A Novel Enhanced Russell Graph Efficiency Measure in Network DEA

2021 ◽  
pp. 1-31
Author(s):  
Robabeh Eslami ◽  
Mohammad Khoveyni
Keyword(s):  
Efficiency Score ◽  
Network System ◽  
Network Structures ◽  
Efficiency Measure ◽  
Network Dea ◽  
Economic Implications ◽  
Multi Stage ◽  
Proposed Model ◽  
Decision Making Units ◽  
High Computational Complexity

Hitherto, the presented models for measuring the efficiency score of multi-stage decision-making units (DMUs) either are nonlinear or require to specify the weights for combining their divisional efficiencies. The nonlinearity leads to high computational complexity for these models, especially when used for problems with enormous dimensions, and also assigning various weights to the divisional efficiencies causes to obtain different efficiency scores for the multi-stage network system. To tackle these problems, this study contributes to network DEA by introducing a novel enhanced Russell graph (ERG) efficiency measure for evaluating the general two-stage series network structures. Then, the proposed model is extended into the general multi-stage series network structures. This study also describes the managerial and economic implications of measuring the efficiency score of the multi-stage DMUs and provides two numerical and empirical examples for illustrating the use of our proposed model.

scholarly journals New inverse DEA models for budgeting and planning

2021 ◽  
Author(s):  
Tahere Sayar ◽  
Mojtaba Ghiyasi ◽  
Jafar Fathali
Keyword(s):  
Planning Process ◽  
Efficiency Score ◽  
Decision Makers ◽  
Data Envelopment ◽  
Input And Output ◽  
Proposed Model ◽  
Dea Models ◽  
Business Sectors ◽  
Input Level ◽  
Decision Making Units

Data envelopment analysis (DEA) measures the efficiency score of a set of homogeneous decision-making units (DMUs) based on observed input and output. Considering input-oriented, the inverse DEA models find the required input level for producing a given amount of production in the current efficiency level. This article proposes a new form of the inverse DEA model considering income (for planning) and budget (for finance and budgeting) constraints. In contrast with the classical inverse model, both input and output levels are variable in proposed models to meet income (or budget) constraints. Proposed models help decision-makers (DMs) to find the required value of each input and each output's income share to meet the income or budget constraint. We apply the proposed model in the efficiency analysis of 58 supermarkets belonging to the same chain. However, these methods are general and can be used in the budgeting and planning process of any production system, including business sectors and firms that provide services.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

scholarly journals How to evaluate sustainability of supply chains? A dynamic network DEA approach

2017 ◽  
Vol 117 (9) ◽  
pp. 1866-1889 ◽  
Author(s):  
Vahid Shokri Kahi ◽  
Saeed Yousefi ◽  
Hadi Shabanpour ◽  
Reza Farzipoor Saen
Keyword(s):  
Big Data ◽  
Supply Chains ◽  
Real World ◽  
Performance Optimization ◽  
Network Dynamic ◽  
Network Dea ◽  
Content Type ◽  
Dea Model ◽  
Proposed Model ◽  
First Time

Purpose The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score. Design/methodology/approach A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach. Findings This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately. Research limitations/implications In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data. Practical implications The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc. Originality/value For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

scholarly journals Assessment of the impact of the COVID-19 pandemic on the poverty of households

2021 ◽  
Vol 11/1 (-) ◽  
pp. 37-39
Author(s):  
Olena POSHYVALOVA
Keyword(s):  
Economic Impact ◽  
Social Protection ◽  
Economic Implications ◽  
Exogenous Shocks ◽  
Proposed Model ◽  
The Social ◽  
National Economies ◽  
The Impact ◽  
Information Basis

Introduction. The COVID-19 pandemic has caused grave and severe losses in many of the economies across the globe. The impact and the duration of the economic crisis occurring due to the pandemic among certain households is difficult to anticipate since the indeterminacy is being defined through the duration of the crisis and costs for the recovery of the economy. The purpose of the paper is to study theoretical aspects related to the assessment of the impact of the COVID-19 pandemic on the poverty of households. Methods. The theoretical and methodological basis of the study are modern theories, concepts, hypotheses. Comparative analysis is used. The methodological and information basis of the work are scientific works, materials of periodicals, information resources. Results. The paper incorporates a content analysis of studies focusing on the economic impact of the COVID-19 on the development of national economies. The majority of studies assess economic implications of the COVID-19 however they are concentrated on the macroeconomic and financial impact of the Corona Crisis. The impact on national economies is subsequently reduced to the microlevel, specifically the social and economic impact on individuals. Nonetheless, there is a need for a microanalysis which may better describe the interrelation between sectors and countries (the effect of macroeconomic aggregate indicators) and supplement the macroanalysis, providing more relevant evaluations of the impact of the distribution of income, outline the authorities of households, the role of people's savings, determine the resilience of households. The work establishes main assumptions and restrictions of formulating the model of impact of social and economic implications of the COVID-19 pandemic on the poverty of households Conclusion. Taking into consideration the distribution of incomes for various sectors, the proposed model allows to ensure the assessment of losses in the consumption of households, savings depletion and time for their recovery. It has been proven that without the social protection of the population the Corona Crisis will lead to a massive economic shock for the national economy. Prospects of further studies lie in the assessment of the impact of indirect macro-level factors, role of indeterminacy in the decision-making of households and implications in case of numerous waves of social crisis as well as the possible effect in the condition of concurrent exogenous shocks.

