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Common weights in dynamic network DEA with goal programming approach for performance assessment of insurance companies in Iran

机译：具有目标规划方法的动态网络DEA中的通用权重，用于伊朗保险公司的绩效评估

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Purpose - Conventional data envelopment analysis (DEA) models permit each decision-making unit (DMU) to assess its efficiency score with the most favorable weights. In other words, each DMU selects the best weighting schemes to obtain maximum efficiency for itself. Therefore, using different sets of weights leads to many different efficient DMUs, which makes comparing and ranking them on a similar basis impossible. Another issue is that often more than one DMU is evaluated as efficient because the selection of weights is flexible; therefore, all DMUs cannot be completely differentiated. The purpose of this paper is to development a common weight in dynamic network DEA with a goal programming approach. Design/methodology/approach - In this paper, a goal programming approach has been proposed to generate common weights in dynamic network DEA. To validate the applicability of the proposed model, the data of 30 non-life insurance companies in Iran during 2013-2015 have been used for measuring their efficiency scores and ranking all of the companies. Findings - Findings show that the proposed methodology is an effective and practical approach to measure the efficiency of DMUs with dynamic network structure. Originality/value - The proposed model delivers more knowledge of the common weight approaches and improves the DEA theory and methodology. This model makes it possible to measure efficiency scores and compare all DMUs from multiple different standpoints. Further, this model allows one to not only calculate the overall efficiency of DMUs throughout the time period but also consider dynamic change of the time period efficiency and dynamic change of the divisional efficiency of DMUs.
机译：目的-常规数据包络分析（DEA）模型允许每个决策单位（DMU）以最有利的权重评估其效率得分。换句话说，每个DMU选择最佳加权方案以获得自己的最大效率。因此，使用不同的权重集会导致许多不同的有效DMU，这使得无法在相似的基础上进行比较和排名。另一个问题是，由于权重的选择是灵活的，通常不止一个DMU被评估为有效。因此，无法完全区分所有DMU。本文的目的是使用目标编程方法开发动态网络DEA中的通用权重。设计/方法/方法-在本文中，提出了一种目标编程方法来在动态网络DEA中生成通用权重。为了验证该模型的适用性，我们使用了2013-2015年伊朗30家非寿险公司的数据来衡量其效率得分并对所有公司进行排名。结果-研究结果表明，所提出的方法是一种有效且实用的方法，可以测量具有动态网络结构的DMU的效率。原创性/价值-提出的模型可提供更多有关常见权重方法的知识，并改进了DEA理论和方法。该模型可以测量效率得分并从多个不同角度比较所有DMU。此外，该模型不仅可以计算整个时间段内DMU的整体效率，还可以考虑时间段效率的动态变化和DMU划分效率的动态变化。

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