Robust data envelopment analysis with Monte Carlo simulation model for optimization the energy consumption in agriculture

The main goal of this study is to evaluate the energy flow and optimization the energy consumption for greenhouse cucumber production with Data Envelopment Analysis (DEA) and Robust Data Envelopment Analysis (RDEA) method by some different values of uncertainty (e) and probability (p) levels. The data were collected randomly from 30 Quonset shape greenhouse structures in Golshan city, Iran by face to face questionnaire method. The results showed that fossil fuel and electricity have the highest share of total energy consumption (more than 95%). So, by controlling the consumption of fossil fuel and electricity in inefficient units, a significant amount of energy can be saved. The results of RDEA showed that by decreasing the probability of deviation from the limit, the number of fully effective units and the average of efficiency score for greenhouse units were decrease. So, there is a trade-off equation between the protection of the system against uncertainty and the efficiency score. Another notable result of this study is the increase in the ESTR index from 10.22 to 22.36, along with the decrease in the probability of p from 100% to 10%, and by controlling the consumption of natural gas and electricity inputs in inefficient units, a significant amount of energy can be easily stored. Finally, the results of the Monte Carlo simulation method showed that the efficiency of the energy units by using the RDEA method was significantly higher than the conventional DEA method.