Introduction
job scheduling weekly and daily assignments the old fashioned way
This study describes the development of a novel job scheduling tool for university technology transfer using simulated annealing in R-programming. Technology commercialization managers often face training inventors on intellectual property (IP) laws and IP policies. They also evaluate invention disclosures for patentability and marketability. In addition, they draft and implement invention marketing plans. Further, they work closely with patent counsel on patent prosecution. Expediency is important. The amount of time taken to evaluate invention disclosures and file patent applications often conflicts with inventors’ desire to publish findings. Yet, very few technology transfer managers use project management job scheduling tools to minimize processing time.
Importance of Job scheduling
Job scheduling is crucial because it has the potential for improving staff accountability and trust between the TTO staff and faculty. However, TTO staff that value their academic freedom and autonomy may resist the use of job scheduling tools. A description of experimentation follows and the test results is provided. The discussion provides the primary implication for technology managers. The job scheduling tool schedules technology transfer tasks quite easily and speedily with this proposed job scheduling tool. I scheduled a hypothetical set of TTO staff job tasks that did not include faculty inventor tasks. These are study limitations. Thus, future research should include further experimentation in actual university technology transfer offices using the job tasks in real time.
Findings
In short, I found fascinating discoveries through experimentation. Simulation annealing is an advanced optimization tool. University technology transfer job scheduling is ideal for this. The meta-heuristic simulated annealing program converges to an optimal solution that satisfied the constraints. As it happens, the use of simulated annealing for job scheduling statistically guarantees finding an optimal solution (Ingber, 1993). In conclusion, the job scheduling tool experimentation illustrates the use of advanced optimization to schedule TTO staff job tasks in a very quick and simple manner.