Uncertainty in Data Envelopment Analysis: Fuzzy and Belief Degree-Based Uncertainties

Uncertainty in Data Envelopment Analysis: Fuzzy and Belief Degree-Based Uncertainties introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods: Fuzzy DEA and Belief Degree Based Uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where inputs and outputs of systems and processes are volatile and complex, making measurement difficult. Classical data envelopment analysis (DEA) models use crisp data in order to measure inputs and outputs of a given system.

Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex-uncertain data, then they will become more important and practical for decision-makers.

《資料包絡分析中的不確定性：模糊和信念度基礎的不確定性》介紹了在資料包絡分析（DEA）模型中研究不確定數據的方法，深入探討了兩種不確定DEA方法：模糊DEA和基於信念度的不確定DEA，這些方法基於不確定度量。這些模型旨在解決傳統數據分析在系統和過程的輸入和輸出不穩定且複雜的情況下遇到的問題，使測量變得困難。傳統的DEA模型使用確定的數據來測量給定系統的輸入和輸出。

在傳統DEA模型中，確定的輸入和輸出數據是基本不可或缺的。如果這些模型包含複雜的不確定數據，則對於決策者來說，它們將變得更加重要和實用。