Enhanced non-dominated sorting genetic algorithms for fuzzy multi-objective multi-route shortest path problem

This study examines a fuzzy multi-objective multi-route short path problem (FMOMRSPP) by transforming it into a crisp multi-objective multi-route shortest path problem (MOMRSPP) using possibility distribution. Using α-level sets, fuzzy judgement is categorised for the decision maker (DM) to optimise fuzzy objective function scenarios. This paper proposes aspiration level (AL)-based non-dominated sorting genetic algorithm (NSGA)-II and AL-based NSGA-III to obtain a Pareto-optimal solution that meets DM AL for the FMOMRSPP. A numerical example has been provided to demonstrate the usage of the presented methodologies. A comparison is presented between the proposed and several other approaches. The sensitivity of objective functions is also investigated with ALs and shape parameters. The coverage is determined to evaluate the effectiveness of the proposed methods.