<![CDATA[Transportation models are a specific type of linear programming used to determine optimal distribution patterns by considering the balance between total supply and total demand. Through mathematical formulations involving transportation costs C_{ij}, distribution allocations X_{ij}, supply a_i, and demand b_j, these models provide an effective analytical framework for evaluating the efficiency of a distribution system. This study applies two solution methods, namely the Maximum Range Method (MRM) and the AH Method (AHM), to a balanced transportation model without linking it to a specific industrial context. The MRM calculation process is carried out by identifying the largest cost difference in each row and column to determine the allocation location that has the potential to provide the highest savings. On the other hand, AHM works as a direct optimization method by selecting the smallest global cost at each stage of calculation. The results of the study show that MRM produces a total cost of 2070, which is lower than AHM, which produces a total cost of 2100. This difference indicates that MRM is more efficient for the transportation data model in this study. These two methods provide different computational perspectives but can complement each other in the analysis of transportation problem solving based on cost optimization.]]>
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