Asghar Ali, Khan Khuram A, Albahar Marwan A, Alammari Abdullah
Department of Mathematics, University of Sargodha, Sargodha, Sargodha, Pakistan.
Computer Science Department, Umm Al-Qura University, Mecca, Saudia Arabia.
PeerJ Comput Sci. 2023 Aug 30;9:e1540. doi: 10.7717/peerj-cs.1540. eCollection 2023.
Supplier selection is a critical decision-making process for any organization, as it directly impacts the quality, cost, and reliability of its products and services. However, the supplier selection problem can become highly complex due to the uncertainties and vagueness associated with it. To overcome these complexities, multi-criteria decision analysis, and fuzzy logic have been used to incorporate uncertainties and vagueness into the supplier selection process. These techniques can help organizations make informed decisions and mitigate the risks associated with supplier selection. In this article, a complex picture fuzzy soft set (cpFSS), a generalized fuzzy set-like structure, is developed to deal with information-based uncertainties involved in the supplier selection process. It can maintain the expected information-based periodicity by introducing amplitude and phase terms. The amplitude term is meant for fuzzy membership, and the phase term is for managing its periodicity within the complex plane. The cpFSS also facilitates the decision-makers by allowing them the opportunity to provide their neutral grade-based opinions for objects under observation. Firstly, the essential notions and set-theoretic operations of cpFSS are investigated and illustrated with examples. Secondly, a MADM-based algorithm is proposed by describing new matrix-based aggregations of cpFSS like the core matrix, maximum and minimum decision value matrices, and score. Lastly, the proposed algorithm is implemented in real-world applications with the aim of selecting a suitable supplier for the provision of required materials for construction projects. With the sensitivity analysis of score values through Pythagorean means, it can be concluded that the results and rankings of the suppliers are consistent. Moreover, through structural comparison, the proposed structure is proven to be more flexible and reliable as compared to existing fuzzy set-like structures.
供应商选择对于任何组织来说都是一个关键的决策过程，因为它直接影响其产品和服务的质量、成本及可靠性。然而，由于与之相关的不确定性和模糊性，供应商选择问题可能会变得高度复杂。为了克服这些复杂性，多准则决策分析和模糊逻辑已被用于将不确定性和模糊性纳入供应商选择过程。这些技术可以帮助组织做出明智的决策，并降低与供应商选择相关的风险。在本文中，一种复杂图像模糊软集（cpFSS），一种广义的类似模糊集的结构，被开发出来以处理供应商选择过程中基于信息的不确定性。它可以通过引入幅度和相位项来保持预期的基于信息的周期性。幅度项用于模糊隶属度，相位项用于在复平面内管理其周期性。cpFSS还通过让决策者有机会为观察对象提供基于中性等级的意见来为他们提供便利。首先，研究了cpFSS的基本概念和集合论运算，并用示例进行了说明。其次，通过描述cpFSS基于新矩阵的聚合，如核心矩阵、最大和最小决策值矩阵以及得分，提出了一种基于多属性决策的算法。最后，将所提出的算法应用于实际应用中，旨在为建筑项目选择合适的供应商以提供所需材料。通过毕达哥拉斯均值对得分值进行敏感性分析，可以得出供应商的结果和排名是一致的。此外，通过结构比较，证明所提出的结构与现有的类似模糊集的结构相比更加灵活和可靠。
