scholarly journals Finding Targets in Non-Radial FDH Models: A Hybrid Technique Based on STEM and Extended Ratio Based Approach

2021 ◽  
Vol 11 (22) ◽  
pp. 10626
Author(s):  
Mehdi Abbasi ◽  
Mohammad Reza Mozaffari ◽  
Peter F. Wanke ◽  
Mohamad Amin Kaviani
Keyword(s):  
Decision Making ◽  
Interactive Methods ◽  
Programming Models ◽  
Hybrid Technique ◽  
Fars Province ◽  
Step Method ◽  
Free Disposal Hull ◽  
Free Disposal ◽  
Decision Making Unit ◽  
Dea Models

Performance evaluation using interactive methods and extended ratio-based approaches can be very important for some organizations. Free disposal hull models can be created if there is no concern for convexity, and using non-radial DEA models can simultaneously create more logical and practical situations for finding DMU targets. In this paper, a new hybrid technique based on the additive slack based method and enhanced Russel measure in variable return to scale technology has been proposed. The proposed technique can find decision making unit targets in non-radial free disposal hull models using the step method. Furthermore, the extended ratio-based approach in the proposed technique has been applied to find DMU targets of related non-radial free disposal hull models without solving any mathematical programming models. Finally, targets of Fars province pharmaceutical distributing companies were found by applying the proposed hybrid technique.

scholarly journals Order Picking and E-Commerce: Introducing Non-Parametric Efficiency Measurement for Sustainable Retail Logistics

2021 ◽  
Vol 16 (4) ◽  
pp. 846-858
Author(s):  
Matthias Klumpp ◽  
Dominic Loske
Keyword(s):  
Goodness Of Fit ◽  
New Technologies ◽  
Order Picking ◽  
Free Disposal Hull ◽  
Free Disposal ◽  
Efficiency Assessment ◽  
Parametric Data ◽  
Proposed Model ◽  
Dea Models ◽  
Non Parametric

Order picking is a crucial but labor- and cost-intensive activity in the retail logistics and e-commerce domain. Comprehensive changes are implemented in this field due to new technologies like AI and automation. Nevertheless, human worker’s activities will be required for quite some time in the future. This fosters the necessity of evaluating manual picker-to-part operations. We apply the non-parametric Data Envelopment Analysis (DEA) to evaluate the efficiency of n = 23 order pickers processing 6109 batches with 865,410 stock keeping units (SKUs). We use distance per location, picks per location, as well as volume per SKU as inputs and picks per hour as output. As the convexity axiom of standard DEA models cannot be fully satisfied when using ratio measures with different denominators, we apply the Free Disposal Hull (FDH) approach that does not assume convexity. Validating the efficiency scores with the company’s efficiency assessment, operationalized by premium payments shows a 93% goodness=of-fit for the proposed model. The formulated non-parametric approach and its empirical application are promising ways forward in implementing empirical efficiency measurements for order picking operations within e-commerce operations.

scholarly journals Finding the Most Preferred Decision-Making Unit in Data Envelopment Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Shirin Mohammadi ◽  
S. Morteza Mirdehghan ◽  
Gholamreza Jahanshahloo
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Utility Function ◽  
Decision Maker ◽  
Programming Models ◽  
Data Envelopment ◽  
Production Possibility ◽  
Decision Making Unit ◽  
Inputs And Outputs

Data envelopment analysis (DEA) evaluates the efficiency of the transformation of a decision-making unit’s (DMU’s) inputs into its outputs. Finding the benchmarks of a DMU is one of the important purposes of DEA. The benchmarks of a DMU in DEA are obtained by solving some linear programming models. Currently, the obtained benchmarks are just found by using the information of the data of inputs and outputs without considering the decision-maker’s preferences. If the preferences of the decision-maker are available, it is very important to obtain the most preferred DMU as a benchmark of the under-assessment DMU. In this regard, we present an algorithm to find the most preferred DMU based on the utility function of decision-maker’s preferences by exploring some properties on that. The proposed method is constructed based on the projection of the gradient of the utility function on the production possibility set’s frontier.

scholarly journals Global malmquist index for measuring the economic productivity changes

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1278-1285
Author(s):  
Esmaeil Mombini ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mansor Saraj ◽  
Mohsen Zahraei ◽  
Reza Tayebi Khorami
Keyword(s):  
Decision Making ◽  
Malmquist Index ◽  
Productivity Index ◽  
Programming Models ◽  
Data Envelopment ◽  
Decision Unit ◽  
Economic Productivity ◽  
Use Of Resources ◽  
Decision Making Unit ◽  
Decision Making Units

