A Multicriteria Goal Programming Model for Ranking Universities

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.

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Ranking the Performance of Universities: The Role of Sustainability

Sustainability ◽

10.3390/su132313286 ◽

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.

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Fuzzy Goal Programming Procedure to Bilevel Multiobjective Linear Fractional Programming Problems

International Journal of Mathematics and Mathematical Sciences ◽

10.1155/2010/148975 ◽

2010 ◽

Vol 2010 ◽

pp. 1-15 ◽

Cited By ~ 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.

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Group Decision Makers' Preferences Modelling within the Goal Programming Model: An Overview and a Typology

Journal of Multi-Criteria Decision Analysis ◽

10.1002/mcda.492 ◽

2012 ◽

Vol 19 (3-4) ◽

pp. 169-184 ◽

Cited By ~ 11

Author(s):

Michelle Munro ◽

Belaïd Aouni

Keyword(s):

Goal Programming ◽

Group Decision ◽

Programming Model ◽

Decision Makers ◽

Goal Programming Model

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Mathematical Nonlinear Goal Programming in Quality Control

Journal of Engineering Research and Reports ◽

10.9734/jerr/2019/v5i116914 ◽

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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