Robust DEA efficiency scores: A probabilistic/combinatorial approach

In this paper we propose robust efficiency scores for the scenario in which the specification of the inputs/outputs to be included in the DEA model is modelled with a probability distribution. This proba- bilistic approach allows us to obtain three different robust efficiency scores: the Conditional Expected Score, the Unconditional Expected Score and the Expected score under the assumption of Maximum Entropy principle. The calculation of the three efficiency scores involves the resolution of an exponential number of linear problems. The algorithm presented in this paper allows to solve over 200 millions of linear problems in an affordable time when considering up 20 inputs/outputs and 200 DMUs. The approach proposed is illustrated with an application to the assessment of professional tennis players

EVALUATION OF IRANIAN SMALL AND MEDIUM-SIZED INDUSTRIES USING THE DEA BASED ON ADDITIVE RATIO MODEL – A REVIEW

Data Envelopment Analysis (DEA) is a prominent procedure in the decision-making process with a pivotal role in the sustainable development assay. Project identification is the first step of sustainability assessment in the Environmental Impact Assessment (EIA) program for the industrial projects prior to complete establishment. The present review research comprised 405 Iranian industries assessment regarding both input and output criteria via DEA integrated with the ratio model of Additive Ratio ASsessment (ARAS) and weighing systems of Kendall and Friedman's tests supported by SPSS software. The findings deployed a classification for Iranian industries pertaining to industries' nominal capacity in certain clusters. Also, the current review paved the pathway towards executing both energy and materials streams in industries

Using data envelopment analysis to support best-value contractor selection

Selecting an appropriate contractor or supplier is essential to the successful implementation of a public procurement project. The Taiwan government frequently applies the best-value (BV) tendering method, a multi-criteria evaluation method, to procure projects. However, the selection process of the winner for a BV-based procurement project is generally subjective and thus is easily accused of corruptions. To develop a systematic method to support contractor selection, this study proposes using the Data Envelopment Analysis (DEA) to facilitate the criteria evaluations for each bidder during the short-listing stage. The evaluation results of using the DEA are a list of potential BV winners who are then suggested to enter into the final selection stage. Based on three case studies related to service procurement projects, this research finds that the DEA is suitable of assessing the relative efficiencies among bidders when the BV approach is applied. Lessons learned here should be helpful in applying the DEA to aid bid evaluations in other supplier selection problems. First published online: 24 Aug 201

Technology Trajectory Mapping Using Data Envelopment Analysis: The Ex-ante use of Disruptive Innovation Theory on Flat Panel Technologies

In this paper, we propose a technology trajectory mapping approach using Data Envelopment Analysis (DEA) that scrutinizes technology progress patterns from multidimensional perspectives. Literature reviews on technology trajectory mappings have revealed that it is imperative to identify key performance measures that can represent different value propositions and then apply them to the investigation of technology systems in order to capture indications of the future disruption. The proposed approach provides a flexibility not only to take multiple characteristics of technology systems into account but also to deal with various tradeoffs among technology attributes by imposing weight restrictions in the DEA model. The application of this approach to the flat panel technologies is provided to give a strategic insight for the players involved

Efficiency assessment of green technology innovation of renewable energy enterprises in China: a dynamic data envelopment analysis considering undesirable output

The rapid development of renewable energy enterprises has produced important benefits for contemporary efforts to address serious environmental pollution and depletion of fossil energy resources. However, the environmental pollution that exists in the production and operation of enterprises has been ignored, and so an objective evaluation of this issue is becoming urgent. This paper established an evaluation index system for green technology innovation efficiency and used dynamic data envelopment analysis (DEA) considering undesirable output to measure the green technology innovation efficiency of renewable energy enterprises, and the improvement potential of ineffective enterprises was put forward. The results show that: (1) the green technology innovation of renewable energy enterprises needs to be greatly improved. The average efficiency score of sample was 0.385 over four years, and only 16 enterprises were found to operate effectively; (2) when effective and inefficient DMUs were compared, the latter were found to have significant output shortfalls, especially in environmental tax, and were found to show an improvement potential of 55.71 percent; (3) the efficiency analysis of different types of renewable energy enterprises found that the green technology innovation efficiency score of nuclear energy enterprises was the highest, and rapidly rose; (4) the green technology innovation efficiency of renewable energy enterprises in the western region greatly exceeded the efficiency of the eastern and central regions. The efficiency evaluation results could not only provide a guidance for central and local governances to optimize the structure of renewable energy sector, but also potentially provide a reference for the operation and management of renewable energy enterprises in China

Input Substitutability in English Higher Education

This paper investigates input substitutability in English higher education and compares merging and non-merging institutions. A stochastic frontier translog output distance function is estimated using a thirteen-year panel of data for all institutions in England. Some differences between merging and non-merging institutions in labour and capital substitutability are revealed, and administrative input becomes an abundant resource for merged institutions. Policy implications are discussed

Operational research and artificial intelligence methods in banking

Supplementary materials are available online at https://www.sciencedirect.com/science/article/pii/S037722172200337X?via%3Dihub#sec0031 .Copyright © 2022 The Authors. Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results

Composite indicators in energy and environmental modeling

Ph.DDOCTOR OF PHILOSOPH

Adapting image processing and clustering methods to productive efficiency analysis and benchmarking: A cross disciplinary approach

This dissertation explores the interdisciplinary applications of computational methods in quantitative economics. Particularly, this thesis focuses on problems in productive efficiency analysis and benchmarking that are hardly approachable or solvable using conventional methods. In productive efficiency analysis, null or zero values are often produced due to the wrong skewness or low kurtosis of the inefficiency distribution as against the distributional assumption on the inefficiency term. This thesis uses the deconvolution technique, which is traditionally used in image processing for noise removal, to develop a fully non-parametric method for efficiency estimation. Publications 1 and 2 are devoted to this topic, with focus being laid on the cross-sectional case and panel case, respectively. Through Monte-Carlo simulations and empirical applications to Finnish electricity distribution network data and Finnish banking data, the results show that the Richardson-Lucy blind deconvolution method is insensitive to the distributio-nal assumptions, robust to the data noise levels and heteroscedasticity on efficiency estimation. In benchmarking, which could be the next step of productive efficiency analysis, the 'best practice' target may not perform under the same operational environment with the DMU under study. This would render the benchmarks impractical to follow and adversely affects the managers to make the correct decisions on performance improvement of a DMU. This dissertation proposes a clustering-based benchmarking framework in Publication 3. The empirical study on Finnish electricity distribution network reveals that the proposed framework novels not only in its consideration on the differences of the operational environment among DMUs, but also its extreme flexibility. We conducted a comparison analysis on the different combinations of the clustering and efficiency estimation techniques using computational simulations and empirical applications to Finnish electricity distribution network data, based on which Publication 4 specifies an efficient combination for benchmarking in energy regulation.  This dissertation endeavors to solve problems in quantitative economics using interdisciplinary approaches. The methods developed benefit this field and the way how we approach the problems open a new perspective