Article
Engineering, Industrial
Binghai Zhou, Zhaoxu He
Summary: This paper focuses on a sustainable material handling scheduling problem in the automobile industry, and proposes a novel Hybrid-load Automated Guided Vehicle (H-AGV) to minimize inventory and energy consumption. A Deep Q network and Non-dominated sorting-based Hyper-Heuristic (DN-HH) algorithm is used to solve the bi-objective scheduling problem and outperforms other algorithms.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Xuemei Liu, Xiaolang Yang, Mingliang Lei
Summary: This study utilized uncertainty theory and complexity theory to consider uncertain demand in mixed-model assembly line balancing. By introducing scenario probability and triangular fuzzy number to describe uncertain demand, and measuring station complexity based on information entropy and fuzzy entropy, a new optimization model was established. An improved genetic algorithm was applied to solve the model, and the effectiveness of the model was verified on instances of mixed-model assembly line for automobile engines.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Binghai Zhou, Mingda Wen
Summary: This paper proposes a dynamic scheduling problem for automotive assembly lines considering material-handling mistakes. A multi-phase dynamic scheduling algorithm is proposed to optimize the material distribution scheduling problem. Through comparative experiments, the superiority of the proposed dynamic scheduling strategy and algorithm is verified.
ENGINEERING COMPUTATIONS
(2023)
Article
Engineering, Manufacturing
Yilmaz Delice, Emel Kizilkaya Aydogan, Salih Himmetoglu, Ugur Ozcan
Summary: In the automotive industry, supermarkets are decentralized in-house logistic areas used for parts feeding to mixed-model assembly lines. This study simultaneously considers the mixed-model assembly line balancing problem and supermarket location problem in order to minimize the total costs. A mathematical model is developed and solved using constraint programming, and an approach based on Ant Colony Optimization and Simulated Annealing is presented for large-sized problems. The proposed approach effectively reduces total costs and achieves a more realistic and applicable structure.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Stefan Bock, Nils Boysen
Summary: This study focuses on real-time launch control in mixed-model assembly lines, proposing an integrated solution and demonstrating its superiority over alternative launching approaches in a simulation study. The integrated approach not only improves production efficiency but also reduces safety stocks of parts within workstations.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Mathematics, Applied
Yuchen Li, Dan Liu, Ibrahim Kucukkoc
Summary: This paper studies the mixed-model assembly line balancing problem, considering the impact of learning effect and uncertain demand on the level of production. A novel model is proposed to optimize the total expected cost and average cycle time, and two algorithms are proposed to solve the model under different system response time requirements.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Energy & Fuels
Iwona Paprocka, Damian Krenczyk
Summary: Mixed and multi-model assembly line sequencing problems are more practical than single-product models, and the Grey Wolf Optimizer (GWO) performs effectively in solving these problems.
Article
Energy & Fuels
Lingxiang Yun, Lin Li, Shuaiyin Ma
Summary: Electricity demand response is an effective method to address peak demand and improve power grid reliability. Implementing demand response in the manufacturing sector has received significant attention. The proposed study aims to develop an analytical model for cost-effective production scheduling under demand response, taking into account the interactions between machines and MHE, production throughput requirements, battery charging characteristics, and time-varying electricity pricing. The results show a 15.1% energy cost reduction compared to the benchmark scheduling scheme.
Article
Thermodynamics
Zhimin Jiang, Philani Hlanze, Jie Cai
Summary: This paper presents a control strategy based on mixed-integer convex programming to optimize the operation of phase change material-based energy storage in building supply ducts. Two model predictive control strategies are developed to achieve energy cost and demand charge savings.
