traditional dea models deal with measurements of relative eciency ofdmus regarding multiple - inputs vs. multiple-outputs. one of the drawbacksof these model is the neglect of intermediate products or linkong activities. af-ter pointing out needs for inclusion of them to dea models. we propose hybridmodel that can deal with intermediate products formally using this model wecan evaluate divisio...

examining the impacts of data modification is considered as sensitivity analysis. a lot of studies have considered the data modification of inputs and outputs in dea. the issues which has not heretofore been considered in dea sensitivity analysis is modification in the number of inputs and (or) outputs and determining the impacts of this modification in the status of efficiency of dmus. this pa...

Data envelopment analysis (DEA) is an approach to measure the relative efficiency of decision-making units with multiple inputs and multiple outputs using mathematical programming. In the traditional DEA, it is assumed that we know the input or output role of each performance measure. But in some situations, the type of performance measure is unknown. These performance measures are called flexi...

Traditional DEA models deal with measurements of relative eciency ofDMUs regarding multiple - inputs VS. multiple-outputs. One of the drawbacksof these model is the neglect of intermediate products or linkong activities. Af-ter pointing out needs for inclusion of them to DEA models. We propose hybridmodel that can deal with intermediate products formally using this model wecan evaluate division...

Michigan winter wheat (Triticum aestivum L.) growers continue to adopt intensive management strategies. However, instead of broadscale implementation an entire collection inputs simultaneously, practitioners question which may better contribute improved production. Studies evaluated soft plant growth, grain yield, and expected economic net return for multiple agronomic nutrient inputsacross var...