Using Harmonic Mean to Solve Multi-Objective Linear Programming Problems

In this paper, we have suggested a new technique to transform multi-objective linear programming problem (MOLPP) to the single objective linear programming problem by using Harmonic mean for values of function and an algorithm is suggested for its solution, the computer application of algorithm has been demonstrated by solving some numerical examples. We have used some other techniques, such as (sen, arithmetic mean, median) to solve the same problems, the results in Table 3 indicate that the new technique in general is promising.

Research on Methods of Parameter Estimation in Combining Forecasting Based on Harmonic Mean

Two kinds of parameter estimation methods (I) and (II) of combining forecasting based on harmontic mean are proposed and compared through a lot of simulation forecasting examples. A very helpful conclusion is obtained, which can lay solid foundations for correct application of the above methods.

The F-measure for Research Priority

Purpose： In this contribution we continue our investigations related to the activity index （A/） and its formal analogs. We try to replace the AI by an indicator which is better suited for policy applications. Design/methodology/approach： We point out that fluctuations in the value of the AI for a given country and domain are never the result of that country＇s policy with respect to that domain alone because there are exogenous factors at play. For this reason we introduce the F-measure. This F-measure is nothing but the harmonic mean of the country＇s share in the world＇s publication output in the given domain and the given domain＇s share in the country＇s publication output.Findings： The F-measure does not suffer from the problems the AI does Research limitations： The indicator is not yet fully tested in real cases R＆D policy management： In policy considerations, the AI should better be replaced by the F-measure as this measure can better show the results of science policy measures （which the AI cannot as it depends on exogenous factors）. Originality/value： We provide an original solution for a problem that is not fully realized by policy makers.

Bilateral Co-authorship Indicators Based on Fractional Counting

Purpose: In this contribution we provide two new co-authorship indicators based on fractional counting. Design/methodology/approach: Based on the idea of fractional counting we reflect on what should be an acceptable indicator for co-authorship between two entities. From this reflection we propose an indicator, the co-authorship score, denoted as cs, using the harmonic mean. Dividing this new indicator by the classical co-authorship indicator based on full counting, leads to a co-authorship intensity indicator.Findings: We show that the indicators we propose have many necessary or at least highly desirable properties for a proper cs-score. It is pointed out that the two new indicators can be used for countries, but also for institutions and other pairs of entities. A small example shows the feasibility of the co-authorship score and the co-authorship intensity indicator.Research limitations: The indicators are not yet tested in real cases.Practical implications: As the notions of co-authorship and collaboration have many aspects, we think that our contribution may help policy management to take yet another aspect into account as part of a multi-faceted description of research outcomes.Originality/value: The indicators we propose cover yet another aspect of co-authorship.