A Multi-objective Two-sided Disassembly Line Balancing Optimization Based on Artificial Bee Colony Algorithm: A Case Study of an Automotive Engine

Zhang, Lei; Wu, Yuanfeng; Zhao, Xiaohui; Pan, Shiwen; Li, Ziqi; Bao, Hong; Tian, Yongting

doi:10.1007/s40684-021-00394-9

Cited by 15 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the algorithm proposed in this article, a jack is used as an application example [11], and its assembly diagram is shown in Figure 2. This assembly drawing contains a total of 9 parts.…”

Section: Formatting Of Manuscript Componentsmentioning

confidence: 99%

Research on selective disassembly sequence planning based on graph model

Liu,

Lin,

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

In order to solve the disassembly plan of the target parts in the product with high efficiency, a disassembly hybrid graph model of the target parts is proposed and established based on the disassembly connection relationship and disassembly priority constraint relationship between the parts in the product. The disassembly sequence planning problem of the target parts is transformed into a search and optimization problem for the path with the optimal value in the graph model. At the same time, the sorting algorithm is used to solve the mixed graph model of the target part disassembly, finally, an example is given the feasibility of this method has been verified.

show abstract

Section: Formatting Of Manuscript Componentsmentioning

confidence: 99%

Research on selective disassembly sequence planning based on graph model

Liu,

Lin,

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

show abstract

“…ZMeta-heuristics are gradually applied for solving disassembly scheduling problems due to their excellent performance in balancing the computation cost and solution quality [28][29][30] , including particle swarm optimization (PSO) [31][32][33][34][35][36] , artificial bee colony (ABC) [37][38][39][40] , genetic algorithm (GA) [41][42][43][44][45][46] , and variable neighborhood search (VNS) [40,[47][48][49][50] . Kalayci et al propose a PSO based on variable neighborhood mutation operators to solve DLBPs [31] .…”

Section: Introductionmentioning

confidence: 99%

Integrating meta-heuristics and a Sarsa algorithm for disassembly scheduling problems with cycle time and hazard coefficients

Li,

Gao,

Ren

et al. 2024

Green Manuf Open

View full text Add to dashboard Cite

End-of-life products recycling can reduce the waste of resources, and disassembly line scheduling planning can effectively improve the recycling efficiency and reduce the pollution of the environment. This work addresses a bi-objective disassembly line scheduling problem with considering time interference between tasks. The weighted sum of the cycle time and hazard coefficients is optimized. First, a mathematical model of the disassembly line scheduling problem is established under the constraints of priority and time interference relationships. Second, four meta-heuristics are improved to solve the concerned problems, including particle swarm optimization, artificial bee colony, genetic algorithm and variable neighborhood search. Ten objective-oriented local search operations are designed for improving meta-heuristics’ performance. A reinforcement learning algorithm, Sarsa, is employed to guide task assignment among workstations and local search selection during iterations, respectively. Finally, experiments are carried out for 10 instances with different scales. The effectiveness of the improving strategies is verified; the meta-heuristics combined with Sarsa based task assignment and local search strategies has better robustness and stability than the classical ones. Comparisons and discussions show that the particle swarm optimization with improved strategies outperforms other algorithms.

show abstract