Problem statement: Data Envelopment Analysis (DEA) is a non-parametric technique for measuring the relative efficiency of a set of production systems or Decision Making Units (DMU) that have multiple inputs and outputs. Sometimes, DMUs have a parallel structure, in which systems composed of parallel units work individually; the sum of their own inputs/outputs is the input/output of the system. ...

Data Envelopment Analysis (DEA) is a mathematical programming approach to assess relative efficiencies with a group of decision making units (DMUs) such as production systems. There have been some useful models for their successful applications in many fields. In this paper, we first point out the defect of the first DEA model CCR (Charnes, Cooper and Rhodes, 1978) in measuring the efficiencies...

This paper surveys recently developed analytical methods for studying the sensitivity of DEA results to variations in the data. The focus is on the stability of classification of DMUs (Decision Making Units) into efficient and inefficient performers. Early work on this topic concentrated on developing solution methods and algorithms for conducting such analyses after it was noted that standard ...

1,3-dimethylurea (DMU) was used to mimic urea and to model melamine-urea-formaldehyde (MUF) co-condensation reactions. The products of 1,3-dimethylurea-formaldehyde (DMUF), melamine-formaldehyde (MF), and melamine-1,3-dimethylurea-formaldehyde (MDMUF) reactions under alkaline and weak acidic conditions were compared by performing quantitative carbon-13 nuclear magnetic resonance (13C-NMR) analy...

In measuring the overall efficiency of a set of decision making units (DMUs) in a time span covering multiple periods, the conventional approach is to use the aggregate data of the multiple periods via a data envelopment analysis (DEA) technique, ignoring the specific situation of each period. This paper proposes using a relational network model to take the operations of individual periods into...

Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different mult...

In this paper, we propose new resampling models in data envelopment analysis (DEA). Input/output values are subject to change for several reasons, e.g., measurement errors, hysteretic factors, arbitrariness and so on. Furthermore, these variations differ in their input/output items and their decision-making units (DMU). Hence, DEA efficiency scores need to be examined by considering these facto...

In this paper we discuss the question: among a group of decision making units (DMUs), if a DMU changes some of its input (output) levels, to what extent should the unit change outputs (inputs) such that its efficiency index remains unchanged? In order to solve this question we propose a solving method based on Data Envelopment Analysis (DEA) and Multiple Objective Linear Programming (MOLP). In ...

The weight is one of the main issues of Data Envelopment Analysis (DEA), and relevant theoretical research indicates that many DEA mathematical models include redundant restraints on weight, resulting in underestimated efficiency, pseudo inefficiency, and difficulty in representing specific Input/Output relationships. This study proposes a context-dependent DEA-R model to address shortcomings r...

This paper concentrates on the evaluation of the efficiency of low carbon industrialization in the tourism sector. Combining the general indices of the regional industrialization with the specific characteristics of low carbon development in the tourism sector, a comprehensive index system is scientifically designed. Due to the complexity of the index system and the tight correlation among some...