Article
Computer Science, Interdisciplinary Applications
Yanwei Sang, Jianping Tan
Summary: The Ma-ODFJCSP is a significant problem that has not been addressed in the literature, with a large scheduling scale that is difficult to optimize and coordinate. The proposed solution involves a many-objective distributed flexible job shop model and a high-dimensional many-objective memetic algorithm (HMOMA).
COMPUTERS & INDUSTRIAL ENGINEERING
(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
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, Theory & Methods
Jianhui Mou, Peiyong Duan, Liang Gao, Xinhua Liu, Junqing Li
Summary: This paper introduces an energy-efficient distributed permutation flow-shop inverse scheduling problem and proposes an effective hybrid collaborative algorithm to meet dynamic market demand. By improving heuristic and random methods for population initialization, the algorithm's performance is enhanced. The algorithm achieves a balance between global exploration and local development capability through a double-population cooperative search link based on learning mechanism.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Engineering, Industrial
Jeffrey Schaller, Jorge M. S. Valente
Summary: This paper addresses the scheduling of jobs in a no-wait flow shop with the goal of minimizing total earliness and tardiness. Various dispatching heuristics and insertion improvement procedures are developed and tested, showing that the two-phase heuristics and insertion search improvement procedure can significantly improve performance.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Fernando Luis Rossi, Marcelo Seido Nagano
Summary: The distributed permutation flowshop scheduling problem (DPFSP) has been widely studied due to the complex production systems with mixed no-idle flowshops. Although the issue of identical factories with mixed no-idle flowshop environments has not been explored in literature, new solutions including MILP formulation, constructive heuristic, and iterated greedy algorithms have been proposed. Extensive experiments showed that the proposed methods outperformed existing approaches.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yuan-Zhen Li, Quan-Ke Pan, Jun-Qing Li, Liang Gao, M. Fatih Tasgetiren
Summary: This research focuses on distributed permutation flow shop scheduling problem with mixed no-idle constraints, using a mixed-integer linear programming model and an Adaptive Iterated Greedy algorithm with restart strategy. The algorithm shows excellent performance in large-scale experiments.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Lixin Cheng, Qiuhua Tang, Liping Zhang, Chunlong Yu
Summary: This article studies the scheduling problem of a flexible manufacturing cell with two production methods. By establishing a mixed integer linear programming model and a Q-learning-based genetic algorithm, it achieves a reasonable ordering and arrangement of standardized products and individualized products. Experimental results show that the proposed Q-GA algorithm outperforms other algorithms in terms of solution effectiveness and robustness.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Xiuli Wu, Zirun Xie
Summary: This study focuses on cold-drawn seamless steel tubes and aims to reduce logistics cost and improve production efficiency. A multi-objective optimization model is established and an improved evolutionary algorithm based on decomposition with local search is proposed. Experimental results demonstrate the effectiveness of this method in solving the scheduling problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Wenqiang Zhang, Huili Geng, Chen Li, Mitsuo Gen, Guohui Zhang, Miaolei Deng
Summary: Given the increasing severity of ecological issues, sustainable development and green manufacturing have emerged as crucial areas of research and practice. In this paper, a Q-learning-based multi-objective particle swarm optimization (QL-MoPSO) algorithm is proposed to address the distributed flow-shop scheduling problem (DFSP), aiming to minimize makespan and total energy consumption. The algorithm combines the advantages of Q-learning and particle swarm optimization to achieve faster convergence and better performance compared to traditional multi-objective evolutionary algorithms.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Automation & Control Systems
Fuqing Zhao, Ru Ma, Ling Wang
Summary: The study introduces a self-learning discrete Jaya algorithm to address the energy-efficient distributed no-idle flow-shop scheduling problem in a heterogeneous factory system.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Federico Della Croce, Andrea Grosso, Fabio Salassa
Summary: In this study, the authors address the two-machine total completion time flow shop problem with no-idle and no-wait constraints. They propose a matheuristic approach based on an ILP formulation and exploit the structural properties of the problem to achieve competitive performances on instances with up to 500 jobs.
