Digital twin-oriented collaborative optimization of fuzzy flexible job shop scheduling under multiple uncertainties

Chen, Zhaoming; Zou, Jinsong; Wang, Wei

doi:10.1007/s12046-023-02133-z

Cited by 4 publications

(3 citation statements)

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“…To achieve this, we initiate the process by conducting parameter calibration. Drawing from experience, preliminary experiments, and literature analysis [3,6,8,23], we designate three reference values for each parameter, as illustrated in Table 2. Conducting a full experiment would necessitate a daunting number of trials, given the exponential nature of the parameter combinations.…”

Section: Parameter Calibrationmentioning

confidence: 99%

“…In practical settings, industrial operations often entail uncertainties such as intelligent scheduling of power systems [5,6], equipment assembly [7], and job shop scheduling [8]. When extending these uncertainty factors to the equipment maintenance disassembly problem we propose, the complexities arise from intricate equipment structures, various maintenance components, differing maintenance conditions, and equipment wear, all contributing to the dynamic nature of the disassembly task.…”

Section: Introductionmentioning

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%

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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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Section: Parameter Calibrationmentioning

confidence: 99%