Human Intuitionistic Data-Based Employee Performance Evaluation With Similarity Measure Using Lattice Ordered Picture Fuzzy Hypersoft Sets

Abstract

Performance evaluation is a critical process in organizations as it provides valuable insights into employee productivity, identifies areas of improvement, facilitates fair reward systems, and ultimately contributes to the overall growth and success of the company. Most evaluations are based on human intuitionistic data, and performance attributes are divided into sub-attributes for a fair and detailed evaluation. For handling attributes at a sub-attributic level, we introduce a novel lattice-ordered picture fuzzy hypersoft set ( $\mathbb {LO}^{\mathbb {PF}}_{\mathbb {HSS}}$ ), which provides a more valuable structure for certain decision-making problems where uncertainty associated with picture fuzzy sets and the ordering among parameters is crucial. The utilization of $\mathbb {LO}^{\mathbb {PF}}_{\mathbb {HSS}}$ can enhance decision-making processes by introducing a systematic and ordered representation of parameters. For a detailed illustration of the designed structure, basic operations are defined, which are then used to develop an employee performance evaluation system that incorporates information in the form of membership degree (MD), non-membership degree (NMD), and abstinence degree (AD) while also addressing the issue of parametric ordering. The structure offers great flexibility and versatility in addressing decision-making problems commonly arising in human resource management, as most data is based on human intuition.

Keywords

• Hypersoft set
• picture fuzzy hypersoft set
• decision making
• lattice ordered set
• lattice ordered picture fuzzy hypersoft set
• similarity measure