A decision support framework for robust R&D budget allocation using machine learning and optimization

被引:43
作者
Jang, Hoon [1 ]
机构
[1] Sci & Technol Policy Inst, Ctr Publ R&D Program Evaluat, Sejong 30147, South Korea
关键词
Research and development; Data-driven R&D budget allocation framework; Public R&D program; Machine learning; Robust optimization; PROJECT PORTFOLIO SELECTION; NEURAL-NETWORK; MANAGEMENT; SUCCESS; MODEL;
D O I
10.1016/j.dss.2019.03.010
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Considering that government funding agencies make decisions on research and development (R&D) budget allocation to support an increasing number of research proposals, effective decision support systems are necessarily required. Motivated by the efforts of the Korean government, we propose a new decision support framework for allocating an R&D budget such that it maximizes the total expected R&D output. The proposed framework incorporates an R&D output prediction model with an optimization technique. We first employ a machine learning algorithm to accurately estimate future R&D output. Then, we apply a robust optimization technique to hedge against uncertainty in the predicted R&D output values. If not properly accounted for, this uncertainty may yield an inefficient budget allocation plan, thus hindering the operation of the R&D budgeting system. We demonstrate the effectiveness of the proposed model by applying it to a national R&D program conducted by the Korean government. Specifically, using the same budget, our budget allocation plan can achieve an output 13.6% greater than the actual R&D output. Thus, our model helps to attain allocation efficiency by systematically allocating budgets. We also observe the price of robustness when our model conservatively allocates budgets in order to hedge against uncertainty in the R&D predictions. Our findings offer insights for both policymakers and researchers related to designing better budget allocation systems for national R&D programs.
引用
收藏
页码:1 / 12
页数:12
相关论文
共 61 条
  • [1] A Discrete Stochastic Goal Program for Portfolio Selection: The Case of United Arab Emirates Equity Market
    Abdelaziz, F. Ben
    El Fayedh, R.
    Rao, A.
    [J]. INFOR, 2009, 47 (01) : 5 - 13
  • [2] [Anonymous], 2017, J UNCERTAIN SYST
  • [3] [Anonymous], 2012, MULTICRITERIA PORTFO
  • [4] Static R&D project portfolio selection in public organizations
    Arratia M, N. M.
    Lopez, F., I
    Schaeffer, S. E.
    Cruz-Reyes, L.
    [J]. DECISION SUPPORT SYSTEMS, 2016, 84 : 53 - 63
  • [5] Aryanezhad M. B., 2011, J IND ENG, V7, P12
  • [6] Badri M. A., 2001, International Journal of Project Management, V19, P243, DOI 10.1016/S0263-7863(99)00078-2
  • [7] Factors for success in R&D projects and new product innovation: A contextual framework
    Balachandra, R
    Friar, JH
    [J]. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 1997, 44 (03) : 276 - 287
  • [8] Multivariate dependence risk and portfolio optimization: An application to mining stock portfolios
    Bekiros, Stelios
    Hernandez, Jose Arreola
    Hammoudeh, Shawkat
    Duc Khuong Nguyen
    [J]. RESOURCES POLICY, 2015, 46 : 1 - 11
  • [9] Robust solutions of Linear Programming problems contaminated with uncertain data
    Ben-Tal, A
    Nemirovski, A
    [J]. MATHEMATICAL PROGRAMMING, 2000, 88 (03) : 411 - 424
  • [10] MATHEMATICAL PROGRAMMING MODELS FOR CAPITAL BUDGETING - SURVEY, GENERALIZATION, AND CRITIQUE
    BERNHARD, RH
    [J]. JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS, 1969, 4 (02) : 111 - 158