Course on Performance Measurement using DEA, at Aston University, June 20-21, 2016

Performance Measurement Using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) at Aston University, UK

Course on Performance Measurement using Data Envelopment Analysis (DEA)
Date: 20 and 21 June 2016

Apart from the classical DEA models under constant and variable returns to scale, the course will cover models for assessing productivity change over time, for incorporating value judgements in DEA, for target setting and exploiting economies of scale, for dealing with cases where some of the variables obey constant and others varying returns to scale etc. The course will use various areas of application drawn from the interests of the participants to illustrate the models. The PIM DEA software will be used for hands on?sessions by participants. All delegates will receive 15 days free licence for PIM-DEA software.

Course on Econometric Methods and Stochastic Frontier Analysis (SFA)
Date: 22 and 23 June 2016

Participants will be introduced to the key underlying concepts of econometric methods in the context of efficiency and productivity analysis and to the types of information on performance that can be derived. Participants will also be introduced to the limitations of each approach and given advice how to use the methods appropriately, avoiding pitfalls. The course will cover Production and Cost functions, Corrected OLS regression (COLS), Modified OLS regression (MOLS), Stochastic Frontier Analysis (SFA) and panel data methods for efficiency and productivity analysis. Lectures will normally be followed by hands on sessions using sample data worked on with appropriate software.

Who should participate?
Those interested in assessing performance of organisational units, for instance, banking, health services , education, regulation, and governance.

How is the programme delivered?
The teaching is based on lectures, small group working and hands-on use of software.

Fees, accommodation and reserving a place

Reserving a place: Payment is required in full prior to the commencement of the course and can be made by credit card.

  Students Non-students
DEA – course (2 days)
June 20 – 21, 2016
£600
Register*
£1100
Register*
SFA – course (2 days)
June 22 – 23, 2016
£600
Register*
£1100
Register*
Special price if you book both DEA and SFA Courses (4 days)
June 20 – 23, 2016
£1000
Register*
£2000
Register*
Fee covers all teaching materials, coffee breaks and lunch
Deadline for reserving a place is June 15, 2016.
*By registering you accept you have read and agree to the “Short Course Terms and Conditions

Accommodation: Accommodation is not included; there is accommodation in campus or nearby. For information please visit: http://www.conferenceaston.co.uk/book-hotel-room/

For more information on course content or registration please contact Prof Emmanuel Thanassoulis (e.thanassoulis@aston.ac.uk).

 

Call for PAPERS: DEA at OR58, September 2016, University of Portsmouth, UK

Call for PAPERS: DEA at OR58:
Data Envelopment Analysis (DEA) stream at the OR Society Annual UK Conference

6th – 8th, September 2016, University of Portsmouth, UK,
http://www.DEAzone.com/DEAatOR58/

Abstract submission: http://www.theorsociety.com/Pages/Conferences/OR58/OR58Abstract.aspx

Dear Colleague,

I would like to issue an invitation to take part in the Data Envelopment Analysis and Performance Measurement Stream of the OR58 Conference. This conference will be held at the University of Portsmouth, Portsmouth, UK from 6th to 8th September 2016. Contributions from both the academic and the non-academic communities are welcome.

This stream covers papers on the theme of efficiency and productivity analysis and performance management. Both parametric and non-parametric papers will be considered. We especially welcome papers on the theory, methodology and application of Data Envelopment Analysis and econometric methods in performance management. Of particular interest are successful applications of performance and efficiency analysis in the real world, for example in banking, healthcare, education, transportation, and so on.

If you are interested in making a presentation, please submit a title of your talk no later than 15th June 2016 at www.theorsociety.com/or58.

Deadline for submission abstract:    June 30, 2016
Deadline for early registration fee:   June 30, 2016
Conference date:                                          September 6-8, 2016
Conference Venue:                                      University of Portsmouth, UK

Abstract submission: http://www.theorsociety.com/Pages/Conferences/OR58/OR58Abstract.aspx

Kind regards,

Ali Emrouznejad,
Professor of Business Analytics
Operations & Information Management Group

Aston Business School
Aston University
Birmingham B4 7ET, UK

[Data Envelopment | DEA Software | Research Gate]

XV EUROPEAN WORKSHOP ON EFFICIENCY AND PRODUCTIVITY ANALYSIS (EWEPA) LONDON, JUNE 12-15 2017

The Centre for Productivity and Performance (CPP) and the School of Business and Economics of Loughborough University, UK, are organising the XV European Workshop on Efficiency and Productivity Analysis (EWEPA) in London, on June 12-15, 2017.

