There is a demand for introducing a challenging and innovative R&D system to develop new technologies to generate weapon system requirements. Despite the increasing trend in annual core technology development tasks, the infrastructure expansion, including personnel in research management institutions, is relatively insufficient. This situation continuously exposes difficulties in task planning, selection, execution, and management. Therefore, there is a pressing need for strategies to initiate timely research and development and enhance budget execution efficiency through the streamlining of task agreement schedules. In this study, we propose a strategic model utilizing a flexible workforce model, considering constraints and optimizing workload distribution through resource allocation to minimize bottlenecks for efficient task agreement schedules. Comparative analysis with the existing operational environment confirms that the proposed model can handle an average of 67 more core technology development tasks within the agreement period compared to the baseline. In addition, the risk management analysis, which considered the probabilistic uncertainty of the fluctuating number of core technology research and development projects, confirmed that up to 115 core technology development can be contracted within the year under risk avoidance.

Logistics project scheduling problem in indeterminate environment is gaining more and more attention in recent years. One effective way to cope with indeterminacy is to develop robust baseline schedule. There exist many related researches on building robust schedule in stochastic environment, where historical data is sufficient to learn probability distributions. However, when historical data is not enough, precise estimation on variables may be impossible. This kind of indeterminate environment can be described by uncertainty according to uncertainty theory. Related researches in uncertain environment are sparse. In this paper, our aim is to solve robust project scheduling in uncertain environment. The specific problem is to develop robust schedule with uncertain activity durations for logistics project. To solve the problem, an uncertain model is built and an intelligent algorithm based on simulated annealing is designed. Moreover, we consider a logistics project as a numerical example and illustrate the effectiveness of the proposed model and algorithm.