Titlebook: Analytical Decision Making and Data Envelopment Analysis; Advances and Challen S.A Edalatpanah,Farhad Hosseinzadeh Lotfi,Peter Wa Book 2024

偏狂癥

Lecture Notes in Computer Sciencea production possibility set that satisfies certain microeconomic and mathematical axioms, such as free disposability in inputs and outputs and convexity, and determines the most conservative estimate of technical inefficiency of each assessed unit via the minimal extrapolation principle (i.e., the

附錄

CLAMP

受傷

修剪過的樹籬

Aaron Cooper,Matteo Danforth,April Edwardsitive set of inputs and outputs is available. However, in certain real-world scenarios, it becomes necessary to assess the performance of DMUs when dealing with flexible and random measures. Hence, in this chapter, we propose the utilization of two distinct methodologies, namely oriented and non-ori

輕觸

ABHOR

庇護(hù)

Jacob T. Browne,Ignacio Garnhamt productive scale size (MPSS) and returns to scale (RTS), are among the most significant challenges faced by managers and decision-makers (DMs) in various real-world situations and problems. Data envelopment analysis (DEA), a widely used and powerful approach, can be applied to address all of these

慌張

領(lǐng)先

Weina Qu,Xiaohui Luo,Jiqing Hou,Yan Ge learning techniques for estimating production frontiers, satisfying shape constraints (free disposability, convexity, …). The second group consists in approaches that apply frontier estimators to classify observations under the classical framework of supervised machine learning with two or more cla