scholarly journals Ranking the Performance of Universities: The Role of Sustainability

2021 ◽  
Vol 13 (23) ◽  
pp. 13286
Author(s):  
Christoph Burmann ◽  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Higher Education ◽  
Sensitivity Analysis ◽  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Teaching And Research ◽  
Sustainability Criteria ◽  
Goal Programming Model

University rankings assess the performance of universities in various fields and aggregate that performance into a single value. In this way, the aggregate performance of universities can be easily compared. The importance of rankings is evident, as they often guide the policy of Higher Education Institutions. The most prestigious multi-criteria rankings use indicators related to teaching and research. However, many stakeholders are now demanding a greater commitment to sustainable development from universities, and it is therefore necessary to include sustainability criteria in university rankings. The development of multi-criteria rankings is subject to numerous criticisms, including the subjectivity of the decision makers when assigning weights to the criteria. In this paper we propose a methodology based on goal programming that allows objective, transparent and reproducible weighting of the criteria. Moreover, it avoids the problems associated with the existence of correlated criteria. The methodology is applied to a sample of 718 universities, using 11 criteria obtained from two prestigious university rankings covering sustainability, teaching and research. A sensitivity analysis is carried out to assess the robustness of the results obtained. This analysis shows how the weights of the criteria and the universities’ rank change depending on the λ parameter of the goal programming model, which is the only parameter set by the decision maker.

scholarly journals A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 459
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Central Tendency ◽  
Performance Variables ◽  
World University Rankings ◽  
Goal Programming Model ◽  
Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

Modeling asset allocation and liability composition for Indian banks

2014 ◽  
Vol 40 (7) ◽  
pp. 700-723 ◽  
Author(s):  
P.K. Viswanathan ◽  
M. Ranganatham ◽  
G. Balasubramanian
Keyword(s):  
Sensitivity Analysis ◽  
Goal Programming ◽  
Asset Allocation ◽  
Banking Sector ◽  
Programming Model ◽  
Balance Sheet ◽  
Model Framework ◽  
Content Type ◽  
Goal Programming Model ◽  
Indian Banks

Purpose – Asset liability management is a multi-dimensional set of activities. Against this backdrop, the purpose of this paper is to build a goal programming model for optimally determining the asset allocation and liability composition for Indian Banks. Design/methodology/approach – The conceptual model framework has been developed and then tested for four banks that typically represent the Indian banking sector. Published balance sheet data were used for the model that span over 1995-2009. The veracity of the model has been tested in terms of its ability to project the optimum asset allocation and liability composition for the year 2010. Findings – The model has been able to generate the optimum asset and liability mix that meets the goals set on the key drivers. The solution provided is realistic and compatible with the actual figures. Sensitivity analysis including current and savings account and interest rate changes has been successfully performed to study impact they cause on profitability. Research limitations/implications – The model provides an overall approach to asset allocation and liability composition based on past data reflecting the preferences and priorities of the banks with regard to their outlook on setting targets. This may change. The variables like return and risk are stochastic in nature. Practical implications – The model demonstrated in this paper would be useful to the policy makers in any bank for decision support and planning in view of its ability to incorporate a large number of constraints. Changes in profit could be instantaneously captured through sensitivity analysis. Originality/value – The goal programming model used here is invariant to the type of bank and year of consideration.

scholarly journals Fuzzy Goal Programming Procedure to Bilevel Multiobjective Linear Fractional Programming Problems

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Mahmoud A. Abo-Sinna ◽  
Ibrahim A. Baky
Keyword(s):  
Goal Programming ◽  
Fractional Programming ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Decision Makers ◽  
Membership Functions ◽  
Linear Fractional Programming ◽  
Goal Programming Model ◽  
Fuzzy Goals ◽  
Fuzzy Goal

This paper presents a fuzzy goal programming (FGP) procedure for solving bilevel multiobjective linear fractional programming (BL-MOLFP) problems. It makes an extension work of Moitra and Pal (2002) and Pal et al. (2003). In the proposed procedure, the membership functions for the defined fuzzy goals of the decision makers (DMs) objective functions at both levels as well as the membership functions for vector of fuzzy goals of the decision variables controlled by first-level decision maker are developed first in the model formulation of the problem. Then a fuzzy goal programming model to minimize the group regret of degree of satisfactions of both the decision makers is developed to achieve the highest degree (unity) of each of the defined membership function goals to the extent possible by minimizing their deviational variables and thereby obtaining the most satisfactory solution for both decision makers. The method of variable change on the under- and over-deviational variables of the membership goals associated with the fuzzy goals of the model is introduced to solve the problem efficiently by using linear goal programming (LGP) methodology. Illustrative numerical example is given to demonstrate the procedure.

The double role of the weight factor in the goal programming model

2004 ◽  
Vol 31 (11) ◽  
pp. 1833-1845 ◽  
Author(s):  
Ossama Kettani ◽  
Belaı̈d Aouni ◽  
Jean-Marc Martel
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Weight Factor ◽  
Goal Programming Model

scholarly journals A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Said Ali El-Qulity ◽  
Ali Wagdy Mohamed
Keyword(s):  
Higher Education ◽  
Differential Evolution ◽  
Goal Programming ◽  
Capacity Planning ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Solution Quality ◽  
De Algorithm ◽  
Planning Approach ◽  
Goal Programming Model

This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.

scholarly journals Mathematical Nonlinear Goal Programming in Quality Control

2019 ◽  
pp. 1-8
Author(s):  
Safia M. Ezzat
Keyword(s):  
Quality Control ◽  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
Consumer’S Risk ◽  
Producer’S Risk ◽  
Acceptance Sampling Plans ◽  
Goal Programming Model ◽  
Level Of Significance ◽  
Quality Control Data

This paper concerned with applying suggested mathematical programming and nonlinear goal programming models to determine the producer's risk (α), consumer's risk (β) and acceptance level (c) simultaneously. The suggested nonlinear goal programming model allowed α and β values to be free and determined their values more accurately which make balance between the power of a statistical test (1-β) and level of significance α. Real quality control data are used to evaluate the performance of the suggested models . This enables decision makers in quality control to develop more accurate and free acceptance sampling plans.

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