Decision support for proposal grouping: A hybrid approach using knowledge rule and genetic algorithm

Authors:
Zhi-Ping Fan;Yuan Chen;Jian Ma;Yan Zhu
Affiliations:
Department of Management Science and Engineering, School of Business Administration, Northeastern University, Shenyang 110004, China;Department of Management Science and Engineering, School of Business Administration, Northeastern University, Shenyang 110004, China and Department of Information Systems, City University of Hong ...;Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong;Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Proposal grouping is a special procedure in the sponsorship process for research projects. In practice, it is conducted to simplify the following procedure of reviewer assignment. As the proposals grow, this procedure becomes complex. Practical managers spend an increasing amount of time struggling for identifying valid proposals, classifying proposals and partitioning proposals into groups as well as maintaining some control over the quality and composition of the resulting groups. This paper proposes an approach for proposal grouping, in which knowledge rules are designed to deal with proposal identification and proposal classification, and the genetic algorithm is developed to search for the expected groupings. In addition, a corresponding system is designed and developed to support the proposed approach. Compared to the previous manual grouping, the proposed approach significantly reduces the time required for grouping, ensures more diverse group composition, and increases overall grouping quality.