This paper presents an innovative solution methodology for optimizing a biomedical solid transportation problem within an interval based framework. In this setting, key parameters–such as cost coefficients, source and destination specifications, and conveyance capacities are represented as interval numbers to capture inherent uncertainties. The original interval transportation problem having single objective is reformulated to a crisp bi-objective problem, aiming to minimize both the lower bound and the width of the interval objective function. A fuzzy programming technique is then employed to obtain a Pareto-optimal solution to the reformulated problem. Additionally, we revisit and adapt the approach proposed by Ishibuchi and Tanaka [1], which focuses on minimizing the upper bound and the midpoint of the interval objective function. To showcase the practical applicability of the proposed method, a numerical example in pharmaceutical logistics is provided. Furthermore, nine diverse problem instances are solved using both the proposed and existing methodologies. Subsequently, a comparative analysis is conducted to evaluate the performance and effectiveness of each approach in solving interval-based biomedical solid transportation problems.