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
Engineering, Industrial
Thiago Cantos Lopes, Adalberto Sato Michels, Nadia Brauner, Leandro Magatao
Summary: This paper focuses on optimizing Mixed-model assembly lines with continuous paced line control and proposes a criterion-space method for defining the Pareto front. Comparing the Pareto fronts between cycle time and line length for paced and unpaced lines allows meaningful comparisons between line controls. An industrial case study suggests that paced lines are more efficient than unpaced lines for lower cycle time ranges.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Junhao Chen, Xiaoliang Jia, Qixuan He
Summary: This study proposes a novel bi-level multi-objective genetic algorithm to solve the integrated problem of assembly line balancing and part feeding. The algorithm outperforms traditional methods in terms of approximation and computational efficiency.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
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
Multidisciplinary Sciences
Amy H. I. Lee, He-Yau Kang, Chong-Lin Chen
Summary: The study considers four objectives and uses a fuzzy multi-objective linear programming model and a genetic algorithm model to solve the assembly line balancing problem. In practical cases, the models can efficiently solve small-scale problems, while the genetic algorithm can obtain good solutions for large-scale problems in a short time.
Article
Computer Science, Interdisciplinary Applications
Lue Tao, Yun Dong, Weihua Chen, Yang Yang, Lijie Su, Qingxin Guo, Gongshu Wang
Summary: This study addresses a new variant of the assembly line feeding problem in automobile manufacturing, proposing a novel mathematical model and algorithm that achieve superior cost savings, solution quality, and convergence efficiency while providing decision support for managers.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
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
Computer Science, Artificial Intelligence
Z. H. Che, Tzu-An Chiang, Tzu-Ting Lin
Summary: This study focuses on the supplier selection problem in multiple assembly plants producing multiple products, using a multi-objective algorithm to find optimal supplier combinations and production resource allocation. The new algorithm W-NSGA2 incorporates task time and quantity mechanisms in the initial solution generation, improving efficiency and outperforming existing algorithms in office furniture assembly tasks.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Beikun Zhang, Liyun Xu, Jian Zhang
Summary: This paper focuses on the mixed-model U-shaped robotic assembly line balancing and sequencing problem, proposing a hybrid multi-objective dragonfly algorithm to address energy consumption and efficient production. The results suggest that the proposed HMODA is more efficient than compared algorithms in solving the problem.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
F. Tanhaie, M. Rabbani, N. Manavizadeh
Summary: This study investigates the use of mixed-model assembly line (MMAL) technology to address make-to-order (MTO) challenges, achieving superior performance through a multi-objective particle swarm optimization (MOPSO) algorithm.
ENGINEERING OPTIMIZATION
(2021)
Article
Computer Science, Interdisciplinary Applications
S. Li, J. Butterfield, A. Murphy
Summary: The aim of this work is to develop a self-adapting digital toolset for manufacturing planning that focuses on minimally constrained assembly line balancing. The approach involves determining the optimal number of workstations, cycle time, and task assignments through a bespoke genetic algorithm. The proposed algorithm consistently outperforms previous studies in terms of convergence time and solution quality, delivering detailed production plans for the simple assembly line balancing problem with minimal inputs.
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
(2023)
Article
Engineering, Industrial
Zikai Zhang, Qiuhua Tang, Dayong Han, Xinbo Qian
Summary: This paper proposes multiple alternative assignment plans with interchangeable abilities to improve production continuity during preventive maintenance. A mixed-integer mathematical model is formulated to minimize cycle time and total assignment plan alteration cost. An enhanced JAYA algorithm is developed to effectively and efficiently obtain well-distributed Pareto frontier solutions.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
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
Engineering, Industrial
Ge Guo, Sarah M. Ryan
Summary: This paper tackles the uncertainty factors in sequencing decisions in mixed-model assembly lines by modeling unreliable part delivery and quality. It utilizes stochastic optimization to find sequencing policies that can improve on-time performance, and also introduces a risk-averse program to protect against worst-case scenarios chosen by decision makers. Computational studies demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method using Progressive Hedging.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Information Systems
Jiang Yan, Wang Daobo, Bai Tingting, Yan Zongyuan
Summary: In this paper, a method is proposed to evaluate the situation at a certain time in the background of air combat, providing a basis for establishing a multi-UAV objective assignment model. The Hungarian fusion Genetic Algorithm is introduced to solve the model, optimizing the drawbacks of the traditional algorithm. The simulation verifies the effectiveness of the proposed method and algorithm.