Journal Ratings and Their Consensus Ranking

Stefan Theussl () and Kurt Hornik ()
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Stefan Theussl: Dep. of Statistics and Mathematics
Kurt Hornik: Dep. of Statistics and Mathematics

Chapter 65 in Operations Research Proceedings 2008, 2009, pp 401-406 from Springer

Abstract: Summary In this paper we explore the possibility of deriving consensus rankings by solving consensus optimization problems, characterizing consensus rankings as suitable complete order relations minimizing the average Kemeny-Snell distance to the individual rankings. This optimization problem can be expressed as a binary programming (BP) problem which can typically be solved reasonably efficiently. The underlying theory is discussed in Sect. 1. Applications of the proposed method given in Sect. 2 include a comparison to other mathematical programming (MP) approaches using the data set of Tse [9] and establishing a consensus ranking of marketing journals identified by domain experts from a subset of the Harzing journal quality list [2]. In Sect. 3 we discuss computational details and present the results of a benchmark experiment comparing the performance of the commercial solver CPLEX to three open source mixed integer linear programming (MILP) solvers

Keywords: Mixed Integer Linear Programming; Social Choice Function; Individual Ranking; Operational Research Society; Journal Ranking (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-3-642-00142-0_65

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