High-end equipment development task decomposition and scheme selection method

Xu, Xiaojun; Yang, Kun; Dou, Yajie; Zhou, Zhexuan; Chen, Ziyi; Yue-jin, Tan

doi:10.23919/jsee.2021.000012

Cited by 6 publications

(2 citation statements)

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“…(3) Real-time response of large-scale cases in a dynamic environment should be investigated to prove the superiority of Blockchain technology-based method over the centralized optimization methods. And more comparisons with other methods (e.g., PSO, game theory) should be made [43].…”

Section: Discussionmentioning

confidence: 99%

Towards Optimal Resources Allocation in Cloud Manufacturing: New Task Decomposition Strategy and Service Composition Model

Zhou

Guan

2022

Security and Communication Networks

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Service composition optimization is one of the core issues in cloud manufacturing research. However, all current studies of service composition in cloud manufacturing assume that tasks have been decomposed into subtasks, so they can be directly mapped to existing services. However, due to the complexity, diversity, and multilevel of services in cloud manufacturing, services have different granularity. Therefore, the matching between tasks and services does not always occur at the lowest level. For solving the problem of discontinuity between task decomposition and service composition, this paper considers the characteristics of existing services in the cloud pool and proposes a task decomposition strategy based on task/service matching on the basis of refining the description model of tasks and services. Then, for the decomposed subtask set, the E-CARGO model is used to model the optimal composition process of services, and CPLEX is used to solve the model. Practical cases show that the proposed task decomposition strategy can solve the problem of discontinuity between task decomposition and service composition without relying on more expert systems. In addition, the proposed service composition model is more flexible, can easily model more variable factors, and CPLEX can solve the model more quickly and stably.

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Section: Discussionmentioning

confidence: 99%

Towards Optimal Resources Allocation in Cloud Manufacturing: New Task Decomposition Strategy and Service Composition Model

Zhou

Guan

2022

Security and Communication Networks

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“…For the fuzzy MADM problem, various methods have been concerned, i.e., ordered weighted averaging (OWA) aggregation operator [5,6], probabilistic approach [7], fuzzy multi-criteria group decision making (FMCGDM) method [8,9], technique for order preference by similarity to ideal solution (TOPSIS) method [10][11][12], extended compromise ratio method [13], Bonferroni mean operator [14,15], multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method [16,17], Tomada de deciso interativa multicriterio (TODIM) method [18,19], vlsekriterijumska optimizacija I kompromisno resenje (VIKOR) method [20][21][22], triangular fuzzy number certitude degree (TFNCD) operator method [23], similarity programming model [24] and best-worst (BW) method [25], the superiority and inferiority ranking (SIR) method [26], and the hesitant fuzzy grey relational analysis (GRA) method [27]. However, the foregoing studies mostly focus on how to develop advanced MADM method without paying much attention to the density degree of attribute information distribution.…”

Section: Introductionmentioning

confidence: 99%

Method for Triangular Fuzzy Multiple Attribute Decision Making Based on Two-Dimensional Density Operator Method

Lin,

Li,

Liu

et al. 2024

J. of Syst. Eng. Electron.

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Aiming at the triangular fuzzy (TF) multi-attribute decision making (MADM) problem with a preference for the distribution density of attribute (DDA), a decision making method with TF number two-dimensional density (TFTD) operator is proposed based on the density operator theory for the decision maker (DM). Firstly, a simple TF vector clustering method is proposed, which considers the feature of TF number and the geometric distance of vectors. Secondly, the least deviation sum of squares method is used in the program model to obtain the density weight vector. Then, two TFTD operators are defined, and the MADM method based on the TFTD operator is proposed. Finally, a numerical example is given to illustrate the superiority of this method, which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.

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