IEEE Access (Jan 2022)

Optimal Planning of University Technology Transfer Measures With FDANP-F-FlowSort and an Extended Multiobjective PROMETHEE V

  • Edgar Tibay,
  • Renissa Quinones,
  • Hubert Quinones,
  • Egberto Selerio,
  • Samantha Shane Evangelista,
  • Joerabell Lourdes Aro,
  • Fatima Maturan,
  • Nadine May Atibing,
  • Kafferine Yamagishi,
  • Lanndon Ocampo

DOI
https://doi.org/10.1109/ACCESS.2022.3176641
Journal volume & issue
Vol. 10
pp. 56629 – 56651

Abstract

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This work offers an integrated methodological framework for decision support in planning the implementation of measures that address the barriers to university technology transfer (UTT). The planning problem consists of two parts: 1) identifying the high priority measures; 2) optimally implementing these measures over a specified planning horizon subject to resource constraints. Treated as a multi-criteria sorting problem under uncertainty, the high priority measures are determined via fuzzy DEcision MAking Trial and Evaluation Laboratory (DEMATEL) and analytic network process (ANP) for evaluating the barriers, and the fuzzy FlowSort (F-FlowSort) for classifying the priority of the various measures. Then, an extended multi-objective extension of the Preference Ranking Organization METHod for Enrichment Evaluations V (PROMETHEE V) is offered to determine the degree of implementation of the high priority measures over multiple periods. Demonstrated in an actual case study with 29 identified measures under 24 previously known barriers, findings reveal six high priority measures, which include designing a sustained partnership, engaging in joint research ventures, establishing partnerships from international financial institutions, streamlining objectives to full support of the technology readiness levels, establishing a holistic system approach towards technology readiness levels, and establishing agreements to have access to the industry laboratory facilities. The implementation plan, represented as a set of Pareto optimal solutions, is obtained through the augmented $\varepsilon $ -constraint (AUGMECON) algorithm for the $\varepsilon $ -constrained multi-objective linear programming formulation of the extended PROMETHEE V. Layers of sensitivity analysis were performed to test the robustness of the results to changes in the parameters. Finally, policy insights are provided to key decision-makers for advancing UTT.

Keywords