Transportation Planning Decision-Making Using Intuitionistic Fuzzy Programming

In this research, we use intuitionistic fuzzy programming to bring insight into transportation planning decision-making processes, namely multi-objective optimization (MOO). Considerations for transportation time and prices are made in the case study, which entails moving things from one location to another. In order to improve the model’s capacity to deal with uncertainty, the supply and demand parameters are represented using crisp parameters. With the goal of simultaneously minimizing time and overall cost, the study interacts MOO with crisp data. Our solution is shown to be feasible by means of a hypothetical numerical issue. With linear membership as well as non-membership functions in particular, the transportation planning problem is tackled and solved. As a result of offering decision-makers with thorough solutions that successfully balance many objectives, the results highlight the effectiveness of the intuitionistic fuzzy programming technique in addressing real-world transportation planning difficulties.