Article
Computer Science, Artificial Intelligence
Shih-Wei Lin, Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying, Chia-Hui Lee
Summary: HFSP is a well-recognized scheduling problem in industrial applications, and the NP-hard nature of the problem requires effective solution approaches. The proposed Chaos-enhanced Simulated Annealing algorithm showed strong performance in terms of computational efficiency and stability, making it a promising benchmark for solving HFSP and its extensions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Shih-Wei Lin, Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying
Summary: Scheduling problems are crucial in modern manufacturing, and an improved meta-heuristic algorithm, MTSA, has been proposed for Permutation Flowshop Scheduling Problem with Mixed-Blocking Constraints, outperforming existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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
Engineering, Industrial
Kuo-Ching Ying, Pourya Pourhejazy, Chen-Yang Cheng, Ren-Siou Syu
Summary: This research extends the distributed assembly permutation flowshop scheduling problem to account for flexible assembly and sequence-independent setup times in a supply chain-like setting. Constructive heuristic and customised metaheuristic algorithms are proposed to solve this emerging scheduling extension, demonstrating higher performance compared to existing algorithms.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
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
Mathematics
Chen-Yang Cheng, Shih-Wei Lin, Pourya Pourhejazy, Kuo-Ching Ying, Yu-Zhe Lin
Summary: The production environment in modern industries features zero idle-time between jobs on each machine, improving energy efficiency and impacting cleaner production in other scenarios. This study developed an extended solution for optimizing the Bi-objective No-Idle Permutation Flowshop Scheduling Problem (BNIPFSP) after conducting a comprehensive literature review. Extensive numerical tests and statistical analysis revealed that the proposed extension outperformed in terms of solution quality, although at the expense of longer computational time.
Article
Computer Science, Interdisciplinary Applications
Andreia F. Silva, Jorge M. S. Valente, Jeffrey E. Schaller
Summary: This paper addresses the permutation flowshop problem with a weighted squared tardiness objective function. Four metaheuristics are proposed to improve the solution quality over existing methods, and the results show significant improvements. The iterated greedy method proves to be the most effective, followed by the steady-state genetic algorithms. The choice of initial sequence and local search has little effect on the performance of the metaheuristics. Increasing the time limit improves the performance of all procedures.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying, Yi-Hsiu Liao
Summary: This study successfully addressed the No-wait Flowshop Group Scheduling Problems, achieving a best-found solution rate of over 99.7% through the development of two metaheuristics. The results indicate that RMSA outperforms existing algorithms for solving the NWFGSP_SDST problem.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying, Shi-Yao Huang
Summary: This study developed an effective metaheuristic to address Blocking Flowshop Scheduling Problems with Sequence-Dependent Setup-Times, showing superior performance and potential applications in solving other complex scheduling problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Guanghui Zhang, Bo Liu, Ling Wang, Dengxiu Yu, Keyi Xing
Summary: This article presents a distributed co-evolutionary memetic algorithm (DCMA) to solve a practical distributed hybrid differentiation flowshop scheduling problem (DHDFSP). The DCMA framework includes four basic modules that cooperate with each other and allow search agents to co-evolve. Computational experiments demonstrate the effectiveness of the DCMA framework and its special designs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Mario Tonizza Pereira, Marcelo Seido Nagano
Summary: In today's complex business environment, the optimisation of operational processes is crucial for the survival of organizations. The coordination and integration of manufacturing and logistics activities are essential for improving service levels and operational performance. This article proposes and evaluates new heuristic methods for scheduling production and distribution operations, aiming to minimize delivery times and increase service levels. The experimental results demonstrate the potential of these methods to solve transportation-related problems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
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
Computer Science, Artificial Intelligence
Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying, Chen-Fang Lin
Summary: This study utilizes an Unsupervised Learning-based Artificial Bee Colony algorithm to minimize non-value-adding activities in production settings with prevalent setup operations, improving solution quality by reducing setup times through a learning mechanism. The gap between scheduling theory and modern industrial applications is narrowed through the application of advanced analytics in production management context.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Manufacturing
Arpan Rijal, Marco Bijvank, Rene de Koster
Summary: This paper examines the trade-offs between warehouse operations and transportation planning and investigates the impact of three managerial interventions: adopting an integrated planning approach, expanding staging space, and expanding delivery time windows. Results from case studies show that integrated planning outperforms the other interventions in terms of cost savings. Expanding delivery time windows by 15 minutes can lead to significant cost reductions in both transportation and warehousing. Expanding staging capacity only results in cost savings for warehouse operations.