EWEPA is a major biennial conference on the topics of productivity, efficiency and performance analysis. We expect a very large number of participants to attend, from a wide international community. Presentations are invited on the theory and application of production economics, econometrics, statistics, management science and operational research to various problems in the areas of productivity and efficiency. All popular techniques and methodologies will be represented, including stochastic frontier analysis, data envelopment analysis, bootstrapping approaches, and many more.

THE ORGANISING COMMITTEE
Victor Podinovski, David Saal, Robin Sickles, Anthony Glass, Karligash Glass (Kenjegalieva) The official conference website www.ewepa.org will be functional by Autumn 2016 and will include details for abstract submission and registration. Please check our website for regular updates. In the meantime, please forward any queries to our contact person, Dr Anthony Glass at A.J.Glass@lboro.ac.uk.

VENUE
The conference will be held at the historic Art Deco Senate House building in Central London, Malet Street, London WC1E 7HU. This is next to the renowned British Museum and is within easy walking distance of the main London attractions.

JOURNAL OF PRODUCTIVITY ANALYSIS
is the official journal of EWEPA XV. The editorial board of JPA invites submissions to the journal on all topics of productivity and efficiency analysis. EWEPA participants are welcome to discuss their publication  ideas with the editorial board members during the conference.

For more information please visit www.ewepa.org

Special issue: RAIRO – Operations Research on Fuzzy Data Envelopment Analysis


RAIRO – Operations Research :
Special issue on  Fuzzy Data Envelopment Analysis: Recent Developments and Applications

(download PDF version of this call here)

Guest editor:
Ali Emrouznejad, Aston Business School, Aston University , Birmingham, UK
Joshua Ignatius, School of Mathematical Sciences, Universiti Sains Malaysia

The field of performance measurement with fuzzy and imprecise data is continuing to grow. Linear programming in general and Data Envelopment Analysis (DEA) in particular have always played a vital role in measuring efficiency and productivity of Decision Making Units. Today, the observed values of the input and output data in many real-world problems are imprecise or vague. Operations Research Models and fuzzy sets have shown tremendous potential in dealing with imprecise, vague and uncertain data.

For this reason, we have taken the initiative to edit a special issue on “Fuzzy Data Envelopment Analysis: Recent Developments and Applications”, which draws on two branches of literature: 1. Fuzzy concepts for its ability to account for imprecision, and 2. Data Envelopment Analysis (DEA) for its vital role in modeling efficiency and productivity.  This special issue aims at providing a collection of recent and state-of-the-art contributions, including novel integration between fuzzy concepts and DEA. Both theoretical and practical papers using advanced models in fuzzy Data Envelopment Analysis are considered.

Manuscript Preparation

Please refer to the Journal Information and Author Guidelines for additional information on manuscript preparation at http://www.rairo-ro.org/author-information

All manuscripts should be submitted online at http://www.editorialmanager.com/ro/, when submitting the manuscript please select the article type “SI: Fuzzy Data Envelopment Analysis

Important Dates

September 31, 2016 Submission Deadline
(early submission recommended, referee process starts once the paper received, accepted papers will be published individually online as they are accepted)
December 31,  2016 Notification of status & acceptance or invitation for revision of paper
March 31, 2017 Revised manuscripts
May 31, 2017 Final version of paper
                         2017 Anticipated print publication

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere.

Reviewing

Submitted papers will be peer-reviewed in the same manner as any other submission to a leading international journal. The major acceptance criterion for a submission is the quality and originality of the contribution.

Guest Editor:

Ali Emrouznejad
Professor and Chair in Business Analytics,
Operations & Information Management Group ,
Aston Business School, Aston University ,
Birmingham, UK
Joshua Ignatius
Associate Professor of Operations Research,
School of Mathematical Sciences,
Universiti Sains Malaysia,
Malaysia

Zervopoulos, P. D., T. S. Brisimi , A. Emrouznejad, G. Cheng (2016) “Assessing Productive Efficiency of Banks Using integrated Fuzzy-DEA and bootstrapping: A Case of Mozambican Banks”, European Journal of Operational Research, 250 (1) 262–272.

Zervopoulos, P. D., T. S. Brisimi , A. Emrouznejad, G. Cheng (2016) “Assessing Productive Efficiency of Banks Using integrated Fuzzy-DEA and bootstrapping: A Case of Mozambican Banks”, European Journal of Operational Research, 250 (1) 262–272.

