<p>For a distribution company, it is very important to study a transportation system which involves the collection of goods from different manufacturers and then the distribution among various retailers. In this respect, this paper presents two multi-objective two-stage transportation models for the delivery of multiple items under hesitant fuzzy environment. The first stage is the transportation from manufacturers to distributors and the second stage between distributors and retailers. Here, items are transported by distributors from different manufacturers to multiple retailers according to their budgets. For doing this, each distributor demands a certain cost to each retailer against actual unit transportation cost. In most of the cases, this demanded unit transportation cost has been assigned by the distributors on purchasing power of the local customers which may also change in different times in some global crisis. Hence fourth, it is uncertain in nature. To increase the profit margin by convincing retailers, distributors offer discount and credit period policies depending which two models have been developed. The first one is formulated without considering above any policy whereas second one is under all above policies. In both models, the main objective is to maximize the profit of distributors and to minimize the cost of retailers simultaneously by making optimal decisions on the number of used vehicles in the first stage and on transportation amount in the second stage. Further, to convert the uncertain models into equivalent deterministic forms, two different algorithms have been developed. Finally, the converted deterministic models have been solved by using elitist non-dominated sorting genetic algorithm with real-world examples. The numerical results show the efficiency of the proposed models, impact of discount policy, and credit period policy. The sensitivity analysis against different generalized credibility levels of different parameters have been presented with comparison of algorithms.</p>