PRODUCTION AND OPERATIONS MANAGEMENT
(2023)
Article
Chemistry, Multidisciplinary
Nathalie Klement, Mohamed Amine Abdeljaouad, Leonardo Porto, Cristovao Silva
Summary: In this paper, a scheduling algorithm that combines a metaheuristic and a list algorithm is proposed and tested. Experimental results show that this algorithm outperforms the scheduling policy conducted in a case-study company. The method is not only able to solve large real-world problems efficiently, but also has a flexible structure that can be easily adapted to various planning and scheduling problems.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Industrial
Ying-Ying Huang, Quan-Ke Pan, Liang Gao
Summary: This paper investigates the distributed permutation flowshop scheduling problem and proposes an effective memetic algorithm (EMA). A constructive heuristic and an initialisation method are used to generate high-quality and diverse initial populations. The EMA uses a new structure of a small iteration nested within a large iteration and includes targeted and flexible local search methods. The experimental results confirm the effectiveness and efficiency of the EMA.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Wen-Qiang Zou, Quan-Ke Pan, Ling Wang, Zhong-Hua Miao, Chen Peng
Summary: Green manufacturing has gained significant attention, but the energy efficiency problem in matrix manufacturing workshops remains unaddressed. This paper proposes a novel automatic guided vehicle (AGV) energy-efficient scheduling problem with release time (AGVEESR) to optimize energy consumption, number of AGVs used, and customer satisfaction simultaneously.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yuan-Zhen Li, Quan-Ke Pan, Ruben Ruiz, Hong-Yan Sang
Summary: This paper studies the distributed assembly mixed no-idle permutation flowshop scheduling problem (DAMNIPFSP) with the objective of minimizing total tardiness. An improved Iterated Greedy algorithm named RIG (Referenced Iterated Greedy) is proposed, which includes two novel destruction methods, four new reconstruction methods, and six new local search methods based on the characteristics of DAMNIPFSP. Experimental results show that RIG algorithm is a state-of-the-art procedure for DAMNIPFSP with the total tardiness criterion.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Qingda Chen, Jinliang Ding, Tianyou Chai, Quanke Pan
Summary: This article studies the operational optimization problem of the FCC unit under uncertainty and proposes a fast adaptive differential evolution algorithm to solve it. The experimental results demonstrate the robustness of the proposed algorithm.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
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, Interdisciplinary Applications
Xin-Rui Tao, Quan-Ke Pan, Liang Gao
Summary: The study proposes a self-adaptive artificial bee colony algorithm to solve distributed resource-constrained hybrid flowshop scheduling problems. Utilizing a two-dimensional vector solution representation, the algorithm also incorporates a self-adaptive perturbation structure and local search strategy to enhance its searching abilities.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
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
Computer Science, Artificial Intelligence
Shuai Chen, Quan-Ke Pan, Liang Gao, Zhong-Hua Miao, Chen Peng
Summary: This paper studies an energy-efficient distributed blocking flowshop scheduling problem and proposes a knowledge-based iterated Pareto greedy algorithm (KBIPG) to simultaneously minimize the makespan and total energy consumption. By adjusting machine speeds and designing local intensification methods, the effectiveness of the algorithm is demonstrated.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xuan He, Quan-Ke Pan, Liang Gao, Ling Wang, Ponnuthurai Nagaratnam Suganthan
Summary: This article addresses the flowshop sequence-dependent group scheduling problem (FSDGSP) by considering both production efficiency measures and energy efficiency indicators. A mixed-integer linear programming model and a critical path-based accelerated evaluation method are proposed. A greedy cooperative co-evolutionary algorithm (GCCEA) is designed to explore the solution space, and a random mutation operator and a greedy energy-saving strategy are employed.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Zhongkai Li, Hongyan Sang, Quanke Pan, Kaizhou Gao, Yuyan Han, Junqing Li
Summary: In this article, a dynamic AGV scheduling model is proposed, which includes an aperiodic departure method and a real-time task list update method. The model can reassign AGVs for new tasks and special cases, proving its effectiveness through verification using a discrete invasive weed optimization algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Jianhui Mou, Peiyong Duan, Liang Gao, Quanke Pan, Kaizhou Gao, Amit Kumar Singh
Summary: This study explores the application of biologically inspired Plasticity Neural Network in the industrial intelligent dispatching energy storage system, focusing on the intelligence and fault detection performance of the control system. By implementing a fault diagnosis model based on Deep Belief Network, the study achieves a 100% transmission probability for the constructed intelligent energy storage scheduling system. Compared with other classical algorithm models, the proposed algorithm shows a higher success rate, detection accuracy, lower energy consumption, and more significant detection effect. Therefore, the constructed system has higher real-time performance, more accurate fault detection performance, and significantly better system detection and protection performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Bin Han, Yicheng Lin, Yan Dong, Hao Wang, Tao Zhang, Chengyuan Liang
Summary: This study proposes an efficient and stable camera attributes control method using distinct image quality metrics and a linear convergence search algorithm to adjust camera acquisition attributes for clear and information-rich images.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Xin-Rui Tao, Quan-Ke Pan, Hong-Yan Sang, Liang Gao, Ao-Lei Yang, Miao Rong
Summary: This study develops a nondominated sorting genetic algorithm-II (NSGA-II) with Q-learning to address the disturbance factors in the distributed permutation flowshop problem. An iterated greedy algorithm (IG) is proposed to generate an initial solution, and the NSGA-II algorithm is designed to optimize dual-objective problems. The results confirm the high efficiency of the proposed algorithm in solving the rescheduling problem in the distributed permutation flowshop.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yu Du, Jun-qing Li, Xiao-long Chen, Pei-yong Duan, Quan-ke Pan
Summary: This study introduces a hybrid multi-objective optimization algorithm to solve a flexible job shop scheduling problem with time-of-use electricity price constraint, involving machine processing speed, setup time, idle time, and the transportation time between machines. The algorithm combines estimation of distribution algorithm and deep Q-network, with two knowledge-based initialization strategies designed for better performance.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Engineering, Civil
Dongdong Wang, Suying Pan, Jin Zhou, Quanke Pan, Zhonghua Miao, Jiangke Yang
Summary: This paper addresses the distributed event-triggered formation control problem of networked nonholonomic mobile robots (NNMRs) in a leader-follower-based framework. An event-triggered mechanism (ETM) is introduced for the design of the kinematic controller using an auxiliary reference vector and combined with backstepping technique and sliding mode approach to propose a unified integrated dynamic controller. The designed event-triggered condition is derived based on the local communication among robots utilizing the nonholonomic property of NNMRs, ensuring the exclusion of Zeno behavior before achieving the desired formation configuration. The theoretical results are validated through simulation analysis and implemented on a real-time physical NNMR experimental platform, demonstrating the key feature of the ETM integral formation scheme in effectively reducing communication resource usage and energy consumption while maintaining comparable performance to the conventional periodic communication mechanism (PCM).
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
