Kayoko Murakami

A solution of human resource allocation problem in a case of hotel management

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. This human resource allocation problem (hRAP) has two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decision-based genetic algorithm (P-mdGA) to solve it. During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach was used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we used fuzzy logic controller for fine-tuning genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA was applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

Earthquake's influence on inbound tourism: voices from the travel blogs

Since the 2011 earthquake off the Pacific coast of Tohoku damaged the major area of eastern Japan on March 11, 2011, the number of foreign visitors has drastically decreased. The Japanese government tried to take urgent countermeasures against the loss; they spread the true information that Japan is safe by using media and Word-of-Mouth (WOM) such as influential bloggers. In this study, we investigate what WOM can be found regarding the earthquake through the blogs posted after the disaster. The blogs including earthquakerelated words were collected and analyzed. Extracting the target data from the large amount of data allows us to qualitatively examine the data. The text mining approach we used in this study includes morphological analysis and topic words analysis with term frequency-inverse document frequency weight. After that, the topic words and the words with high document frequency were used to find a collocation of those topic words. As a result, the topic words “safe” and “normal” were found in the March 2011 blogs while they contained many other topic words about earthquake, tsunami, and nuclear crises. Moreover, gradually the number of earthquake-related words disappeared in the blogs and travelrelated words appeared as time went by. In addition, we used concordance software to see the context where the topic words are used and found that the blogs written by the foreign visitors could convey true information about the disaster.