## 2 Related Work  
In this section, we will review the relevant literature on scheduling algorithms, explicitly focusing on the approaches of dynamic time quantum and priority scheduling. For example, the authors in [1] proposed the design of four versions of the Round Robin scheduling algorithm that allows for the dynamic determination of the time quantum based on the candidate process's overall execution time. The main goal of these variations was to achieve a fair distribution of time quantum among the processes to increase the system's overall performance. The initial variation was a fixed algorithm that allotted the time quantum to each process based on its execution time. The subsequent variation was a dynamic algorithm, which adapted the time quantum in real time based on the process'sexecution time. The third variation was a hybrid algorithm that combined static and dynamic algorithms to balance fairness and efficiency. The fourth variation was an enhanced version of the third variation by adding a process-aging technique. A novel scheduling algorithm, \"Intuitionistic Fuzzy-based Round-Robin\", has been created to improve efficiency and handle imprecise parameters. This approach employed an intuitionistic fuzzy inference system and a hybrid Round Robin strategy to adjust the time slice dynamically for inaccurate burst time. On the other hand, the hybrid Round Robin approach determines the task selection process [2]. The authors in [3] designed a Round Robin technique using a mean average and two registers for burst time storage. Upon completing a time slice by a process, the registers and ready queue undergo reformation, whereas adding new processes to the ready queue entails updating the registers. The authors in [4] described an algorithm called Median Average Round Robin (MARR) that calculated time quantum as (a) the average of all processes taken (avg), (b) the median of burst time (MedBt), (c) time quantum (Tq) = (avg + MedBg)/2. Using this process, the time quantum is allotted to the ready process in the queue. This process is repeated when the new process arrives or leaves the ready queue, and a new time quantum is generated for the ready process. The authors in [5] presented a new variant of the RR technique. The author described the algorithm in steps, as follows: (a) In ascending order, sort the burst times of all processes; (b) calculate the differences between consecutive processes; (c) identify the highest difference among these calculated differences; (d) calculate the median value among the sorted processes; (e) calculate the time quantum as the sum of the maximum difference and the median burst time; (f) if the difference between the process burst time and the time quantum is equal to zero, the process will be terminated; (g) if the difference between the process burst time and the time quantum is not equal to zero, the process will be moved to the end of the ready queue, and steps 6 and 7 will be repeated until the process finishes executing; (h) calculate average turnaround and waiting time. A new RR algorithm is presented in [6] to minimize the average waiting time, turnaround time, and NCS by combining the benefits of favoring short processes and the low scheduling overhead of RR. The novel technique classifies processes based on CPU characteristics, such as service period, weights, and allocation count. Each process within a group is allocated the same time quantum, calculated based on the group's weight and the process's CPU service period. In a previous study [7], the authors introduced a novel CPU scheduling algorithm that is a modified version of the preemptive priority scheduling algorithm, a popular technique used in real-time systems. The performance of the traditional priority scheduling algorithm was enhanced by the proposed algorithm, which introduced an aging mechanism to prevent low-priority processes from being indefinitely postponed.