Composite Index Construction in Performance Evaluation: A Network DEA Approach Public
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Performance evaluation is vital to business or operations in decision-making, productivity enhancement, and continuous improvement. Effective performance assessment is essential in assisting firms or organizations to execute their strategic goals, and evaluate their competitive capabilities, operations strategy, and other actions. However, it can be difficult to implement performance evaluation and benchmarking due to the complex relations among various performance metrics for specific operations or entities under consideration. The current study focuses on composite index construction in performance evaluation via a data-oriented tool called data envelopment analysis (DEA). DEA is a linear programming based approach for evaluating relative performances of similar operations or decision-making units (DMUs). When multiple performance metrics exist, DEA has been proven an effective tool for multiple-factor reconciliation and best-practice identification. Under big data modeling, the traditional DEA is not sufficient to deal with information and value that are hidden within data. The current dissertation develops a network DEA technique for performance metrics that are inter-linked as in network structures. We consider the internal data structures of DMUs by expanding existing simple network structures of performance measures. Unlike the existing network DEA models which can be solved via linear programming, network DEA models in the current study are non-linear due to the complexity of the performance data’s network structures. We use the second-order cone programming (SOCP) technique to solve the non-linear network DEA models. The current study applies the new network DEA technique to provide a performance evaluation index for eight major airlines from 2006 to 2016 via considering both operations and economics metrics. The new technique is also applied to evaluate the globalization performance via constructing composite globalization indices for countries by integrating globalization indicators in political, economic and social dimension.
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Permanent link to this page: https://digital.wpi.edu/show/m613n146n