Data envelopment analysis is a nonparametric method for measuring of the performance of decision-making units—which do not need to have or compute a firm’s production function, which is often difficult to calculate. For any manager, the progress or setback of the thing they manage is important because it makes planning and adoption of future policies for the organization or decision-making unit more rational and scientific. Different methods have been used to calculate the improvements and regressions using Malmquist Index. In this article, we evaluate the units under review in terms of economic efficiency, and the units in terms of spending, production, revenue and profit over several periods, and the rate of improvement or regression of each of these units. Considering the minimal use of resources and consuming less money, generating more revenue, and maximizing profits, the improvement or retreat of the recipient’s decision unit in terms of cost, revenue, and profit was examined by presenting a method based on solving linear programming models using the productivity index is Malmquist and Malmquist Global. Finally, by designing and solving a numerical example, we emphasize and test the applicability of the material presented in this article.

scholarly journals The Comparative Efficiency Analysis of EU Members Agriculture Sectors

2017 ◽  
Vol 37 (332) ◽  
pp. 10-19 ◽  
Author(s):  
Eligijus Laurinavičius ◽  
Daiva Rimkuvienė
Keyword(s):  
Stochastic Frontier ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Multifunctional Agriculture ◽  
Stochastic Frontier Approach ◽  
Free Disposal Hull ◽  
Comparative Efficiency ◽  
Free Disposal ◽  
Decision Making Unit ◽  
Dea Method

Abstract Production economics forms a very important part of an enormous range of economic theory. Agricultural production is no exception. When evaluating the competitiveness of the multifunctional agriculture, it is necessary to use the measure of efficiency instead of productivity. The conception of the efficiency is explained and the methods for measurement are provided. The authors discuss the methods of Stochastic Frontier Approach (SFA), Free Disposal Hull (FDH) and Data Envelopment Analysis (DEA) that are particularly useful for multi-criterial evaluation of multifunctional processes. Those methods assign an efficiency score to each Decision Making Unit (DMU) based on how well it transforms a given set of inputs into outputs. Most studies have only focused on application of DEA method for assesment of the efficiency of agriculture farms. There is still a need on applications for sectors. This paper provides an examination of the applicability of DEA method to agriculture sectors efficiency measurement. By applying mathematical models, which are based on the DEA, the efficiency of agriculture in each EU country was evaluated.

scholarly journals Construction of three-dimensional sections of the efficient frontier for non-convex models

2019 ◽  
Vol 488 (5) ◽  
pp. 481-485
Author(s):  
V. E. Krivonozhko ◽  
A. V. Lychev ◽  
N. S. Blokhina
Keyword(s):  
Scientific Literature ◽  
Three Dimensional ◽  
Real Life ◽  
Efficient Frontier ◽  
Data Envelopment Analysis Model ◽  
Data Sets ◽  
Analysis Model ◽  
Free Disposal Hull ◽  
Free Disposal ◽  
Dea Models

Non-convex Free Disposal Hull (FDH) model was proposed in the scientific literature in the end of 20-th century for performance measurement of complex multidimensional production units. FDH model was proposed almost simultaneously with DEA (Data Envelopment Analysis) model. However, as distinct from the DEA models, production possibility set of FDH models are non-convex ones, what significantly refrained the development of these models. As far as we know, the necessity for such approach has been noted in the world scientific literature for a long time. In this paper, an approach is proposed for three-dimensional visualization of FDH models. An approach was tested using real-life data sets from different areas. Computational experiments confirm reliability and effectiveness of the proposed approach.

scholarly journals Dual frontiers data envelopment analysis: multiple ranking approaches from optimistic and pessimistic perspectives

2021 ◽  
Author(s):  
Abdullah Maraee Aldamak
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Efficiency Score ◽  
The Body ◽  
Data Envelopment ◽  
Free Disposal Hull ◽  
Dea Model ◽  
Ranking Models ◽  
Input And Output ◽  
Dea Models

The field of data envelopment analysis (DEA) has evolved rapidly since its introduction to decision-making science 40 years ago. DEA has since attracted the attention of many researchers because of its unique characteristic to measure the efficiency of multiple-input and multiple-output decision-making units (DMUs) without assigning prior weight to the input and output, unlike most available decision analysis tools. The body of research has resulted in a huge amount of literature and diverse DEA models with very many different approaches. DEA classifies all units under assessment into two groups: efficient with a 100% efficiency score and inefficient with a less than 100% efficiency score. This ability is considered both a strength and a weakness of the standard DEA model because, although it allows DEA to evaluate the efficiency of any dataset, it lacks the power to rank all DMUs, by giving full efficiency scores to many efficient units. This issue has attracted many researchers to investigate the weak discrimination power of classical DEA models, resulting in a subfield of research that focuses on DEA ranking. This thesis focuses on the development of the conventional DEA model, and an attempt has been made to study models that are considered as improved models, or approaches that bring a better ranking field, that may bring more accurate evaluation than the original DEA. After studying DEA ranking models, the thesis presents various models under the optimistic and pessimistic DEA ranking approaches. The first and fundamental contribution are the optimistic and pessimistic free disposal hull (FDH) models. In this study, authentic optimistic and pessimistic DEA models without convexity are developed from both input and output orientation. Further into the research investigation, extended models have been proposed, by combining the conventional and FDH ranking models with other different approaches in the literature. Chapter 4 of this thesis presents three extended FDH models: an FDH slack-based model, an FDH superefficiency model, and a dual frontier without infeasibility super-efficiency FDH model. Chapter 5 shows the development of extended models when virtual DMUs are considered. Improved virtual DMU models and improved FDH virtual DMU models are proposed in order to develop the DEA ranking ability from both optimistic and pessimistic approaches. The final model is an optimistic and pessimistic forecasting approach using regression analysis. The forecasting model can be used by decision makers to determine the resources needed for future planning to build an efficient new unit with reference to the current DMU set.

