Multi-criteria Decision Making for Job Selection

Selection of job sectors for Industrial and Production Engineering (IPE) graduates has become an emerging aspect of study now-a-days due to its rapid diversity and expansion. Owing to the developing nature, an analytical study on various job sectors for IPE graduates has become very demanding and burning issue now. This study aims to conduct a comparative analysis among various potential job sectors to select the best one for IPE graduates on basis of different attributes associated with significant weights followed by clustering of the job sectors to perceive a fundamental understanding on their similarities. The job sectors were classified in three different levels such as Entry Level, Middle Level and Upper Level. Firstly, K-means clustering technique is adopted in order to cluster the job sectors. Based on the outcomes of three different levels, a generalized cluster analysis was concluded having the most reparability among various criteria denoting the attribute of the job sectors. The traditional “Elbow Method” was incorporated to justify the effectiveness of the cluster analysis. A prominent multi-criteria decision making (MCDM) techniques named the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is utilized to identify the best potential job sectors for IPE graduates. TOPSIS is implemented on three different types of job level separately to select the best options regarding different levels. The comparative analysis revealed some significant similarities and variations among the different levels of jobs. Overall, this study provided a better guidance to trace the best options to select different job sectors based on the acceptance of different fundamental criteria indicating different attribute of nature of the job sector.