Article
Computer Science, Artificial Intelligence
Zhi-zhong Zeng, Zhi-peng Lue, Xin-guo Yu, Qing-hua Wu, Yang Wang, Zhou Zhou
Summary: This paper proposes a new learning method called post-flip edge-state learning (PF-ESL) for the max-cut problem. Unlike previous algorithms, PF-ESL focuses on edge-states as the critical information and extracts their statistics for learning. Experimental results show that PF-ESL is competitive and provides value-added learning for both the EDA perturbation operator and the path-relinking operator. The paper also introduces a new perspective on edge-states, which can inspire future research in learning-based algorithms and graph partitioning problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chu-ge Wu, Ling Wang, Jing-jing Wang
Summary: This paper proposes a task scheduling scheme based on EDA and path relinking, which can effectively improve the performance of task completion time in multi-processor computing systems. By establishing probability models and designing local search methods, the relative position relationships between tasks can be described and the utilization of the algorithm can be improved.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jia-Yang Mao, Quan-Ke Pan, Zhong-Hua Miao, Liang Gao, Shuai Chen
Summary: This paper studies the distributed permutation flowshop scheduling problem with preventive maintenance. It proposes a hash map-based algorithm and demonstrates its effectiveness in optimizing computational efficiency.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Qihao Liu, Xinyu Li, Liang Gao, Guangchen Wang
Summary: This paper studies the multiobjective distributed integrated process planning and scheduling problem, establishing a mixed-integer linear programming model and proposing a new encoding method based on the process network graph and a multiobjective memetic algorithm to solve the problem. The algorithm introduces a simulated annealing mechanism to avoid falling into a local optimum.
Article
Computer Science, Artificial Intelligence
Zi-Qi Zhang, Rong Hu, Bin Qian, Huai-Ping Jin, Ling Wang, Jian-Bo Yang
Summary: In this paper, a matrix-cube-based estimation of distribution algorithm (MCEDA) is proposed to solve the energy-efficient distributed assembly permutation flow-shop scheduling problem (EE_DAPFSP). The proposed algorithm constructs a high-quality and diverse initial population, designs a matrix-cube-based probabilistic model and its update mechanism, develops a suitable sampling strategy, provides a problem-dependent neighborhood search, and embeds speed adjustment strategies based on problem properties, which improve the quality and efficiency of the obtained solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
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, Information Systems
Xiaohui Zhang, Yuyan Han, Grzegorz Krolczyk, Marek Rydel, Rafal Stanislawski, Zhixiong Li
Summary: This study explores the dynamic scheduling problem in distributed manufacturing systems and proposes a rescheduling framework to address production disruptions caused by random machine breakdowns. By establishing a mathematical model and adopting an event-driven policy, a two-stage predictive-reactive method is proposed for dynamic scheduling optimization.
Article
Computer Science, Interdisciplinary Applications
Xueli Yan, Xingsheng Gu
Summary: This paper proposes an algorithm to solve the multi-product multi-stage production scheduling problem by combining improved differential evolution algorithm and memetic algorithm, effectively improving the scheduling performance.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Automation & Control Systems
Jing-jing Wang, Ling Wang, Xia Xiu
Summary: Facing the challenges of globalization and sustainable industrial development, this paper addresses the energy-aware distributed welding shop scheduling problem (EADWSP) with the aim of minimizing both makespan and total energy consumption. A mathematical model and a cooperative memetic algorithm (CMA) are proposed to tackle the large scale and multiple objective characteristics of the problem. Various specific designs, such as hybrid initialization, cooperative search based on feedback, cooperative selection strategy, problem-specific operators, and local intensification with Q-learning, are introduced to enhance the algorithm's efficiency and effectiveness. Numerical experiments and comparisons with existing algorithms demonstrate the superiority of the proposed CMA, and a real-life case study further verifies its practicality in solving the EADWSP.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Jing-jing Wang, Ling Wang
Summary: This paper addresses the energy-aware distributed flow-shop with flexible assembly scheduling problem and proposes a cooperative memetic algorithm with feedback to optimize global supply chains. The algorithm utilizes problem-specific heuristics, a cooperative search with feedback mechanism, local intensification, and multiple selection strategies to balance exploration and exploitation. The results demonstrate the effectiveness of the proposed algorithm in solving the problem, outperforming existing algorithms.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Mathematics
Cristina Sorina Stangaciu, Eugenia Ana Capota, Valentin Stangaciu, Mihai Victor Micea, Daniel Ioan Curiac
Summary: This paper introduces the concept of the Mixed-Criticality Internet of Things and presents a mathematical model and methodology based on real-time scheduling. The study also offers a model for setting task parameters and evaluates the effectiveness of the task scheduling algorithm.
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
Zi-Qi Zhang, Bin Qian, Rong Hu, Huai-Ping Jin, Ling Wang
Summary: This paper introduces an innovative three-dimensional matrix-cube-based estimation algorithm to solve the DAPFSP problem, which improves computational efficiency through global exploration and local exploitation, achieving significantly better results than existing algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Nan Zhu, Guiliang Gong, Dian Lu, Dan Huang, Ningtao Peng, Hao Qi
Summary: This research proposes a solution to the distributed flexible job shop scheduling problem considering order cancellation for the first time. The reformative memetic algorithm designed in this work shows outstanding performance in reducing resource waste.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yu Du, Jun-qing Li, Chao Luo, Lei-lei Meng
Summary: This study proposed a hybrid algorithm to solve the distributed flexible job shop scheduling problem efficiently by combining EDA and VNS, achieving better performance.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
