Optimization of high-end equipment development task process influenced by multiple uncertainty factors

ZHANG Xilin, TAN Yuejin, YANG Zhiwei

Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (3) : 725-734.

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Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (3) : 725-734. DOI: 10.12011/1000-6788-2017-1698-10

Optimization of high-end equipment development task process influenced by multiple uncertainty factors

  • ZHANG Xilin1,2, TAN Yuejin1, YANG Zhiwei1
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Abstract

The simulation model of high-end equipment development task is established based on design structure matrix, and Monte Carlo method is used to simulate the execution process of the development task. The simulation outputs are used to estimate the duration, cost, failure rate and other parameters of the development task. The development task simulation is embedded into multi-objective optimization algorithm, and each individual corresponding development task process is simulated many times. The average duration, cost and failure rate of simulation outputs are used as fitness evaluation indexes. Multi-objective optimization algorithm is constructed based on non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ), the Pareto optimal solution set is obtained by running optimization algorithm. More simulations for the development task process of each Pareto optimal solution are carried out, and the development task processes are deeply analyzed and evaluated. Finally, an uninhabited combat aerial vehicle development task is taken as an example to study the application, and the optimization result of this paper are compared with the data in the literature. The effectiveness and superiority of the proposed method are verified.

Key words

high-end equipment development task / process optimization / design structure matrix / multi-objective optimization / NSGA-III

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ZHANG Xilin , TAN Yuejin , YANG Zhiwei. Optimization of high-end equipment development task process influenced by multiple uncertainty factors. Systems Engineering - Theory & Practice, 2019, 39(3): 725-734 https://doi.org/10.12011/1000-6788-2017-1698-10

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Funding

National Natural Science Foundation of China (71690233, 71671186, 71501182, 71571185)
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