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Forecasting the confidence interval of efficiency in fuzzy DEA

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KAFI A., DANESHIAN B., ROSTAMY-MALKHALIFEH M.
Operations Research and Decisions
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2021
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vol. 31
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issue 1
41-59
EN
Data envelopment analysis (DEA) is a well-known method that based on inputs and outputs calculates the efficiency of decision-making units (DMUs). Comparing the efficiency and ranking of DMUs in different periods lets the decision-makers prevent any loss in the productivity of units and improve the production planning. Despite the merits of DEA models, they are not able to forecast the efficiency of future periods with known input/output records of the DMUs. With this end in view, this study aims at proposing a forecasting algorithm with a 95% confidence interval to generate fuzzy data sets for future periods. Moreover, managers’ opinions are inserted in the proposed forecasting model. Equipped with the forecasted data sets and concerning the data sets from earlier periods, this model can rightly forecast the efficiency of the future periods. The proposed procedure also employs the simple geometric mean to discriminate between efficient units. Examples from a real case including 20 automobile firms show the applicability of the proposed algorithm.
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Efficiency measurement in dynamic two-stage network structures with flexible intermediate materials

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SEYEDBOVEIR S., KORDROSTAMI S., DANESHIAN B., AMIRTEIMOORI A.
Operations Research and Decisions
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2018
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vol. 28
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issue 3
63-76
EN
Data envelopment analysis (DEA) is a nonparametric method for evaluating the relative efficiency of decision making units (DMUs) described by multiple inputs and multiple outputs. Since DEA was introduced in the 1970s, it has been widely applied to measure the efficiency of a wide variety of production and operation systems, including two-stage production systems with a series or parallel structure. The outputs from the first stage to the next stage are called intermediate factors (or measures). In some real applications, an intermediate material or some part of it can become the final output or input to the second stage of production. Previously existing models cannot be employed directly to measure the efficiency of such systems. The authors introduce a dynamic DEA model that identifies the structure of flexible intermediate factors to maximise the measure of overall system efficiency.
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