An inverse data envelopment analysis model with stochastic features

This paper discusses an inverse Data Envelopment Analysis problem with stochastic factors. The inverse DEA problem is: if among a group of non-profitable decision making units, we increase certain input to a particular unit, and assume that the DMU maintains its current efficiency level with respect to other units, how much output we can expect the unit to produce? So the inverse DEA model can be used for short term forecasting problems. The inverse DEA problem with stochastic factors is transformed into a chance constraint multi-objective programming problem, and in some special cases it can be answered by solving normal chance constraint LP problem.