An integrated parallel big data decision support tool using the W-CLUS-MCDA: A multi-scenario personnel assessment

Abstract

One of the most primary issues that organizations have to deal with is incorporating massive structured data problems, simultaneously. Additionally, a vital division in any organization is the department of human resources (HR), which is in charge of the recruitment and personnel selection procedures. Due to the nature of the personnel assessment problems, which include multiple candidates as alternatives along with various complex evaluating criteria, these types of problems can be tackled by the aid of multi-attribute decision making (MADM) techniques. Moreover, in mega-structured organizations, the procedure of personnel selection contains massive structures of data due to the number of potential candidates for job positions in various sub-divisions and departments. Therefore, the personnel selection problem in such environments can be subjected as a big data problem which should be handled prudently to save time and cost. The main objective of the current study is to extend the CLUS-MCDA approach (CLUSter analysis for improving Multiple Criteria Decision Analysis) and integrate it with the Best–Worst Method (BWM) and a specific structure to solve multi-scenario big data decision-making problems. In this study, to validate the practicality and reliability of the W-CLUS-MCDA approach, multiple personnel selection and risk assessment problems have been investigated with various scenarios within several departments, simultaneously. This study has also introduced the concept of multi-scenario parallel decision making (PDM) within the context of MADM methodology using a data-driven decision-making approach solving various big data problems.