Kazim Baris Atici

A Multiple Criteria Energy Decision Support System

Abstract Multiple Criteria Decision Analysis (MCDA) techniques are particularly suitable for Energy and Environment (E&E) decisions due to multiple criteria nature of these types of problems and they have been widely applied in this area. Main points that can be taken into consideration when examining the MCDA literature related to E&E are the selection of appropriate method, selection of criteria to be used and the widely distributed application areas. The motivation behind this study is designing such a Decision Support System (DSS) that enables the energy decision makers to perform analysis using different methods of MCDA and different criteria from various application areas and test the applicability of the system through an application in Turkish Energy Sector. The system includes several methods of MCDA and common parameters from previous studies in order to provide different approaches of evaluating decision problems. With this feature, the system stands on the intersection of MCDA and E&E fields. ...

Mixed partial elasticities in constant returns-to-scale production technologies

Recently, Podinovski and Forsund (2010) developed a linear programming approach to the analysis and calculation of a class of mixed partial elasticity measures in variable returns-to-scale (VRS) production technologies. In the current paper, we extend their approach to the constant returns-to-scale (CRS) technologies and formulate linear programs required for the computation of elasticity measures. Among other results obtained in this paper, we prove a new result, valid in both VRS and CRS technologies, that allows us to identify the reason why the corresponding elasticity measure is undefined at the unit. This removes the need for a preliminary sorting of the units into those units where the elasticity measure applies and those where it does not.

Marginal Values and Returns to Scale for Nonparametric Production Frontiers

We present a unifying linear programming approach to the calculation of various directional derivatives for a very large class of production frontiers of data envelopment analysis (DEA). Special cases of this include different marginal rates, the scale elasticity, and a spectrum of partial and mixed elasticity measures. Our development applies to any polyhedral production technology including, to name a few, the conventional variable and constant returns-to-scale DEA technologies, their extensions with weight restrictions, technologies with weakly disposable undesirable outputs, and network DEA models. Furthermore, our development provides a general method for characterization of returns to scale (RTS) in any polyhedral technology. The new approach effectively removes the need to develop bespoke models for the RTS characterization and calculation of marginal rates and elasticity measures for each particular technology.

A multiple criteria sorting methodology with multiple classifi cation criteria and an application to country risk evaluation

Abstract In this paper, we propose an extension of the standard UTADIS methodology, an approach that originates from multicriteria decision aid (MCDA) for sorting problems, such that it can handle more than one classification criteria simultaneously which possibly involves different predefined classes for alternatives. Moreover, we test the classification ability of the standard UTADIS methodology using the out-of-classification criterion approach, a new variant of the studies comprising out-of-time and out-of-sample testing methodologies. Results obtained in out-of-classification criterion testing are then compared with the classification ability of the Multiple Classification Criteria UTADIS (MCC UTADIS). Finally, an application to country risk evaluation is performed. In this application, classifications of two credit rating agencies, Standard & Poors and Moodys, are taken as two different classification criteria. Moreover, robustness of MCC UTADIS method is tested through using several data sets. Re...

The measurement of agricultural productivity change in OECD countries with Fuzzy data

In this paper, we aim to measure agricultural productivity change of 34 OECD countries between years 1990 and 2014. The methods employed are data envelopment analysis (DEA) and malmquist productivity index (MPI). DEA is a relative efficiency method in a production technology, whereas MPI is based on DEA to measure the changes in the production technology over time. Our challenge is the existence of missing data points over the years in the initial dataset, which correspond to approximately 9% of the data. Removing units, factors or years with missing data as commonly practiced in DEA, would cause loss of information and makes it very difficult to draw conclusions in such a macro-data. We present the idea of using averages of available data points for a given factor and average variations over the years in those data to produce intervals for the missing points and handle the problem without any dimension reduction in the available data. Fuzzy DEA approach is employed using the calculated factor-specific intervals followed by MPI calculations to conduct a productivity change analysis. We suggest and empirically illustrate that instead of narrowing down the scope of the analysis by excluding the points missing, applying fuzzy approaches is an option worth considering by which it can be possible to make the best out of the available information. The results of the analysis are interpreted with respect to years, countries, regions and economic size of the countries.

Robust DEA Approaches to Performance Evaluation of Olive Oil Production Under Uncertainty

In this chapter, we are concerned with performance evaluation of olive oil production using Data Envelopment Analysis (DEA) under uncertainty. In order to measure production efficiency of olive-growing farms, we first apply an imprecise DEA approach by taking into account optimistic and pessimistic perspectives on uncertainty realized in olive oil production yield. We then consider robust optimization based DEA under an uncertainty set where the random data belong. The robust DEA model enables to adjust level of conservatism that is defined by the price of robustness of the uncertainty set. The performance of imprecise and robust DEA models is illustrated via a case study of olive-growing farms located in the Aegean Region of Turkey. The numerical experiments reveal that the efficiency scores and efficiency discriminations dramatically depend on how the uncertainty is treated both in imprecise and robust DEA modeling. There exists a trade-off between the protection level and conservatism of the efficiency scores.