Data Envelopment Analysis(DEA) has been widely used as a means of relative efficiency evaluation since the first DEA model was introduced in 1978. It uses mathematical programming techniques and models to evaluate the relative efficiency of decision making units(DMUs) with multiple inputs and outputs. In DEA, an inefficient DMU’s efficiency can be improved by adjusting the inputs or outputs or both to reach the projection target on the efficient frontier. In this research, we aim at solving the lowest cost problem in DEA, which is to provide an efficient target for an inefficient DMU with the lowest adjustment costs. For this purpose, a new approach based on the least distance DEA model is proposed. Here, the marginal costs of adjusting the inputs and outputs are assumed to be known and symmetrical. For the practical merit, different with the existing studies, our approach is able to increase inputs and decrease outputs. Numerical experiments are conducted to compare the performance of the proposed approach with previous existing studies. The results show that the proposed approach can always provide an efficient target with no higher total adjustment costs than the costs of targets provided by previous approaches. Therefore, this research’s contributions can be summarized as follows:

　　• Propose an approach to DEA that minimizes the total adjustment costs incurred when transitioning an inefficient DMU to an efficient target;

• Enable the real world condition that some inputs could be increased or some outputs could be reduced to be reflected in the target setting process.

Thus, the proposed approach is more practical and useful for decision makers.