@inproceedings{Hsieh2018ICIM,
  title={Applying Parallel Association Algorithms to Value Meal Design for a Chinese Fast Food Chain Restaurant},
  author={Chi-Bin Cheng, Liam Y. Hsieh, and Yu-Chung Su},
  booktitle={International Conference on Innovation and Management, Chiang Mai, Thailand, July 10-13, 2018.},
  year={2018},
  ABSTRACT ={The case company of this study is a Chinese fast food chain restaurant. To enhance its operating efficiency, the company\'s tactics are to encourage the expenditure by customer per transaction and to improve the service speed by serving more value meals (i.e. combo) to customers. The design of the company’s value meal is based on some fixed base items coupled with main dishes. To implement this operational policy, the company must confirm that the base items for value meals meet customer preferences, as well as appropriate prices. This study utilizes the POS data to find implicit information regarding customer preferences by the association analysis between individual items. Considering the fast growth of POS data in the future, we adopt Hadoop as the computing platform, and use parallel FP-Growth algorithm for association analysis. Two tasks are carried out based on the association analysis: 1) combining weather and POS data, the resulting association rules provide information regarding popular products under different weather information, and such information can be used for marketing designs; and 2) based on pair-wise support of items, the design of the value meal base is modeled as an optimization problem where the objective is to maximize the overall supports in a value meal base.},
  url={https://sites.google.com/site/liamhsieh/2018ICIM_best_paper_award.pdf},
  note ={(best paper award)}
}

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