DOI QR코드

DOI QR Code

A Study on the Government's Investment Priorities for Building a Supercomputer Joint Utilization System

  • Hyungwook Shim (Division of National Supercomputer, Korea Institute of Science and technology Information) ;
  • Jaegyoon Hahm (Division of National Supercomputer, Korea Institute of Science and technology Information)
  • 투고 : 2023.06.13
  • 심사 : 2023.07.10
  • 발행 : 2023.08.31

초록

The purpose of this paper is to analyze the Korean government's investment priorities for the establishment of a supercomputer joint utilization system using AHP. The AHP model was designed as a two-layered structure consisting of two areas of specialized infrastructure, a one-stop joint utilization system service, and four evaluation items for detailed tasks. For the weight of each evaluation item, a cost efficiency index considering the annual budget was developed for the first time and applied to the weight calculation process. AHP analysis conducted a survey targeting supercomputer experts and derived priorities with 22 data that had completed reliability verification. As a result of the analysis, the government's investment priority was high in the order of dividing infrastructure for each Specialized Center and building resources in stages. In the future, the analysis results will be used to select economic promotion plans and prepare strategies for the establishment of the government's supercomputer joint utilization system.

키워드

과제정보

This work was supported by the Korea Institute of Science and Technology Information(KISTI) (No. K-23-L02-C03).

참고문헌

  1. Banwet, J., Deshmukh, S.G. (2008) Evaluating performance of national R&D organizations using integrated DEA-AHP technique, The international journal of productivity and performance management, 57(5), 370-388.  https://doi.org/10.1108/17410400810881836
  2. Feng, Y.J., Lu, H., Bi, K. (2004) An AHP/DEA method for measurement of the efficiency of R&D management activities in universities, International transactions in operational research: a journal of The International Federation of Operational Research Societies, 11(2), 181-191. 
  3. Rimantho, D., Elistiani, A., Sundana, S., Sundari, AS. (2019) Decision Making Strategy For Decreasing The Potential Hazards of Work Accidents at Division R&D Using SWOT And AHP Methods, IOP conference series. Materials science and engineering, 528, 27-29. 
  4. Vellore, R.C., Olson, D.L. (1991) An AHP application to computer system selection, Mathematical and computer modelling, 15(7), 83-93.  https://doi.org/10.1016/0895-7177(91)90035-6
  5. Arunraj, N.S., Maiti, J. (2010) Risk-based maintenance policy selection using AHP and goal programming, Safety science, 48(2), 238-247.  https://doi.org/10.1016/j.ssci.2009.09.005
  6. Radevito, A., Putro, U., Dannya, M. (2021) Determining policy recommendations towards electric vehicles incentives in Jakarta using AHP-Entropy, IOP conference series : Earth and environmental science, 927(1), 1-12.  https://doi.org/10.1088/1755-1315/927/1/012007
  7. Altuzarra, A., Moreno, J.M., Salvador, M. (2007) A Bayesian priorization procedure for AHP-group decision making, European journal of operational research, 182(1), 367-382.  https://doi.org/10.1016/j.ejor.2006.07.025
  8. Li, X. (1998), Improved group AHP method and its group coordination statistical analysis, Journal of East China Shipbuilding Institute 1998(2), 45-48. 
  9. Liu, F.P., Yanan, Z., Weiguo, P. (2017) On Consistency in AHP and Fuzzy AHP, Journal of systems science and information, 5(2), 128-147.  https://doi.org/10.21078/JSSI-2017-128-20
  10. Shim, H.W., Jung, Y.H., Hahm, J.G. (2023) A Study on the Designation Institution for Supercomputer Specialized Centers in Republic of Korea, International Journal of Advanced Computer Science and Applications, 14(1), 306-312. https://doi.org/10.14569/IJACSA.2023.0140132