Interference probability matrix for disassembly sequence planning under uncertain interference

Laili, Yuanjun; Ye, Fei; Wang, Yongjing; Zhang, Lin

doi:10.1016/j.jmsy.2021.05.014

Cited by 13 publications

(6 citation statements)

References 47 publications

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“…Compared to complete disassembly, the goal of selective disassembly is to obtain specific details (such as high value, features that require repair, etc.) or immediately stop after achieving particular objectives (such as maximum profit) [7,8,9]. Suppose the complete disassembly sequence is similar to the reverse process of the assembly sequence, i.e., the reverse assembly sequence.…”

Section: Selective Disassemblymentioning

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)

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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.

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Section: Selective Disassemblymentioning

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)

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show abstract

“…In addition, in order to improve the recommendation efficiency of the algorithm, the batch processing module [18] is added. For 90000 training data in the experiment, it is divided into 9 batches and 10000 data are processed each time, which greatly reduces the amount of calculation of model training and the instability of model convergence caused by the calculation of each training data.…”

Section: Plos Onementioning

confidence: 99%

“…In Eq (17), μ represents the error correction coefficient. Meanwhile, improved Eq (11) is Eq (18) to calculate Perr. The values of parameters ξ 1 and ξ 2 can be obtained from cross validation experiments.…”

Section: Plos Onementioning

confidence: 99%

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

Mou

Zhang

2023

PLoS ONE

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Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied. The natural nearest neighbor of a few samples in the unbalanced data cumulative sequence is determined, and the few samples in the unbalanced data cumulative sequence are clustered according to the natural nearest neighbor relationship. In the same cluster, new samples are generated from the core points of dense regions and non core points of sparse regions, and then new samples are added to the original data accumulation sequence to balance the data accumulation sequence. The probability matrix decomposition method is used to generate two random number matrices with Gaussian distribution in the cumulative sequence of balanced data, and the linear combination of low dimensional eigenvectors is used to explain the preference of specific users for the data sequence; At the same time, from a global perspective, the AdaBoost idea is used to adaptively adjust the sample weight and optimize the probability matrix decomposition algorithm. Experimental results show that the algorithm can effectively generate new samples, improve the imbalance of data accumulation sequence, and obtain more accurate mining results. Optimizing global errors as well as more efficient single-sample errors. When the decomposition dimension is 5, the minimum RMSE is obtained. The proposed algorithm has good classification performance for the cumulative sequence of balanced data, and the average ranking of index F value, G mean and AUC is the best.

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“…The disassembly precedence between parts is represented as the disassembly priority graph owing to Han et al (2013) and Tseng et al (2019). Laili et al (2021) used the interference probability matrix to represent the disassembly interference between parts. The uncertainty of the interference condition is noted as a probability.…”

Section: Introductionmentioning

confidence: 99%

“…Disassembly is a significant step in product remanufacturing and recycling (Zhou et al , 2019). Disassembly sequence planning (DSP) is to find the optimal disassembly sequence of parts, which is one key technology for product remanufacturing (Laili et al , 2021). A good disassembly sequence is helpful to reduce the disassembly cost and improve the disassembly efficiency.…”

Section: Introductionmentioning

confidence: 99%

Selective disassembly sequence optimization based on the improved immune algorithm

Wang

2023

RIA

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Purpose The purpose of this paper is to improve the automation of selective disassembly sequence planning (SDSP) and generate the optimal or near-optimal disassembly sequences. Design/methodology/approach The disassembly constraints is automatically extracted from the computer-aided design (CAD) model of products and represented as disassembly constraint matrices for DSP. A new disassembly planning model is built for computing the optimal disassembly sequences. The immune algorithm (IA) is improved for finding the optimal or near-optimal disassembly sequences. Findings The workload for recognizing disassembly constraints is avoided for DSP. The disassembly constraints are useful for generating feasible and optimal solutions. The improved IA has the better performance than the genetic algorithm, IA and particle swarm optimization for DSP. Research limitations/implications All parts must have rigid bodies, flexible and soft parts are not considered. After the global coordinate system is given, every part is disassembled along one of the six disassembly directions –X, +X, –Y, +Y, –Z and +Z. All connections between the parts can be removed, and all parts can be disassembled. Originality/value The disassembly constraints are extracted from CAD model of products, which improves the automation of DSP. The disassembly model is useful for reducing the computation of generating the feasible and optimal disassembly sequences. The improved IA converges to the optimal disassembly sequence quickly.

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Interference probability matrix for disassembly sequence planning under uncertain interference

Cited by 13 publications

References 47 publications

Research on selective disassembly sequence planning based on graph model

Research on selective disassembly sequence planning based on graph model

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

Selective disassembly sequence optimization based on the improved immune algorithm

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