A Fuzzy Simheuristic for the Permutation Flow Shop Problem under Stochastic and Fuzzy Uncertainty

Stochastic, as well fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper, we analyze the permutation flow shop problem (PFSP) with stochastic and processing times. The main goal is to find solution (permutation jobs) that minimizes expected makespan. However, due existence other characteristics are also taken into account. particular, illustrate how survival analysis employed enrich probabilistic information given decision-makers. To solve aforementioned problem, extend concept a simheuristic framework so it include elements. Hence, uncertainty incorporated PFSP. order test our approach, classical PFSP instances have been adapted extended, times become either or fuzzy. experimental results show effectiveness proposed approach when compared more traditional ones.