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Decision-making is a process to find the best option when there are many available alternatives. Decision happens in our daily life, no matter how simple or difficult it is. The coverage of decision-making can be from a simple decision such as what to eat for breakfast to complicated business decisions affecting the future development of companies. In general, the bigger the impact is, the more related factors are involved in a decision. This makes decision-making become difficult and complicated. Recently, Chen, Huang, and Chang. (2015) did a great work “Using Summarization Techniques to Resolve the Multi-Criteria Decision Making Problems” that using summarization techniques solves MCDM problems. However, there is a weakness in their proposed algorithm, i.e., users need to predefine the numbers of alternative and criteria clusters before executing their algorithm to summarize information. Unfortunately, the numbers are difficult to decide in practice and it can affect the performance of summarization table in the end.
In this research, we first propose one heuristic algorithm to decide the numbers of clusters when summarizing decision tables. Next, we design a specific incremental method and apply it to the heuristic algorithm to improve the calculation efficiency. Finally, Genetic Algorithm – Roulette Wheel Selection is included into the algorithm to obtain better results. In experimental design and results, to verify the proposed approach, nine combinations of decision tables are used, each of them consisted of ten data sets. The results indicated that our algorithms can effectively and efficiently determine the numbers of alternative and criteria clusters when summarize the decision table. Moreover, a real case study is used to illustrate the effectiveness of the proposed approach. | en_US |