In this study we develop a DEA–based performance measurement methodology that is consistent with performance assessment frameworks such as the Balanced Scorecard. The methodology developed in this paper takes into account the direct or inverse relationships that may exist among the dimensions of performance to construct appropriate production frontiers. The production frontiers we obtain are deemed appropriate as they consist solely of units with desirable levels for all dimensions of performance. These levels should be at least equal to the critical values set by decision makers. The properties and advantages of our methodology against competing methodologies are presented through a numerical example and comparative analysis. This analysis explains the failure of existing studies to define appropriate production frontiers when directly or inversely related dimensions of performance are present.

[Download1   or   Download2]

Safdar, K. J., A. Emrouznejad, P. K. Dey (2015). “Assessing the Queuing Process Using Data Envelopment Analysis: An Application in Health Centres.” Journal of Medical Systems 40(1):32-45.

Safdar, K. J., A. Emrouznejad, P. K. Dey (2015). “Assessing the Queuing Process Using Data Envelopment Analysis: An Application in Health Centres.” Journal of Medical Systems 40(1):32-45.

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system.  The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

[Download1   or   Download2]

Wank, P. F., C. Barros, A. Emrouznejad (2015) “Assessing Productive Efficiency of Banks Using integrated Fuzzy-DEA and bootstrapping: A Case of Mozambican Banks”, European Journal of Operational Research, 249 (1) 378–389.

Wank, P. F., C. Barros, A. Emrouznejad (2015) “Assessing Productive Efficiency of Banks Using integrated Fuzzy-DEA and bootstrapping: A Case of Mozambican Banks”, European Journal of Operational Research, 249 (1) 378–389.

Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we propose new Fuzzy-DEA ?-level models to assess underlying uncertainty. Further, bootstrap truncated regressions with fixed factors are used to measure the impact of each model on the efficiency scores and to identify the most relevant contextual variables on efficiency. The proposed models have been demonstrated using an application in Mozambican banks to handle the underlying uncertainty. Findings reveal that fuzziness is predominant over randomness in interpreting the results. Additionally, fuzziness can be used by decision-makers to identify missing variables to help in interpreting the results. Price of labor, price of capital, and market-share were found to be the significant factors in measuring bank efficiency. Managerial implications are addressed.

[Download1   or   Download2]

Toloo M. , A. Zandi and A. Emrouznejad (2015), “Evaluation Efficiency of Large Scale Data Set with Negative Data: An Artificial Neural Networks Approach,” Journal of Supercomputing 71(7): 2397–2411.

Toloo M. , A. Zandi and  A. Emrouznejad (2015), “Evaluation Efficiency of Large Scale Data Set with Negative Data: An Artificial Neural Networks Approach,” Journal of Supercomputing 71(7): 2397–2411.

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large scale data set especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network (ANN) and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore it can be considered as a useful tool for measuring the efficiency of DMUs with (large scale) negative data.

[Download1   or  Download2]

Indra Widiarto and A. Emrouznejad (2015) “Social and financial efficiency of Islamic microfinance institutions: A Data Envelopment Analysis application”. Socio-Economic Planning Sciences, 50 (1): 1-17.

Indra Widiarto and A. Emrouznejad (2015) “Social and financial efficiency of Islamic microfinance institutions: A Data Envelopment Analysis application”. Socio-Economic Planning Sciences, 50 (1): 1-17.

Microfinance has been developed as alternative solution for global poverty alleviation effort in the last 30 years. Microfinance institution (MFI) has unique characteristic wherein they face double bottom line objectives of outreach to the poor and financial sustainability. This study proposes a two-stage analysis to measure Islamic Microfinance institutions (IMFIs) performance by comparing them to conventional MFIs. First, we develop a Data Envelopment Analysis (DEA) framework to measure MFIs’ efficiency in its double bottom line objectives, i.e. in terms of social and financial efficiency. In the second stage non-parametric tests are used to compare the performance and identify factors that contribute to the efficiency of IMFIs and MFIs.

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Ghasemi M.R., J. Ignatius, S. Lozano, A. Emrouznejad, A. Hatamimarbini (2015) A fuzzy expected value approach under generalized data envelopment analysis, Knowledge-Based Systems, 89: 148–159.

Ghasemi M.R., J. Ignatius, S. Lozano, A. Emrouznejad, A. Hatamimarbini (2015) A fuzzy expected value approach under generalized data envelopment analysis, Knowledge-Based Systems, 89: 148–159.

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes a fuzzy expected generalized DEA model, which can treat fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models in a unified way that handles both symmetrical and asymmetrical fuzzy numbers. We also considered super-efficiency evaluation problems, which is always feasible and it can be suggested as a way in dealing with infeasibility problems. The proposed method can be perceived as a form of aggregating solutions across a range of ?-levels. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. An application of energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.

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