Evaluation of Heuristics Using Data Envelopment Analysis

2014 ◽  
Vol 13 (04) ◽  
pp. 795-810 ◽  
Author(s):  
Chung-Cheng Jason Lu ◽  
Yen-Chun Jim Wu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Data Envelopment Analysis ◽  
Statistical Tests ◽  
Data Envelopment ◽  
Decision Making Unit ◽  
Dea Models ◽  
Using Data ◽  
Inputs And Outputs

This paper focuses on identifying relatively efficient configurations of algorithmic operators among a set of configurations in the development of heuristics or meta-heuristics. Each configuration is considered as a decision-making unit with multiple inputs and outputs. Then, data envelopment analysis (DEA) is adopted to evaluate relative and cross-efficiencies of a set of algorithmic configurations. The proposed approach differs from existing methods based on statistical tests in that multiple inputs and outputs are simultaneously considered in an integrated framework for the evaluation of algorithmic efficiency. A case study is presented to demonstrate the application of DEA for determining the efficient configurations of genetic algorithm operators. The evaluation results of two DEA models are also compared. The DEA evaluation results are consistent with those obtained by a commonly used statistical method.

scholarly journals Relative Technical Efficiency of Life Insurance Companies DEA and Interval DEA Methods

2020 ◽  
Vol 8 (4S5) ◽  
pp. 101-105
Keyword(s):  
Decision Making ◽  
Life Insurance ◽  
Relative Efficiency ◽  
Interval Data ◽  
Vital Role ◽  
Insurance Companies ◽  
Data Envelopment ◽  
Parametric Linear Programming ◽  
Decision Making Unit ◽  
Dea Models

Insurance industries in India have taken a huge shape especially after privatization and introduction of Insurance Regulatory & Development Authority (IRDA). It plays an vital role in the growth of financial sector in all developed and developing countries. Insurance may be a sort of risk management and primarily used hedge against the danger of a contingent or uncertain loss. In this paper the author analyses the relative efficiency of life insurance companies in India using DEA and Interval Data Envelopment Approach (Interval DEA). DEA is a non parametric linear programming problem used for measuring the relative efficiency of decision making units (DMU) which utilize several identical inputs to produce a set of identical outputs. Interval DEA model is used in efficiency measurement of the life insurance companies under imprecise inputs and outputs. The empirical results of the conventional DEA models and Interval DEA models are computed to trace the performance of decision making unit at a possibility level.

scholarly journals The Environmental Efficiency Analysis Based on the Three-Step Method for Two-Stage Data Envelopment Analysis

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7028
Author(s):  
Qingyou Yan ◽  
Fei Zhao ◽  
Xu Wang ◽  
Tomas Balezentis
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Cooperative Game ◽  
Environmental Efficiency ◽  
Data Envelopment ◽  
Step Method ◽  
Two Stage ◽  
Dea Model ◽  
The Status ◽  
Dea Models

This paper suggests that the efficiency of a system (decision-making unit) and its subsystem cannot be properly measured using a two-stage data envelopment analysis (DEA) model either in cooperative or non-cooperative evaluation. Indeed, the existing methods subjectively determine the status of the subsystems in the whole system. The two-stage DEA models, either cooperative game or non-cooperative game, are used to analyze the environmental efficiency. However, when the actual relationship between the two subsystems is inconsistent with the subjective relationship assumptions, the overall efficiency of the system and the efficiency of each subsystem will be biased. The conventional two-stage DEA models require predetermining the relationship between the subsystems within the system based on the subjective judgment of the decision-maker. Based on this, this paper proposes a three-step method to solve the two-stage DEA. First, the position relation among subsystems is determined according to the optimal weights through the model. According to the status relationship among subsystems, the decision units are grouped, and the two-stage DEA model of cooperative game or non-cooperative game is used to analyze the efficiency in each group. This method reduces the subjectivity of decision making and analyzes the efficiency of each decision unit applying the most appropriate two-stage DEA model to find the source of inefficiency. Finally, this paper verifies the rationality and validity of the method by analyzing the water use efficiency of industrial systems in China. It is found that most regions in China value economic development more than environmental protection (as evidenced by the DEA weights). What is more, the method proposed by the paper can be generalized for any two-stage DEA problem.

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