Multi-criteria benchmarking: ARWU 2020

This paper proposes a novel framework to support the generation of strategies for multi-criteria long-term improvement. It can be applied to general preference models but it is illustrated in this article on a Multi-Attribute Value Theory model. The novel contributions to the literature are twofold. Firstly, the framework addresses the issue of resistance to change that may arise during the implementation of a strategy. It constrains a step of improvement to be focused on a single criterion, and minimizes the intensity of operational changes. Secondly, it addresses the realism of the improvement scenarios by treating three types of structural dependencies differently: the positive synergies, the negative synergies and the bottlenecks. The scenarios are generated by finding a set of efficient solutions to a shortest path problem in a graph whose edges represent possible steps of improvement. Each edge is characterized by an increase of rank or level and two penalty functions relating to the difficulty of its execution, one representing a risk associated to bottleneck mechanisms and the other to operational change relative to a previous edge. A case-study using the Shanghai Academic Ranking of World Universities is presented in order to illustrate how this framework could be useful to generate a sequence of strategic actions for the Université libre de Bruxelles.

The framework based on multi-criteria benchmarking in order to help generate strategic action plans developed in the article (J.P. Hubinont & Y. De Smet, Long-term Multi-Criteria Improvement Planning - Decision Support System) is provided as an executable file : "MCDMBM.exe". It should work fine on Windows OS 64 bits. The case-study presented in the article uses parameters values (contained in a configuration file: "Article_cfg.txt") and a data set (ARWU2020.xlsx). It is possible to load the configuration file into the software with the menu :File>Open. Then one can go in the tab 'Paths' and click on 'Generate paths'. It allows the user/scientist to reproduce the results of the article in a few clicks. Moreover, the python files used to generate the executable files are also provided for any scientist to be able to adapt it and be helped in its research work.