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Mathematical Problems in Engineering
Volume 2015, Article ID 186970, 15 pages
http://dx.doi.org/10.1155/2015/186970
Research Article

To Make Good Decision: A Group DSS for Multiple Criteria Alternative Rank and Selection

1Graduate Institute of Information Management, National Taipei University of Technology, Taiwan
2Department of Management Information Systems, National Chengchi University, Taiwan

Received 26 September 2014; Accepted 7 January 2015

Academic Editor: Jianming Shi

Copyright © 2015 Chen-Shu Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Decision making is a recursive process and usually involves multiple decision criteria. However, such multiple criteria decision making may have a problem in which partial decision criteria may conflict with each other. An information technology, such as the decision support system (DSS) and group DSS (GDSS), emerges to assist decision maker for decision-making process. Both the DSS and GDSS should integrate with a symmetrical approach to assist decision maker to take all decision criteria into consideration simultaneously. This study proposes a GDSS architecture named hybrid decision-making support model (HDMSM) and integrated four decision approaches (Delphi, DEMATEL, ANP, and MDS) to help decision maker to rank and select appropriate alternatives. The HDMSM consists of five steps, namely, criteria identification, criteria correlation calculation, criteria evaluation, critical criteria selection, and alternative rank and comparison. Finally, to validate the proposed feasibility of the proposed model, this study also conducts a case study to find out the important indexes of corporate social responsibility (CSR) from multiple perspectives. As the case study demonstrates the proposed HDMSM enables a group of decision makers to implement the MCDM effectively and help them to analyze the relation and degree of mutual influence among different evaluation factors.