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Article
Automation & Control Systems
Lijun He et al.
Summary: This article studies an energy-efficient job-shop scheduling problem with sequence-dependent setup time, aiming to minimize the makespan, total tardiness, and total energy consumption. A novel fitness evaluation mechanism based on fuzzy relative entropy is developed to evaluate and select solutions for the multiobjective optimization problem. A hybrid multiobjective genetic algorithm is incorporated into the proposed framework to solve the problem.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Operations Research & Management Science
Guangchen Wang et al.
Summary: This paper proposes a multi-objective scheduling method based on a multi-objective whale swarm algorithm to optimize the energy efficiency of distributed welding flow shop. By solving the problem of allocating jobs among factories, scheduling jobs in each factory, and determining the number of machines for each job, the proposed method shows superior performance in real-life cases. The experimental results demonstrate the effectiveness of the proposed algorithm.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Lei Cao et al.
Summary: This paper investigates a business delivery model that involves professional vehicles and occasional passing-by vehicles. By proposing a multi-layer comprehensive model, a satisfactory solution is given from the perspective of platform owner, customers, professional drivers, occasional drivers, and authority. The experimental results of the new algorithm show that the proposed strategies are effective, and the new delivery model in this paper has some advantages over traditional models.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Jianhui Mou et al.
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
Computer Science, Artificial Intelligence
Leilei Meng et al.
Summary: This study addresses the problem of distributed hybrid flow shop scheduling with sequence-dependent setup times. Three novel mathematical models are proposed and compared with existing models, demonstrating their effectiveness. Experimental results show that the model based on sequence-based modeling performs the best, while the existing model performs the worst. Additionally, the constraint programming model is more efficient and effective than the mathematical models.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Fuqing Zhao et al.
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
Zixiao Pan et al.
Summary: This study focuses on energy-efficient fuzzy FJSP and proposes a bi-population evolutionary algorithm to optimize scheduling results. By handling uncertainty, dynamically adjusting population size, and using enhanced local search, the new method shows promising results in experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Industrial
Yingli Li et al.
Summary: This paper presents a mathematical model and a discrete artificial bee colony algorithm for optimizing the distributed hybrid flowshop scheduling problem with sequence dependent setup times. The proposed algorithm utilizes two-level encoding and effective solution update techniques, showing superior performance in experiments.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jingcao Cai et al.
Summary: In this paper, a cooperated shuffled frog-leaping algorithm (CSFLA) is proposed to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption for the distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP). Extensive experiments validate the promising advantages of CSFLA in solving DEHFSP.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Automation & Control Systems
Chao Lu et al.
Summary: This study introduces a novel energy-efficient scheduling approach for distributed flow shop machining, using a hybrid multiobjective optimization algorithm that outperformed other state-of-the-art algorithms in real-world experiments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Industrial
Qihao Liu et al.
Summary: With the development of globalization in economy and manufacturing industry, distributed manufacturing mode has become a hot topic in production research, aiming to efficiently complete processing tasks in different workshops. A novel network-based MILP model for distributed integrated process planning and scheduling, along with a discrete artificial bee colony algorithm (DABC) has been proposed to solve the DIPPS problem effectively.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Li Haoran et al.
Summary: With the development of globalization, distributed manufacturing has become a main mode of manufacturing. This paper addresses a distributed heterogeneous no-wait flowshop scheduling problem (DHNWFSP) and proposes a discrete artificial bee colony algorithm (DABC) to effectively solve it, considering the heterogeneity between factories in distributed flow-shop scheduling for the first time. The proposed DABC achieves the highest-quality solutions in comparison with state-of-art algorithms, as shown by numerical experiments for small and large-scale problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Enda Jiang et al.
Summary: This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks (EADHFSPMT) and proposes a Novel Multi-Objective Evolutionary Algorithm based on Decomposition (NMOEA/D) to solve it. The algorithm includes special designs such as decoding scheme, local intensification operators, and dynamic adjustment strategy for weight vectors, which have been proven effective through extensive computational experiments. The NMOEA/D has superior performances compared to existing algorithms, as verified by statistical comparisons.
TSINGHUA SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Guangchen Wang et al.
Summary: This study proposes a multi-objective evolutionary algorithm based on decomposition for energy-efficient scheduling of distributed heterogeneous welding flow shop, addressing three sub-problems and designing a mathematical model and algorithm for optimization. Comparative experiments demonstrate the effectiveness of the proposed algorithm.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiaoyuan Lian et al.
Summary: This paper investigates a steelmaking plant scheduling problem considering energy saving, proposing an improved multi-objective evolutionary algorithm and verifying the effectiveness of the model through numerical experiments. The results show a decrease in comprehensive energy consumption and production fluctuation, confirming that scheduling considering energy-related objectives can balance energy consumption and production while improving efficiency.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jingcao Cai et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Weishi Shao et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Jie Zheng et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Guangchen Wang et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2020)
Article
Computer Science, Artificial Intelligence
Yibing Li et al.
APPLIED SOFT COMPUTING
(2020)
Article
Operations Research & Management Science
Shuang Cai et al.
JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA
(2018)
Article
Engineering, Industrial
Kuo-Ching Ying et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2014)
Article
Computer Science, Interdisciplinary Applications
B. Naderi et al.
COMPUTERS & OPERATIONS RESEARCH
(2010)
Article
Management
R Ruiz et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2006)
Article
Computer Science, Interdisciplinary Applications
JCH Pan et al.
COMPUTERS & OPERATIONS RESEARCH
(2005)
