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<p>To enhance the scientific rigor of complex project justification and effectively coordinate project resources and capabilities, this paper proposes a group consensus decision-making method for probabilistic linguistic complex project schemes based on a cloud model and the Q-learning algorithm. First, a probabilistic linguistic term set (PLTS) is used to portray the information preference of decision makers (DMs). To solve the overly subjective problem of optimal and worst attribute selection of the Best–worst method (BWM) method, the Bayesian-BWM method is extended to PLTS to determine the attribute weights. At the same time, the consensus and hesitation degrees of decision-making subjects are integrated to determine the DM weights. The Q-learning algorithm is introduced to optimize the adjustment strategy of DMs' ratings, and the evaluation information of DMs is adjusted according to the reward function, so that the evaluation among DMs reaches a consensus. Then, based on PLTS and cloud model characteristic parameters, the ambiguity and uncertainty of decision-making information are quantified, and the cloud model of the complex project scheme is constructed. The scheme ranking is performed by the cloud similarity technique for order preference by similarity to ideal solution method. Finally, the effectiveness and feasibility of the method in this paper are verified with the case of the space engine development project selection to provide a reference for complex project justification work.</p>

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Group consensus decision method for probabilistic language complex project scheme based on cloud model and Q-learning algorithm

  • 期刊论文

  • 发表日期：

    2026年4月23日

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  • Jikai Wang,
  • Zhiran Qiu,
  • Yajie Dou,
  • Weijun Ouyang,
  • Kewei Yang,
  • Yuejin Tan

摘要

To enhance the scientific rigor of complex project justification and effectively coordinate project resources and capabilities, this paper proposes a group consensus decision-making method for probabilistic linguistic complex project schemes based on a cloud model and the Q-learning algorithm. First, a probabilistic linguistic term set (PLTS) is used to portray the information preference of decision makers (DMs). To solve the overly subjective problem of optimal and worst attribute selection of the Best–worst method (BWM) method, the Bayesian-BWM method is extended to PLTS to determine the attribute weights. At the same time, the consensus and hesitation degrees of decision-making subjects are integrated to determine the DM weights. The Q-learning algorithm is introduced to optimize the adjustment strategy of DMs' ratings, and the evaluation information of DMs is adjusted according to the reward function, so that the evaluation among DMs reaches a consensus. Then, based on PLTS and cloud model characteristic parameters, the ambiguity and uncertainty of decision-making information are quantified, and the cloud model of the complex project scheme is constructed. The scheme ranking is performed by the cloud similarity technique for order preference by similarity to ideal solution method. Finally, the effectiveness and feasibility of the method in this paper are verified with the case of the space engine development project selection to provide a reference for complex project justification work.