Related Experiment Video
Updated: Jun 20, 2025
11:41
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
Published on: February 1, 2020
20.3K
Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study.
Seyed Ehsan Shojaie1, Seyed Jafar Sadjadi2, Reza Tavakkoli-Moghaddam3
1Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Plos One
|July 18, 2024
Summary
This study introduces a novel method for measuring productivity change in two-stage network data envelopment analysis (TSNDEA) by integrating the Malmquist productivity index (MPI) with uncertain programming. This approach enhances performance assessment by accounting for data uncertainty.
Area of Science:
- Operations Research
- Management Science
- Econometrics
Background:
- Measuring productivity change is vital for decision-making unit (DMU) performance evaluation.
- Traditional data envelopment analysis (DEA) models struggle with data uncertainty.
- Two-stage network structures are common in financial and insurance sectors.
Purpose of the Study:
- To develop a new approach for measuring productivity change under data uncertainty.
- To integrate the Malmquist productivity index (MPI) with two-stage network data envelopment analysis (TSNDEA).
- To enhance the robustness and reliability of productivity measurements in DMUs.
Main Methods:
- Utilizing two-stage network data envelopment analysis (TSNDEA).
- Integrating the Malmquist productivity index (MPI) with an uncertain programming framework.
- Employing fuzzy mathematical programming to model data uncertainty in DEA.
Main Results:
- The proposed approach effectively measures productivity change by considering technical efficiency and technological progress.
- The integration of fuzzy programming enhances the modeling of uncertain input and output data.
- The method provides a more robust and reliable assessment of productivity change.
Conclusions:
- The developed method offers a comprehensive analysis of productivity change in DMUs.
- It aids decision-makers in identifying areas for efficiency improvement.
- The findings support more informed resource allocation and strategic decision-making.
Related Concept Videos
01:25Multimachine Stability
150
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
150
01:10Propagation of Uncertainty from Systematic Error
498
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
498
00:59Propagation of Uncertainty from Random Error
663
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
663
01:01Production Efficiency
16.8K
Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
16.8K
00:54Uncertainty: Confidence Intervals
3.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.1K
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
452
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
452