JOURNAL OF HEURISTICS
(2021)
Article
Automation & Control Systems
Mustafa Avci
Summary: The distributed no-wait flowshop scheduling problem (DNWFSP) is a variant of the classical flowshop scheduling problem. An iterated local search (ILS) algorithm is proposed to solve the DNWFSP, which incorporates specialized local search and adaptively adjusted perturbation strength. The ILS is able to produce high-quality solutions in short computing times.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Fuqing Zhao, Zesong Xu, Ling Wang, Ningning Zhu, Tianpeng Xu, J. Jonrinaldi
Summary: This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP) and proposes a population-based iterated greedy algorithm (PBIGA) to address the problem. The PBIGA is shown to be effective and outperforms state-of-the-art algorithms in terms of minimizing total flowtime. Experimental results on large-scale benchmark instances demonstrate the superiority of the proposed PBIGA.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Ming Li, Bin Su, Deming Lei
Summary: The paper considers the fuzzy distributed assembly flow shop scheduling problem and proposes an algorithm optimized through imperialist cooperation. Experimental results demonstrate the excellent performance of the algorithm in solving the problem.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jing-fang Chen, Ling Wang, Shengyao Wang, Xing Wang, Hao Ren
Summary: This paper introduces an effective matching algorithm to solve online food delivery platform problems, which can improve delivery efficiency and customer satisfaction.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Aijuan Song, Guohua Wu, Witold Pedrycz, Ling Wang
Summary: This study investigates the problem domain knowledge of nonlinear equation systems (NESs) and proposes the incorporation of a variable reduction strategy (VRS) into evolutionary algorithms (EAs) to solve NESs. Experimental results show that with the assistance of VRS, EAs can produce better results than the original methods and other compared methods.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
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
Automation & Control Systems
Fuqing Zhao, Hui Zhang, Ling Wang, Ru Ma, Tianpeng Xu, Ningning Zhu, Jonrinaldi
Summary: An improved Jaya algorithm, named surrogate-assisted Jaya algorithm (SDH-Jaya), is proposed in this study to solve continuous optimization problems. The SDH-Jaya algorithm introduces a surrogate-assisted model to decrease computational simulations and accelerates convergence speed. The experimental results demonstrate that the SDH-Jaya algorithm outperforms other algorithms in terms of solution quality and execution time.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jun Dong, Wenyin Gong, Fei Ming, Ling Wang
Summary: One of the key issues in solving constrained multi-objective optimization problems is balancing convergence, diversity, and feasibility. This paper proposes a two-stage constrained multi-objective evolutionary algorithm with different emphases on the three indicators. Experimental results demonstrate that the proposed algorithm achieves significant improvements on most benchmark problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jie Zheng, Ling Wang, Shengyao Wang, Jing-fang Chen, Xing Wang, Haining Duan, Yile Liang, Xuetao Ding
Summary: Uncertainty in food delivery affects decision-making, efficiency, and customer satisfaction. A Gaussian mixture model is used to model uncertain service times, with a hybrid algorithm for clustering to optimize quality and simplicity. Various methods such as problem-specific encoding, initialization mechanism, and local intensification are employed to improve the algorithm's performance.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Xue-Lei Jing, Quan-Ke Pan, Liang Gao, Ling Wang
Summary: A new scheduling problem involving distributed permutation flowshop scheduling with uncertain processing times and carryover sequence-dependent setup time is addressed. A robust model is established with the makespan criterion, along with the discovery of a counter-intuitive paradox and acceleration methods. An iterated greedy algorithm is proposed to solve the problem, outperforming six competing algorithms in extensive experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yu-Jun Zheng, Xin Chen, Qin Song, Jun Yang, Ling Wang
Summary: Vaccination uptake is crucial for containing the COVID-19 pandemic. Efficient distribution of vaccines to inoculation spots is essential, and researchers have proposed a hybrid machine learning and evolutionary computation method to optimize the distribution process.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Wenhua Li, Xingyi Yao, Tao Zhang, Rui Wang, Ling Wang
Summary: This study proposes several benchmark problems with multiple local Pareto fronts and an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and local Pareto fronts. Experimental results show that HREA is competitive compared with other state-of-the-art MMEAs for solving the chosen benchmark problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Kaiwen Li, Tao Zhang, Rui Wang, Yuheng Wang, Yi Han, Ling Wang
Summary: This deep learning approach efficiently solves the covering salesman problem using reinforcement learning, generalizing to various task types with fast speed. Experimental results show that it outperforms traditional heuristic solvers significantly in terms of speed and training inferencing.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Fuqing Zhao, Ru Ma, Ling Wang
Summary: The study introduces a self-learning discrete Jaya algorithm to address the energy-efficient distributed no-idle flow-shop scheduling problem in a heterogeneous factory system.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jie Zheng, Ling Wang, Li Wang, Shengyao Wang, Jing-Fang Chen, Xing Wang
Summary: This article addresses a problem in online food delivery with stochastic food preparation time and proposes an iterated greedy algorithm to solve it. The algorithm includes a filtration mechanism to cope with massive demands, a risk-measuring criterion to reduce uncertainty, and two time-saving strategies for timeliness requirements. Real-world experiments demonstrate the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Huixiang Zhen, Wenyin Gong, Ling Wang, Fei Ming, Zuowen Liao
Summary: This article proposes a novel two-stage data-driven evolutionary optimization (TS-DDEO) method that uses surrogate models and different evolutionary sampling strategies to achieve better optimization performance and robustness in solving complex and computationally expensive optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Fei Ming, Wenyin Gong, Ling Wang, Liang Gao
Summary: This article introduces a novel constrained many-objective optimization evolutionary algorithm (CMME), which achieves a balance between feasibility, convergence, and diversity in constrained many-objective optimization problems by improving mating and environmental selections, proposing novel ranking strategies, designing individual density estimation, and using θ-dominance to enhance selection pressure on both convergence and diversity.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Zixiao Pan, Ling Wang, Jingjing Wang, Jiawen Lu
Summary: This paper proposes an optimization algorithm based on deep reinforcement learning for solving permutation flow-shop scheduling problem. By designing a new deep neural network and using reinforcement learning methods, the algorithm achieves better results than existing heuristics in similar computational time.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
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)
