escamilla Serna, Juan Carlos Seck-Tuoh-Mora, Joselito Medina-Marin, Norberto Hernandez-Romero, Irving Barragan-Vite, Jose Ramon Corona Armenta
Summary: The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that has been extensively studied to model and optimize more complex situations reflecting the current needs of the industry. This work introduces a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), which utilizes the concepts of a cellular automaton to generate and share information among a set of leading solutions called smart-cells. Experimental results demonstrate the satisfactory performance of the GLNSA algorithm when compared with recent algorithms, using four benchmark sets and 101 test problems.
PEERJ COMPUTER SCIENCE
(2021)
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)
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)
Article
Computer Science, Interdisciplinary Applications
Rui Li, Wenyin Gong, Chao Lu
Summary: This study addresses the multi-objective flexible job shop scheduling problem and proposes a hybrid self-adaptive multi-objective evolutionary algorithm based on decomposition (HPEA) to solve it. The algorithm shows better performance in solving the problem by utilizing problem-specific initial rules, local search methods, solution selection method, and parameter selection strategy.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Automation & Control Systems
Mohamed Abdel-Basset, Reda Mohamed, Doaa El-Shahat, Karam M. Sallam
Summary: Due to the uncertainty in manufacturing and production systems, a flexible job-shop scheduling approach using fuzzy processing times is proposed. The approach incorporates four operators to improve solution quality and integrates a local search strategy. The proposed method is compared to twenty-five algorithms and shown to be superior based on statistical analyses.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(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
Computer Science, Artificial Intelligence
Rui Li, Wenyin Gong, Chao Lu, Ling Wang
Summary: This study proposes a mixed-integer linear programming model and a learning-based reference vector memetic algorithm (LRVMA) to solve the multiobjective energy-efficient flexible job-shop scheduling problem (FJSP) with type-2 fuzzy processing time (ET2FJSP). LRVMA includes specific initial rules, local search methods, a solution selection method based on Tchebycheff decomposition strategy, a reinforcement learning-based parameter selection strategy, and an energy-saving strategy. Experimental results show that LRVMA outperforms other algorithms for solving ET2FJSP.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Operations Research & Management Science
Moussa Abderrahim, Abdelghani Bekrar, Damien Trentesaux, Nassima Aissani, Karim Bouamrane
Summary: This paper addresses the problem of job assignment in a job-shop manufacturing system and proposes an improved algorithm to minimize the maximum completion time of a job set. Experimental tests demonstrate the effectiveness of the proposed approach.
OPTIMIZATION LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Nanlei Chen, Naiming Xie, Yuquan Wang
Summary: This paper investigates a flexible job shop scheduling problem with uncertain processing time using generalized grey numbers. An elite genetic algorithm is developed to find excellent solutions and the experiments demonstrate the effectiveness and competitiveness of the proposed algorithm.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Pablo Garcia Gomez, Ines Gonzalez-Rodriguez, Camino R. Vela
Summary: This article discusses the flexible job shop scheduling problem and its variant where uncertainty in operation processing times is modeled using triangular fuzzy numbers. The objective is to minimize total energy consumption, considering the energy required by resources during operation and the energy consumed when resources are switched on. To solve this NP-Hard problem, a memetic algorithm is proposed, combining global search and local search. The focus is on obtaining an efficient method that can achieve similar solutions to existing state-of-the-art approaches in less time. An extensive experimental analysis compares the algorithm with previous proposals and evaluates the effect of different components on the search.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Xixing Li, Xing Guo, Hongtao Tang, Rui Wu, Lei Wang, Shibao Pang, Zhengchao Liu, Wenxiang Xu, Xin Li
Summary: This paper presents a comprehensive literature review on the integrated optimization of the flexible job shop scheduling problem (FJSP). Five different integration models of FJSP are explained and research challenges and directions are demonstrated. The study aims to aid academic researchers and application engineers in Industry 4.0.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Rui Li, Wenyin Gong, Chao Lu
Summary: This paper presents a multiobjective method for solving the flexible job shop scheduling problem with fuzzy processing time. The proposed algorithm outperforms five state-of-the-art methods in three benchmark tests.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Jian Lin, Lei Zhu, Zhou-Jing Wang
COMPUTERS & INDUSTRIAL ENGINEERING
(2019)
Article
Thermodynamics
Jian Lin, Zhou-Jing Wang
Article
Computer Science, Interdisciplinary Applications
Zhou-Jing Wang, Fang Liu, Jian Lin
COMPUTERS & INDUSTRIAL ENGINEERING
(2019)
Article
Computer Science, Information Systems
Zhou-Jing Wang, Jian Lin
INFORMATION SCIENCES
(2019)
Article
Computer Science, Information Systems
Zhou-Jing Wang, Jian Lin, Fang Liu
INFORMATION SCIENCES
(2019)
Article
Computer Science, Artificial Intelligence
Mingzhou Chen, Shuai Zhang, Wenyu Zhang, Jian Lin
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Lei Zhu, Jian Lin, Zhou-Jing Wang
APPLIED SOFT COMPUTING
(2019)
Article
Computer Science, Artificial Intelligence
Jian Lin, Lei Zhu, Kaizhou Gao
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Zhou-Jing Wang, Jian Lin
INFORMATION SCIENCES
(2020)
Article
Engineering, Industrial
Jiaxuan Shi, Wenyu Zhang, Shuai Zhang, Weirui Wang, Jian Lin, Ruijun Feng
JOURNAL OF MANUFACTURING SYSTEMS
(2020)
Article
Computer Science, Interdisciplinary Applications
Wenyu Zhang, Jiuhong Xiao, Shuai Zhang, Jian Lin, Ruijun Feng
Summary: This article proposes a new utility-aware cloud manufacturing multi-task scheduling model, incorporating the utilities of both customers and manufacturers. An improved non-dominated sorting genetic algorithm-II is used to find the approximate optimal Pareto solution set, which is then ranked using game theory to recommend the optimal solution to the cloud manufacturing system. Simulation experiments confirm the effectiveness of the proposed algorithm compared to three baseline multi-objective evolutionary algorithms.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Artificial Intelligence
Hong-Bo Song, Jian Lin
Summary: The paper introduces a GP-HH algorithm to address the DAPFSP-SDST problem by using genetic programming to generate heuristic sequences and incorporating simulated annealing for local search, achieving effective solutions and improving upon existing benchmarks.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Yang-Yuan Li, Jian Lin, Zhou-Jing Wang
Summary: This paper presents a multi-objective discrete Jaya algorithm to address the multi-skill resource constrained project scheduling problem, aiming to minimize makespan and total cost simultaneously. The algorithm enhances exploration ability using simple heuristics and efficient encoding and decoding pairs to construct feasible schedules, while also proposing different assignment criteria to increase solution diversity. The performance evaluation on a benchmark dataset indicates the superiority of the proposed MODJaya algorithm in solving the multi-objective MS-RCPSP.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Lei Zhu, Jian Lin, Yang-Yuan Li, Zhou-Jing Wang
Summary: This paper proposes an efficient decomposition-based multi-objective genetic programming hyper-heuristic approach for solving the multi-skill resource constrained project scheduling problem. The effectiveness of the proposed method has been validated through experiments.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jian Lin, Yang-Yuan Li, Hong-Bo Song
Summary: The paper introduces a Q-learning based hyper-heuristic algorithm to address the semiconductor final testing scheduling problem, which autonomously selects low-level heuristics to optimize the solution space and improve resource utilization, demonstrating the effectiveness and efficiency of the algorithm through computational simulation and comparison on a benchmark set.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
