Selective disassembly sequence optimization based on the improved immune algorithm

Ji, Jiaqi; Wang, Yong

doi:10.1108/ria-06-2022-0156

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Then, the feasible solutions of the problem to be optimized are binary coded as antibodies, and an initial antibody population is randomly generated. The binary programming method can ensure the stability of the algorithm and the diversity of the population [29]. The affinity calculation of immune algorithm is shown in equation (7), where vw opt means the binding degree between antibody v and antigen w .…”

Section: B Design Of Ia-fdc Algorithm Optimizationmentioning

confidence: 99%

Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology

Liang

2024

IEEE Access

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With the continuous improvement of digitization, the processing and analysis of massive data has become one of the hot issues. Soft computing technology, as an emerging machine intelligence technology, performs well in handling complex uncertainty problems and is an important component of artificial intelligence. This study takes soft computing technology as the technical core and constructs a fuzzy dynamic clustering model based on improved immune algorithms to process unlabeled data. And an anomaly detection and analysis algorithm is designed based on soft instance transfer learning to handle weakly labeled data. The performance test outcomes denote that the accuracy, recall, and F1 values of the immune optimization fuzzy dynamic clustering algorithm are 91.69%, 89.27%, and 92.15%, respectively, reaching the optimal level of similar intelligent optimization clustering algorithms. The immune optimization fuzzy dynamic clustering algorithm has better computational efficiency, loss function curve performance, and strong global search ability, and avoids the occurrence of local optimal solutions. Compared with other advanced clustering algorithms, the immune optimization fuzzy dynamic clustering algorithm performs well on datasets in various fields, and both external and internal evaluation indicators verify the algorithm's clustering effect. The AUC value of the soft computing instance transfer learning anomaly detection algorithm is 0.913, with a detection accuracy of 91.67%, which is superior to other anomaly detection algorithms. The unlabeled and weak-label data processing model designed based on soft computing technology can effectively achieve the processing and analysis of real-world data problems.

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Section: B Design Of Ia-fdc Algorithm Optimizationmentioning

confidence: 99%

Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology

Liang

2024

IEEE Access

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“…Their optimization objectives include total disassembly time, disassembly direction change frequency, and disassembly tool change frequency, offering new directions for multi-objective optimization of DSP problems [19]. Ji et al [20] enhanced the immune optimization algorithm, providing new tools for rapidly finding optimal or near-optimal disassembly sequences to solve DSP problems, which are essential for accelerating the DSP process and improving production efficiency, providing strong support for the manufacturing industry [20].…”

Section: Research On Dspmentioning

confidence: 99%

“…Case study A: undominated solution set. ,3, 1,16,4,20,19,5,7,10,12,18,6,17,21,14,11,9,8, 15 562.9, 586.9, 611.9 115 17 7 13, 1, 16, 2, 3, 4, 19, 20, 10, 5, 7, 12, 18, 6, 17, 21, 14, 15, 11, 9, 8 565.2, 589.4, 614.7 114 18 8 13, 1, 16, 2, 3, 4, 5, 7, 19, 20, 10, 6, 12, 18, 17, 21, 14, 11, 9, 8, 15 566.4, 590.7, 616.2 111 19 9 13, 1, 2, 16, 3, 4, 5, 7, 10, 20, 6, 12, 18, 17, 21, 14, 15, 11, 9, 8, 19 569.9, 594.5, 620.5 107 21 10 13, 19, 1, 16, 2, 3, 4, 10, 5, 7, 12, 17, 18, 21, 14, 15, 11, 9, 8, 6, 20 573.3, 598.2, 624.6 105 22…”

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confidence: 99%

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

Fan,

Zhan,

Aljuaid

2023

Processes

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Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes.

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“…Chen et al proposed an optimization algorithm based on Q-learning to optimize the DSP problem of discarded smartphones [24]. Ji et al improved the immune algorithm to find the optimal or near-optimal disassembly sequence [25]. Kheder et al customized and enhanced the genetic algorithm, considering variations in component volume, tool change, and disassembly direction in their research [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Equipment Disassembly and Maintenance in an Uncertain Environment Based on a Peafowl Optimization Algorithm

Liu¹,

Zhan

Zheng³

et al. 2023

Processes

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Disassembly sequence planning (DSP) is a key approach for optimizing various industrial equipment-maintenance processes. Finding fast and effective DSP solutions plays an important role in improving maintenance efficiency and quality. However, when disassembling industrial equipment, there are many uncertainties that can have a detrimental impact on the disassembly and subsequent maintenance work. Therefore, this paper proposes a multi-objective DSP problem in an uncertain environment that addresses the uncertainties in the disassembly process through stochastic planning, with the objectives of minimizing disassembly time and enhancing responsiveness to priority maintenance components. Due to the complexity of the problem, an improved peafowl optimization algorithm (IPOA) is proposed for efficient problem-solving. The algorithm is specifically designed and incorporates four customized optimization mechanisms: peafowls’ courtship behavior, the adaptive behavior of female peafowls in proximity, the adaptive search behavior of peafowl chicks, and interactive behavior among male peafowls. These mechanisms enable effective search for optimal or near-optimal solutions. Through comparisons with a real-world industrial case and other advanced algorithms, the superiority of the IPOA in solving DSP problems is demonstrated. This research contributes to improving maintenance efficiency and quality, bringing positive impacts to industrial equipment maintenance.

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Selective disassembly sequence optimization based on the improved immune algorithm

Cited by 7 publications

References 32 publications

Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology

Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

Equipment Disassembly and Maintenance in an Uncertain Environment Based on a Peafowl Optimization Algorithm

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