International Journal of Computing and Mathematics

In the current situation due to elevated market competition, there is a lot of pressure on organizations involved with the transportation industry to provide the service in a better and more efficient way. Systematically distributing goods among clients is not a simple job. Transportation models provide an efficient structure for meeting these difficulties. In a setting of uncertainty, dealing with a multi-objective transportation problem with set parameters is too hard, rather it is simple to consider all associated variables in terms of linguistic parameters. Various goals of a multi-objective transportation problem are influenced by various criteria such as transportation path, weather conditions, cars used for transportation, etc. A multi-stage multi-objective transport model with blurred relationships is created in this research. Minimization of both cost and delivery moment are regarded as two distinct first-stage goals that are associated with several distinct criteria such as distortion moment, fixed charge and mode of transportation. In the second phase, another goal, i.e. the quantity of transported amount, is regarded on the grounds of two prior goals. All of these factors considered for this model are inherently imprecise and are portrayed in terms of linguistic variables. The multi-objective transport problem based on the fuzzy rule is developed and the Genetic Algorithm for Multi-Objective Issues (MOGA) is used to discover the ideal solution. The model is provided with a numerical issue and the optimal outcome is discussed.