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A machine learning based intelligent agent for human resource planning in IC design service industry
Conference paper

A machine learning based intelligent agent for human resource planning in IC design service industry

Chieh Hsu, Hsuan-An Kuo, Ju-Chien Chien, Wenhan Fu, Kang-Ting Ma and Chen-Fu Chien
Proceedings of the International Conference on Industrial Engineering and Operations Management, Vol.2019(MAR), pp.3758-3768
2019

Abstract

Genetic algorithm Human capital management IC design Knowledge worker Work force allocation XGboost Strategy and Management Management Science and Operations Research Control and Systems Engineering Industrial and Manufacturing Engineering
IC Design has been an industry which provides flexible application-specific integrated circuit (ASIC) services enabling semiconductor manufacturing companies for flexible decision. Although the industry influences semiconductor supply chain significantly, capacity portfolio and planning issues of IC design industry is seldom mentioned in the past studies. For IC design service industry, the main productivity denotes to IC design which is influenced by the performance of project management from workforce allocation. The purpose of this study is to develop an intelligent agent to predict the workforce required for each wafer production service project, and thus based on the prediction, the intelligent agent is able to provide an IC design service company with workforce allocation strategy. Featuring learning algorithms and analyzing from the existing data, the study trains a XG Boosting model combining Genetic Algorithm based parameter optimization mechanism. The proposed intelligent agent contributes to Total Resource Management (TRM) to enhance productivity, reduce costs and intelligence management.

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