Abstract
This paper explores the optimal combination as a way to improve efficiency. Nonlinear optimization, random search, and greedy algorithm methods were used to find an optimal project combination of projects for government research institutes. According to our numerical experiments, the nonlinear programming method is not computationally efficient (i.e., requiring long computational time) to find an optimal combination. However, the greedy algorithm can produce multiple optimal project combinations in a short time window. Random search can also generates multiple optimal combinations, but it has disadvantage that computational time increases in the number of random generations. By using the proposed greedy method, multiple optimal combination can be quickly found in a situation where the number of R&D projects increases. Therefore, a research institute has a greater flexibility in selecting one among multiple optimal combinations.
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