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Robust Optimization Model for R&D Project Selection under Uncertainty in the Automobile Industry

Author

Listed:
  • Seunghoon Lee

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Yongju Cho

    (Innovative Smart Manufacturing R&D Department, Korea Institute of Industrial Technology, 89 Yangdaegiro-gil, Seobuk-gu, Cheonan-si 31056, Korea)

  • Minjae Ko

    (Innovative Smart Manufacturing R&D Department, Korea Institute of Industrial Technology, 89 Yangdaegiro-gil, Seobuk-gu, Cheonan-si 31056, Korea)

Abstract

In a company, project management is responsible for project selection from candidates under some limited constraints to achieve the company’s goal before the project begins as well as the project operations in progress. The development of new technologies and products can broaden a company’s market share, and to do so, research and development (R&D) projects are significant. However, limited funds force a company to select projects that can best represent the company’s interests. As projects may take a long time to develop, a number of uncertainties may occur, and the most concerning uncertainty is cost uncertainty. In this study, a robust optimization decision model for project selection considering cost uncertainty is proposed to assist the decision-making process for companies that need to select projects from a number of candidates due to limited funds. The model considers project selection in view of the total cost of ownership, which is a key factor for customers and companies in the automobile industry. The proposed model is tested in the automobile industry environment with different conservatism levels about cost uncertainty, and an analysis of expected market changes and a company’s income is performed with the solutions obtained from the proposed model. The result shows that the presented model reacts to cost uncertainty robustly for assisting the decision-makers in the company.

Suggested Citation

  • Seunghoon Lee & Yongju Cho & Minjae Ko, 2020. "Robust Optimization Model for R&D Project Selection under Uncertainty in the Automobile Industry," Sustainability, MDPI, vol. 12(23), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:10210-:d:458288
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    References listed on IDEAS

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