APPLIED THERMAL ENGINEERING
(2022)
Article
Engineering, Industrial
Kang Wang, Qianqian Han, Zhenping Li
Summary: This research investigates the mixed-model assembly line balancing problem in multi-demand scenarios and proposes a solution through a phased heuristic algorithm. The results show that considering demand fluctuations can improve workstation load balance and assembly line production efficiency.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shi-Gen Liao, Yi-Bo Zhang, Chun-Yan Sang, Hui Liu
Summary: This study models and solves the mixed-model two-sided assembly line balancing and sequencing problem with unpaced synchronous transfer using a genetic algorithm. A mathematical model is established for the balancing and sequencing problem, with a focus on Type-II balancing. An improved genetic algorithm is proposed, which utilizes a combination and evaluation mechanism to effectively solve the problem. Experimental results demonstrate the effectiveness and superior performance of the proposed algorithm.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Zikai Zhang, Qiuhua Tang, Manuel Chica
Summary: This study addresses the mixed-model multi-manned assembly line balancing under uncertain demand conditions, optimizing the line configuration using a robust MILP model and solution generation mechanisms. Two solution generation mechanisms are designed, with the GEP method effectively improving solution efficiency.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Industrial
Adele Louis, Gulgun Alpan, Bernard Penz, Alain Benichou
Summary: In the automotive industry, sequencing cars on assembly lines is a major challenge in production scheduling. Two specific models, Mixed-Model Sequencing (MMS) and Car Sequencing (CS), have gained significant attention. Comparison between the two models shows that MMS generates more feasible sequences than CS. However, real-life production schedulers using specific sequencing rules may result in a higher number of distinct feasible sequences for CS.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Kai Meng, Qiuhua Tang, Zikai Zhang, Zixiang Li
Summary: This study designs a robust mathematical model for solving robust mixed-model assembly line balancing and sequencing problems (RMALBSP) considering preventive maintenance scenarios (PMS), and develops a multi-objective cooperative differential evolution algorithm (MOCDE) to solve large-scale instances. The experimental results demonstrate the effectiveness of the proposed model and algorithm.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Management
Nico Andre Schmid, Veronique Limere, Birger Raa
Summary: This study proposes a new mathematical programming model to address the issue of feeding parts in assembly systems, investigating the selection of feeding policies and space allocation at assembly stations. Key findings include the factors influencing these decisions and overall costs.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yu Zheng, Sen Yang, Huanchong Cheng
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2019)
Article
Engineering, Multidisciplinary
Liang Chen, Yu Zheng, Juntong Xi, Shaoyang Li
RESEARCH IN ENGINEERING DESIGN
(2020)
Article
Computer Science, Artificial Intelligence
Junzheng Li, Yu Zheng, Zhiyuan You, Xinyi Le
Summary: This paper proposes a novel approach to generate high-quality industrial defect images from extremely few samples using a generative adversarial network (GAN). The problem of mode collapse in GANs when training data is insufficient is addressed by applying a fully convolutional network and Markovian discriminator. A novel sampling strategy is proposed to balance memory consumption and training quality for generating high-resolution images. Experiments show that the model is capable of generating high-resolution industrial defect images and improving the accuracy of classification and detection models compared to the original rare samples. Thus, this approach can increase the intelligence of defect inspection systems.
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
(2022)
Article
Engineering, Electrical & Electronic
Junzheng Li, Jieji Ren, Mingjun Ren, Yu Zheng, Xinyi Le
Summary: This article presents an efficient machining error evaluation approach for complex surfaces based on the neural process, improving measurement efficiency through sparse sampling and reconstruction, and enhancing sampling efficiency by introducing adaptive sampling.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Information Systems
Wenqiang Yang, Yu Zheng, Shaoyang Li
Summary: With the development of new information technologies, the era of space intelligence is gradually coming, and spacecraft are facing more tests and challenges in complex environments. Digital twin technology provides a systematic approach for operation and management, playing an important role in spacecraft.
Article
Computer Science, Interdisciplinary Applications
Xiaolin Wang, Liyi Zhan, Yong Zhang, Teng Fei, Ming-Lang Tseng
Summary: This study proposes an environmental cold chain logistics distribution center location model to reduce transportation costs and carbon emissions. It also introduces a hybrid arithmetic whale optimization algorithm to overcome the limitations of the conventional algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hong-yu Liu, Shou-feng Ji, Yuan-yuan Ji
Summary: This study proposes an architecture that utilizes Ethereum to investigate the production-inventory-delivery problem in Physical Internet (PI), and develops an iterative heuristic algorithm that outperforms other algorithms. However, due to gas prices and consumption, blockchain technology may not always be the optimal solution.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Paraskevi Th. Zacharia, Elias K. Xidias, Andreas C. Nearchou
Summary: This article discusses the assembly line balancing problem in production lines with collaborative robots. Collaborative robots have the potential to improve automation, productivity, accuracy, and flexibility in manufacturing. The article explores the use of a problem-specific metaheuristic to solve this complex problem under uncertainty.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
