scholarly journals Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case

2019 ◽  
Vol 11 (16) ◽  
pp. 4340 ◽  
Author(s):  
Renata Machado de Andrade ◽  
Suhyung Lee ◽  
Paul Tae-Woo Lee ◽  
Oh Kyoung Kwon ◽  
Hye Min Chung
Keyword(s):  
Data Envelopment Analysis ◽  
Clustering Analysis ◽  
Programming Approach ◽  
Data Envelopment ◽  
Strong Correlations ◽  
Discrimination Power ◽  
Port Efficiency ◽  
Dea Models ◽  
Cargo Throughput ◽  
Brazilian Ports

Data envelopment analysis (DEA) has many advantages for analyzing the efficiency of decision-making units, as well as drawbacks, such as a lack of discrimination power. This study applied bi-objective multiple-criteria data envelopment analysis (BiO-MCDEA), a programming approach used to overcome the limitations of traditional DEA models, to analyze the efficiency of 20 Brazilian ports with a consideration of six input and one output variables from 2010 to 2016. Two time-related variables were included to reflect current problems faced by Brazilian ports experiencing long wait times. The results reveal a significant disparity in port efficiency among Brazilian ports. The top five most efficient ports are those with the highest cargo throughput. A clustering analysis also confirmed a strong correlation between cargo throughput and port efficiency scores. Total time of stay, pier length, and courtyard also had strong correlations with the efficiency scores. The clustering method divided Brazilian ports into three groups: efficient ports, medium efficient ports, and inefficient ports.

scholarly journals Modifying the convexity condition in Data Envelopment Analysis (DEA)

2020 ◽  
Vol 33 (02) ◽  
pp. 454-467
Author(s):  
Roghyeh Malekii Vishkaeii ◽  
Behrouz Daneshian ◽  
Farhad Hosseinzadeh Lotfi
Keyword(s):  
Data Envelopment Analysis ◽  
Real Data ◽  
Data Envelopment ◽  
Convexity Condition ◽  
Discrimination Power ◽  
Data Set ◽  
Step Procedure ◽  
Proposed Model ◽  
Dea Models ◽  
Production Possibility Set

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reza Kiani Mavi ◽  
Neda Kiani Mavi ◽  
Reza Farzipoor Saen ◽  
Mark Goh
Keyword(s):  
Sustainable Development ◽  
Data Envelopment Analysis ◽  
Oecd Countries ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Environmental Research ◽  
Data Envelopment ◽  
Discrimination Power ◽  
Content Type ◽  
Dea Models

PurposeDespite unanimity in the literature that eco-innovation (EI) leads to sustainable development, evidence remains limited on measuring EI efficiency with the Malmquist productivity index (MPI). In conventional data envelopment analysis (DEA) models, decision-making units (DMUs) are inclined to assign more favorable weights, even zero, to the inputs and outputs to maximize their own efficiency. This paper aims to overcome this shortcoming by developing a common set of weights (CSW). Design/methodology/approachUsing goal programming, this study develops a CSW model to evaluate the EI efficiency of the organization for economic co-operation and development (OECD) countries and track their changes with MPI during 2010–2018. FindingsAchieving a complete ranking of DMUs, findings show the higher discrimination power of the proposed CSW compared with the original DEA models. Furthermore, results reveal that Iceland, Latvia and Luxembourg are the only OECD countries that have incessantly improved their EI productivity (MPI > 1) from 2010 to 2018. On the other hand, Japan is the OECD country that has experienced the highest yearly EI efficiency during 2010–2018. This paper also found that Iceland has the highest MPI over 2010–2018. Practical implicationsMore investment in environmental research and development (R&D) projects instead of generic R&D enables OECD members to realize more opportunities for sustainable development through minimizing energy use and environmental pollution in any form of waste and greenhouse gas emissions. Originality/valueIn addition to developing a novel common weights model for DEA-MPI to measure and evaluate the EI of OECD countries, this paper develops a CSW model by including the undesirable outputs for EI analysis.

