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Trade and transportation are two related issues that affect each other, and planning freight-carrying vehicles to deliver products to markets is a challenging logistics issue which is currently under extensive studies. To have a better decision-making, a bi-objective mathematical programming model should be addressed. It is critical for decision-makers to minimize the travel costs while considering the desired product quality in the destination to maximize the customers’ satisfaction. In the scientific research, uncertainty is natural currency. Uncertainty is normal in the real world, but to decision-makers, it sounds like ineffective. As a result, to have a robust decision, a robust possibilistic programming model is proposed. This paper is aimed to plan and allocate the product to proper carrier with appropriate vehicle in the specified route considering its percent reduced quality, under uncertainty. To solve the problem, use has been made of the mathematical modeling and the model has been implemented on a real case to validate it. Tomatoes, oranges, and potatoes have been considered in three high-traffic routes and the model has been solved once without and once with uncertainty in the demand. Numerical results have shown that the proposed model reduces the total cost and, hence, reduces the cost price of the finished product, and prevents its reduced quality when carried by proper vehicles. Effects of the model’s key parameters on the cost price have also been checked through some sensitivity analyses.