  In the era of Industry 4.0, the focus is on the minimization of human element
and maximizing the automation in almost all the industrial and manufacturing
establishments. These establishments contain numerous processing systems, which
can execute a number of tasks, in parallel with minimum number of human beings.
This parallel execution of tasks is done in accordance to a scheduling policy.
However, the minimization of human element beyond a certain point is difficult.
In fact, the expertise and experience of a group of humans, called the experts,
becomes imminent to design a fruitful scheduling policy. The aim of the
scheduling policy is to achieve the optimal value of an objective, like
production time, cost, etc. In real-life situations, there are more often than
not, multiple objectives in any parallel processing scenario. Furthermore, the
experts generally provide their opinions, about various scheduling criteria
(pertaining to the scheduling policies) in linguistic terms or words. Word
semantics are best modeled using fuzzy sets (FSs). Thus, all these factors have
motivated us to model the parallel processing scenario as a multi-objective
linguistic optimization problem (MOLOP) and use the novel perceptual reasoning
(PR) based methodology for solving it. We have also compared the results of the
PR based solution methodology with those obtained from the 2-tuple based
solution methodology. PR based solution methodology offers three main
advantages viz., it generates unique recommendations, here the linguistic
recommendations match a codebook word, and also the word model comes before the
word. 2-tuple based solution methodology fails to give all these advantages.
Thus, we feel that our work is novel and will provide directions for the future
research.
