MEASURES DEA TO EVALUATE PERFORMANCE OF SUPPLY CHAIN (CASE OF STUDY: ELECTRIC POWER SUPPLY CHAIN IN IRAN)

Decision making units (DMUs) with network structure can be operated into series subsystems where each subsystem in the series may consist of parallel divisions or may not. In this case, the system eciency shows the performance of the system, series subsystems, and parallel divisions. The mixed structure network DEA models are developed into deterministic linear programming models under the assumption of constant return to scale (CRS) with undesirable outputs. In this study, the performance of the supply chain of Iran's power industry has been evaluated in a series- parallel structure. The supply chain includes the sixteen regions of generation, transmission, and distribution subsystems, which are interconnected in series format. Power plants and power distribution companies are also considered in the calculation of eciency scores with a parallel and independent divisions. The main contribution of the paper are 1) to develop the constraints for evaluating parallel divisions using a constant lambda coecient in a network-slack based measure models; 2) to determine the origin of ine ciency in generation, transmission, and distribution subsystems, power plants, and distribution companies; 3) to consider the all types of inputs, intermediate measures, the desired and undesired intermediate and nal outputs; and 4) to consider the real case study at the level of the Iranian power industry. The Network DEA Models of the paper is to evaluate the performance of the system and multi subsystems which are formed of unequal and parallel divisions. The proposed models provide the origin of the ineciency for system and subsystems. Also, the ndings shown that the eciency percentage of ecient DMUs for the system, generation, transmission, and distribution subsystems are respectively 12.5, 31.25, 31.25, 25.