Measuring the relative performance of forest management units: a chance-constrained DEA model in the presence of the nondiscretionary factor

2019 ◽  
Vol 49 (7) ◽  
pp. 788-801 ◽  
Author(s):  
Majid Zadmirzaei ◽  
Soleiman Mohammadi Limaei ◽  
Alireza Amirteimoori ◽  
Leif Olsson
Keyword(s):  
Decision Making ◽  
Forest Management ◽  
Efficiency Score ◽  
Management Problem ◽  
Strong Positive Correlation ◽  
Deterministic Models ◽  
Management Units ◽  
Dea Model ◽  
Management Plans ◽  
Decision Making Units

In this study, we develop a marginal chance-constrained data envelopment analysis (DEA) model in the presence of nondiscretionary inputs and hybrid outputs for the first time. We call it a stochastic nondiscretionary DEA model (SND-DEA), and it is developed to measure and compare the relative efficiency of forest management units under different environmental management systems. Furthermore, we apply an output-oriented DEA technology to both deterministic and stochastic scenarios. The required data are collected from 24 forest management plans (as decision-making units) and included four inputs and an equal amount of outputs. The findings of this practical research show that the modified SND-DEA model in different probability levels gives us apparently different results compared with the output from pure deterministic models. However, when we calculate the correlation measures, the probability levels give us a strong positive correlation between stochastic and deterministic models. Therefore, approximately 40% of the forest management plans based on the applied SND-DEA model should substantially increase their average efficiency score. As the major conclusion, our developed SND-DEA model is a suitable improvement over previous developed models to discriminate the efficiency and (or) inefficiency of decision-making units to hedge against risk and uncertainty in this type of forest management problem.

A New Single-Zone Multi-Stage Scavenging Model for Real-Time Emissions Control in Two-Stroke Engines

2019 ◽  
Author(s):  
Abdullah U. Bajwa ◽  
Mark Patterson ◽  
Taylor Linker ◽  
Timothy J. Jacobs
Keyword(s):  
Gas Exchange ◽  
Internal Combustion Engines ◽  
Engine Performance ◽  
Combustion Engines ◽  
Thermodynamic State ◽  
Multi Stage ◽  
Exchange Processes ◽  
Proposed Model ◽  
Perfect Mixing ◽  
And Control

Abstract Gas exchange processes in two-stroke internal combustion engines, i.e. scavenging, remove exhaust gases from the combustion chamber and prepare the fuel-oxidizer mixture that undergoes combustion. A non-negligible fraction of the mixture trapped in the cylinder at the conclusion of scavenging is composed of residual gases from the previous cycle. This can cause significant changes to the combustion characteristics of the mixture by changing its composition and temperature, i.e. its thermodynamic state. Thus, it is vital to have accurate knowledge of the thermodynamic state of the post-scavenging mixture to be able to reliably predict and control engine performance, efficiency and emissions. Several simple-scavenging models can be found in the literature that — based on a variety of idealized interaction modes between incoming and cylinder gases — calculate the state of the trapped mixture. In this study, boundary conditions extracted from a validated 1-D predictive model of a single-cylinder two-stroke engine are used to gauge the performance of four simple scavenging models. It is discovered that the assumption of thermal homogeneity of the incoming and exiting gases is a major source of inaccuracy. A new non-isothermal multi-stage single-zone scavenging model is thus, proposed to address some of the shortcomings of the four models. The proposed model assumes that gas-exchange in cross-scavenged two-stroke engines takes place in three stages; an isentropic blowdown stage, followed by perfect-displacement and perfect-mixing stages. Significant improvements in the trapped mixture state estimates were observed as a result.

An optimistic-pessimistic DEA model based on game cross efficiency approach

2020 ◽  
Vol 54 (4) ◽  
pp. 1215-1230
Author(s):  
Mediha Örkcü ◽  
Volkan Soner Özsoy ◽  
H. Hasan Örkcü
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Equilibrium Solution ◽  
Data Envelopment ◽  
Optimal Weights ◽  
Discriminating Power ◽  
Proposed Model ◽  
Essential Problem ◽  
Decision Making Units ◽  
Selection Of

The ranking of the decision making units (DMUs) is an essential problem in data envelopment analysis (DEA). Numerous approaches have been proposed for fully ranking of units. Majority of these methods consider DMUs with optimistic approach, whereas their weaknesses are ignored. In this study, for fully ranking of the units, a modified optimistic–pessimistic approach, which is based on game cross efficiency idea is proposed. The proposed game like iterative optimistic-pessimistic DEA procedure calculates the efficiency scores according to weaknesses and strengths of units and is based on non-cooperative game. This study extends the optimistic-pessimistic DEA approach to obtain robust rank values for DMUs. The proposed approach yields Nash equilibrium solution, thus overcomes the problem of non-uniqueness of the DEA optimal weights that can possibly reduce the usefulness of cross efficiency. Finally, in order to verify the validity of the proposed model and to show the practicability of algorithm, we apply a real-world example for selection of industrial R&D projects. The proposed model can increase the discriminating power of DMUs and can fully rank the DMUs.

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