A novel decision algorithm for the innovation and optimization in university labor education courses using fuzzy information involving multiple experts.

The labor of any organization, especially in universities, plays a significant role in its progress. Several organizations plan to train their labor through training programs, workshops, and courses. However, innovation and optimization in the training programs and labor courses are necessary with technological and digitalization changes. However, there are several complexities in designing, selecting, and proposing any course to enhance innovation in labor education because several workers have different mindsets and skill sets. Targeting workers with various mindsets and skill sets with a single course is uncertain and may cause poor results. The existing assessment models for selecting and optimizing the courses cannot assess the courses for workers with different skill sets. Moreover, they cannot evaluate the course based on various uncertain factors. Therefore, a decision algorithm is utilized to cope with these challenges. The decision algorithm can assess the courses based on multiple uncertain factors and optimize the courses for the university labor to obtain maximum output. The decision-makers or organizations can optimize their courses once the utilized decision algorithm provides an ordering of the courses based on considered factors.