Fuzzy stochastic Data Envelopment Analysis with application to NATO enlargement problem

2019 ◽  
Vol 53 (2) ◽  
pp. 705-721 ◽  
Author(s):  
Ali Ebrahimnejad ◽  
Seyed Hadi Nasseri ◽  
Omid Gholami
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Threshold Values ◽  
Discrimination Power ◽  
Dea Model ◽  
Nato Enlargement ◽  
Proposed Model ◽  
Stochastic Dea ◽  
Dea Models ◽  
Stochastic Data

Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of Decision Making Units (DMUs) with multiple deterministic inputs and multiple outputs. However, in real-world problems, the observed values of the input and output data are often vague or random. Indeed, Decision Makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Hence, we formulate a new DEA model to deal with fuzzy stochastic DEA models. The contributions of the present study are fivefold: (1) We formulate a deterministic linear model according to the probability–possibility approach for solving input-oriented fuzzy stochastic DEA model, (2) In contrast to the existing approach, which is infeasible for some threshold values; the proposed approach is feasible for all threshold values, (3) We apply the cross-efficiency technique to increase the discrimination power of the proposed fuzzy stochastic DEA model and to rank the efficient DMUs, (4) We solve two numerical examples to illustrate the proposed approach and to describe the effects of threshold values on the efficiency results, and (5) We present a pilot study for the NATO enlargement problem to demonstrate the applicability of the proposed model.

scholarly journals Fuzzy Data envelopment Analysis with SBM using α-level Fuzzy Approach

2019 ◽  
pp. 481-501
Author(s):  
Qaiser Farooq Dar ◽  
Ahn Young Hyo ◽  
Gulbadian Farooq Dar ◽  
Shariq Ahmad Bhat ◽  
Arif Muhammad Tali ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Fuzzy Approach ◽  
Discrimination Power ◽  
Sbm Model ◽  
Fuzzy Data Envelopment Analysis ◽  
Dea Models ◽  
Α Level ◽  
Fuzzy Dea

The applications of fuzzy analysis in data-oriented techniques are the challenging aspect in the field of applied operational research. The use of fuzzy set theoretic measure is explored here in the context of data envelopment analysis (DEA) where we are utilizing the fuzzy α-level approach in the three types of efficiency models. Namely, BCC models, SBM model and supper efficiency model in DEA. It was observed from the result that the fuzzy SBM model has good discrimination power over fuzzy BCC. On the other side, both the models fuzzy BCC and fuzzy SBM are not able to make the genuine ranking which is acceptable for all. So this weakness is overcome with the help of fuzzy super SBM model and all three models are applied to illustrate the types of decisions and solutions that are achievable when the data are vague and prior information is in imprecise. In this paper, we are considering that our inputs and outputs are not known with absolute precision in DEA and here, we using Fuzzy-DEA models based on an α-level fuzzy approach to assessing fuzzy data.

Double Frontier Two-Stage Fuzzy Data Envelopment Analysis

2020 ◽  
Vol 28 (01) ◽  
pp. 117-152 ◽  
Author(s):  
Alireza Amirteimoori ◽  
Hossein Azizi ◽  
Sohrab Kordrostami
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Efficiency Analysis ◽  
Programming Approach ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Insurance Company ◽  
Two Stage ◽  
Dea Models ◽  
Fuzzy Dea

Data envelopment analysis (DEA) is a mathematical programming approach with widespread applications in productivity and efficiency analysis. Compared with traditional DEA models, two-stage DEA models show the performance of each process and make available more information for decision making. In an article by Kao and Liu, models were proposed for combining a two-stage process to achieve overall fuzzy efficiency measures. Their method follows the simple geometric average approach and uses the product of two efficiencies. The present article applies a different angle for efficiency analysis in the two-stage fuzzy DEA. We suggest that the overall efficiency score of a decision-making unit (DMU) is defined as total weight of stage efficiencies, not as the simple product of their efficiency. Moreover, the proposed fuzzy DEA models are different from the model by Kao and Liu for fuzzy data in that our models are linear without the need for additional changes in variables and use the same set of constraints to measure the efficiency of DMUs with fuzzy input and output data. While the models by Kao and Liu are a nonlinear optimization problem that need additional changes in variables, and use different sets of constraints to measure fuzzy efficiencies. Additionally, our proposed approach evaluates the performance of DMUs from both optimistic and pessimistic viewpoints. Finally, using the proposed approach, the Taiwanese non-life insurance company problem will be investigated.

