Estimating most productive scale size with double frontiers data envelopment analysis

In this paper, most productive scale size (MPSS) for input and output mixes is measured from pessimistic point of view by using pessimistic data envelopment analysis (DEA). It is proved that the decision making unit (DMU) with the maximum pessimistic efficiency represents MPSS. However, the optimistic and the pessimistic measurements may identify different DMU as MPSS. To find the optimal DMU that represents MPSS, a double frontiers approach is developed by using the Hurwicz criterion to integrate both the information on the optimistic and the pessimistic frontiers. Numerical examples are provided to show the applications of the proposed methods in estimating MPSS.