This paper aims at presenting a new decision framework under an interval-valued probabilistic hesitant fuzzy set (IVPHFS) context with fully unknown weight information. At first, the weights of the attributes are determined by using the interval-valued probabilistic hesitant deviation method. Later, the DMs’ weights are determined by using a recently proposed evidence theory-based Bayesian approximation method under the IVPHFS context. The preferences are aggregated by using a newly extended generalized Maclaurin symmetric mean operator under the IVPHFS context. Further, the alternatives are prioritized by using an interval-valued probabilistic hesitant complex proportional assessment method. From the proposed framework, the following significances are inferred; for example, it uses a generalized preference structure that provides ease and flexibility to the decision-makers (DMs) during preference elicitation; weights are calculated systematically to mitigate inaccuracies and subjective randomness; interrelationship among attributes are effectively captured; and alternatives are prioritized from different angles by properly considering the nature of the attributes. Finally, the applicability of the framework is validated by using green supplier selection for a leading bakery company, and from the comparison, it is observed that the framework is useful, practical and systematic for rational decision-making and robust and consistent from sensitivity analysis of weights and Spearman correlation of rank values, respectively.

本文旨在提出一种完全未知权重信息的区间值概率犹豫模糊集（IVPHFS）上下文下的新决策框架。首先，使用区间值概率犹豫偏差法确定属性的权重。后来，通过使用最近提出的基于证据理论的贝叶斯近似方法在IVPHFS上下文中确定DM的权重。通过在IVPHFS上下文中使用新扩展的广义Maclaurin对称均值算子来汇总首选项。此外，通过使用区间值概率犹豫复杂比例评估方法确定替代方案的优先级。从提出的框架中，可以推断出以下意义：例如，它采用的是广义偏好结构，其提供容易和灵活的决定-决策者（的DM）偏好诱导期间; 系统地计算权重以减轻误差和主观随机性；有效地捕获属性之间的相互关系；通过适当考虑属性的性质，从不同的角度对替代方案进行优先排序。最后，通过使用一家领先的面包店公司的绿色供应商选择来验证该框架的适用性，并且通过比较发现，该框架对于合理的决策是有用，实用和系统的，并且健壮且一致 分别来自权重的敏感性分析和等级值的Spearman相关性。