scholarly journals Selecting Suitable, Green Port Crane Equipment for International Commercial Ports

2021 ◽  
Vol 13 (12) ◽  
pp. 6801
Author(s):  
Guo-Ya Gan ◽  
Hsuan-Shih Lee ◽  
Yu-Jwo Tao ◽  
Chang-Shu Tu
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Protection ◽  
Green Energy ◽  
Programming Approach ◽  
Data Envelopment ◽  
Container Handling ◽  
Dea Method ◽  
Green Port ◽  
Large Container ◽  
Gantry Cranes

Responding to the increasing global need for environmental protection, a green port balances economic vibrancy with environmental protection. However, because exhaust emissions (e.g., CO2 or sulfide) are difficult to monitor around ports, data on such emissions are often incomplete, which hinders research on this topic. The present study aimed to fill this gap in this topic. To remedy this problem, this study formulated a new data envelopment analysis (DEA) method for collecting CO2 emissions data at their source. This method was applied to collect real-world operating data from a large container-handling company in Taiwan. Specifically, we provide a real example using a novel green energy index to account for undesirable outputs. Our main objective was to formulate two methods that combine: (1) data envelopment analysis based on a modified slack-based measure, and (2) a multi-choice goal programming approach. The contributions of this paper included the finding that rubber-tired gantry cranes are the greenest and should be used in ports. Finally, our findings aid port managers in selecting port equipment that provides the best balance between environmental protection and profitability.

Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application

2018 ◽  
Vol 17 (05) ◽  
pp. 1429-1467 ◽  
Author(s):  
Mohammad Amirkhan ◽  
Hosein Didehkhani ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Nash Bargaining ◽  
Data Envelopment ◽  
Second Stage ◽  
Proposed Model ◽  
Nash Bargaining Game ◽  
Dea Models ◽  
Overall Efficiency

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

scholarly journals A knowledge system proposal with weighted goal programming approach for multi-criteria dea model and an application

2021 ◽  
Vol 12 (2) ◽  
pp. 422-438
Author(s):  
Tugba Polat ◽  
Safak Kiris
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Goal Programming ◽  
Evaluation Process ◽  
Knowledge System ◽  
Programming Approach ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Weighted Goal Programming

In today's competitive environment, enterprises should use their resources correctly; they should continuously improve themselves and work efficiently. It is important to evaluate the performances of the units under the same conditions in enterprises according to each other, to see the current situations and to determine appropriate improvements in necessary points. One of the commonly used approaches to performance evaluation is Data Envelopment Analysis. Many approaches have been developed for the Data Envelopment Analysis model, and Goal programming using in multi-objective decision making solutions approaches is one of them. Goal Programming gives decision-makers the opportunity to evaluate many objectives together in the decision-making process. In this study, classical Data Envelopment Analysis and weighted goal programming approach for multi-criteria data envelopment analysis model was applied in the evaluation process of the projects worked in an automotive supplier industry. A knowledge system has also been proposed in order to evaluate the effectiveness of the projects periodically and to include new projects or conditions into the evaluation.

scholarly journals A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs

2021 ◽  
Vol 9 (4) ◽  
pp. 378-398
Author(s):  
Chunhua Chen ◽  
Haohua Liu ◽  
Lijun Tang ◽  
Jianwei Ren
Keyword(s):  
Data Envelopment Analysis ◽  
Transportation Systems ◽  
Decision Makers ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Super Efficiency ◽  
Dea Model ◽  
Dea Models ◽  
Analysis Models ◽  
Transport Accidents

Abstract DEA (data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure (RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model, especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA model with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs; 2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable; 3) The results can be easily obtained as it is based on linear programming; 4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China’s regional transportation systems (RTSs) considering the number of transport accidents (an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.

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