Vincent Charles

Fuzzy rough DEA model: A possibility and expected value approaches

Uncertainty is certain in the world of uncertainty. Measuring the performance of any entity in such an uncertain environment is unavoidable. Fuzzy rough data envelopment analysis (FRDEA) provides a room to evaluate the relative efficiency of homogenous entities, widely known as decision making units (DMUs) in the data envelopment analysis (DEA) literature. This paper attempts to create a fuzzy rough DEA model by integrating the classical DEA, fuzzy set theory, and rough set theory, which apparently provide a way to accommodate the uncertainty. Moreover, in contrast to the probability approach, this paper provides a pavement to measure the relative efficiency of any given DMUs in line with the possibility approach along with the fuzzy rough expected value operator.

Measuring regional competitiveness through Data Envelopment Analysis: A Peruvian case

Abstract It is well known that competitiveness has a positive effect on long-term economic growth. Concerned, thus, with creating and maintaining an environment that sustains more value creation for its enterprises and more prosperity for its people, the goal of this research paper is to assist the Peruvian national and regional policy makers, business, and academic community in their endeavor to improve regional and national competitiveness by means of developing a methodology based on Data Envelopment Analysis (DEA) to measure and rank the competitiveness of all the regions of Peru. It is important to highlight that DEA is a method that has never been used before in the calculation of regional competitiveness and this research paper is the first of its kind in Peru to adapt this method to develop a regional competitiveness index. Results revealed that coastal regions are highly competitive when compared to the mountains and jungle regions. Because of the large differences in the competitiveness of the regions of Peru, the research results point out to the need for a unified approach in creating a development strategy and improving the competitiveness of all the regions of Peru.

An approach to find redundant objective function(s) and redundant constraint(s) in multi-objective nonlinear stochastic fractional programming problems

Structural redundancies in mathematical programming models are nothing uncommon and nonlinear programming problems are no exception. Over the past few decades numerous papers have been written on redundancy. Redundancy in constraints and variables are usually studied in a class of mathematical programming problems. However, main emphasis has so far been given only to linear programming problems. In this paper, an algorithm that identifies redundant objective function(s) and redundant constraint(s) simultaneously in multi-objective nonlinear stochastic fractional programming problems is provided. A solution procedure is also illustrated with numerical examples. The proposed algorithm reduces the number of nonlinear fractional objective functions and constraints in cases where redundancy exists.

Stochastic simulation-based genetic algorithm for chance constrained fractional programming problem

The field of chance constrained fractional programming (CCFP) has grown into a huge area over the last few years because of its applications in real life problems. Therefore, finding a solution technique to it is of paramount importance. The solution technique so far has been deriving deterministic equivalence of CCFP with random coefficients in the objective function and/or constraints and is possible only if random variable follows some specified distribution with known parameters. This paper presents a stochastic simulation-based genetic algorithm (GA) for solving CCFP problems, where random variables used can follow any continuous distribution. The solution procedure is tested on a few numerical examples. The results demonstrate that the suggested approach could provide researchers a promising way for solving various types of chance constrained programming (CCP) problems.

Evaluating the performance of indian banking sector using DEA during post-reform and global financial crisis

The purpose of the study is to examine the performance of Indian banking sector in terms of efficiency, returns to scale, and total factor productivity change. The technique of data envelopment analysis is applied due to its flexibility to incorporate multiple inputs and multiple outputs without any underlying assumption on the functional form. There is growing tendency of public sector banks operating under increasing returns to scale, implying that substantial gains could be obtained from altering scale via either internal growth or consolidation in the sector. In terms of productivity, the results show a positive change in both the sectors due to technological change, possibly as a result of adoption of latest technology and new business practices in post reform period. However, there is an evidence of shrink in the market and negative growth in productivity in both the sectors during the period of global financial crisis. The main contribution of the paper is to empirically provide the evidences to resolve the debate if the global financial crisis had any impact on the performance of banking sector in India.

Genetic algorithm for chance constrained reliability stochastic optimisation problems

This paper addresses the chance constrained reliability stochastic optimisation problem, in which the objective is to maximise system reliability for the given chance constraints. A problem specific stochastic simulationbased genetic algorithm (GA) is developed for finding optimal redundancy to an n-stage series system with m -chance constraints of the redundancy allocation problem. As GA is a proven robust evolutionary optimisation search technique for solving various reliability optimisation problems and the Monte Carlo (MC) simulation, which is a flexible tool for checking feasibility of chance constraints, we have effectively combined GA and MC simulation in the proposed algorithm. The effectiveness of the proposed algorithm is illustrated for a four-stage series system with two chance constraints.

A translation invariant pure DEA model

This communication complements the DEA model proposed by Lovell and Pastor (1999), by incorporating both positive and negative criteria in the model. As such, we propose a DEA model, known as pure DEA, using a directional distance function approach.

Measuring the attractiveness of academic journals

Various journal-ranking algorithms have been proposed, most of them based on citation counts. This article introduces a new approach based on the reciprocal direct influence of all pairs of a list of journals. The proposed method is assessed against an opinion-based ranking published in 2005 for 25 operations research and management science (OR/MS) journals, and five existing approaches based on citation counts. The results show a strong correlation with the opinion-based ranking.

Green exchange-traded fund performance appraisal using slacks-based DEA models

This paper appraises the performance of a sample of green exchange-traded funds (ETFs) using two types of data envelopment analysis (DEA) metrics. The first type is based on slacks-based DEA models, namely, the range-adjusted measure (RAM) and its variant the RAM-BCC model; the second type is based on a common set of weights of RAM. The appraisal is performed under the assumption that there are value stocks on the green equity market and the potential investors prefer ETFs that put emphasis on value stocks. In the first stage of the analysis, ETF efficiency ratings are derived, whereas in the second stage, ordinary least squares, censored Tobit, and bootstrapped-truncated regression are employed to model the fund ratings. The results are acceptable, indicating that four or five out of the sixteen sample funds depending on the model employed can be candidates for value investors. Moreover, although there is not much evidence for systematic effects of the beta coefficient on fund rating, the findings of the analyses entail implications for potential investors by using the models as an investment pick and for fund managers by considering the mitigation of risk and bringing higher selectivity to their portfolios.

Stochastic Fractional Programming Approach to a Mean and Variance Model of a Transportation Problem

In this paper, we propose a stochastic programming model, which considers a ratio of two nonlinear functions and probabilistic constraints. In the former, only expected model has been proposed without caring variability in the model. On the other hand, in the variance model, the variability played a vital role without concerning its counterpart, namely, the expected model. Further, the expected model optimizes the ratio of two linear cost functions where as variance model optimize the ratio of two non-linear functions, that is, the stochastic nature in the denominator and numerator and considering expectation and variability as well leads to a non-linear fractional program. In this paper, a transportation model with stochastic fractional programming (SFP) problem approach is proposed, which strikes the balance between previous models available in the literature.