| dc.description.abstract | The performance evaluation is an essential issue in the enterprise’s operations. In past studies, the Data Envelopment Analysis (DEA) is usually utilized to evaluate the performance of the industry. The DEA mainly focuses on employing the actual input and output data, coupled with the concept of production bound, to analyze the efficiency. However, to accurately evaluate the performance, the acquisition to precise input data is very critical. In addition, the deviation can be divided into two sorts. One is the stochastic deviation resulting from the affection of actual operations; the other is the systemic deviation arising from the influence on the human and the instrument. In past DEA studies, the systemic deviation is usually neglected and the Fuzzy DEA or Imprecise DEA is generally employed to evaluate the performance, causing that the evaluated performance has the characteristic of fuzz. In past DEA studies, the systemic deviation is usually neglected and the Fuzzy DEA or Imprecise DEA is generally employed to evaluate the performance, causing that the evaluated performance has the characteristic of fuzz. Therefore, in this study, we design seven different deviation scenarios, utilizing the real operation data from the international container port, to evaluate the performance, under the CCR and BCC models, with the DEA. To clearly present the change in deviation under different scenarios, we utilize the diagram of production bound to express these changes. In addition, to shorten the solution time for using DEA Solver to solve the test problems, we employ the C computer language, coupled with the use of the mathematics programming solver, CPLEX.
The results show that under deviation influence, the decision making units with the efficiency value 1 can be categorized to three different sorts: relative stability units, units influenced by obvious deviation and units influenced by existence of deviation. These decision making units can verify using A&P Model. Other decision making units with the efficiency value from 0.4 to 0.7 are relatively sensitive. It’s contains a special type of decision making units, that is, its deviation of efficiency is much higher than others. However, these sorts can be explored by the proposed method in this study. According to the results, we should spend more human resources and cost on the relatively sensitive units and the special units in order to make sure the accuracy of data and to avoid the impact on deviation. It is expected that the results obtained from our study could be useful reference for the related carriers or studies.
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