## 2 Discrete Event Simulation Concepts  
Researchers benefit from DES concepts in studying systems [17; 18; 19]. The DES methodology enables the analysis of systems with time dependency between system elements. The name of DES refers to the use of events and the discrete-time advancement in the simulation execution. Events cause the state of a simulated model to change. For example, finishing the process of a job on a machine lets the machine become idle if no other job waits in its queue, and another machine could start processing the job in its manufacturing route. Another example is a postman arriving at a node in a network to deliver orders to the node. The time at which an event occurs defines the time advancement in the simulation model. Thus, a list of events that will occur in the future is defined and ordered chronologically according to events' time.  
The events in DES are divided into events with a defined time to occur and are listed in the event list [18], e.g.,: the end of processing job \(i\) on machine \(A\). This event time depends on the start of a process that lasts for a specific time, such as processing job \(i\) on machine \(A\). Other events are conditional [19]. Their occurrence depends on satisfying one or more conditions. For example, processing job \(i\) on machine \(B\) requires an extra part to be delivered to the machine. If this part is not delivered, the processing of job \(i\) cannot start. Hence, the finish processing time cannot be scheduled yet.  
At the execution of the DES, simulation clock time advances to the time of the first event in the event list, as illustrated in Figure 3. This event is executed, and changes are applied to the simulation model according to defined event routines. These changes include updating model variables and states. Therefore, new events are defined and added to the event list, and the executed event is removed from the list. This execution continues  
Figure 2: Number of articles indexed in Scopus for the main topics addressed in this article.  
until all events have been considered. Events with similar occurrence time are executed sequentially when the simulation clock time reaches their time. In addition, conditional events are checked at each time advancement to execute those with satisfied conditions.  
This execution of events enables the handling of dependencies between different elements in the model. As a result, complex systems are modeled and analyzed using the DES methodology. In addition, the defined relation between events helps to guarantee the synchronization of processes and elements in the simulated model. Most real-world systems are complex concerning the relation among system elements, such as production lines and logistics systems. Accordingly, the DES approach has become a candidate tool for analyzing these systems. For example, DES was used to analyze health care processes and support planning in them [20; 21]. [PERSON] et al. [22] evaluated different crowding scores in an emergency department using DES, and [PERSON] et al. [23] evaluated recommended solutions to reduce resource waste using DES. In transportation and logistics, [PERSON]. [24] studied the impact of delivery time strategies on last-mile delivery distribution problems. In addition, DES can be used to analyze the stochastic behavior of systems because of the ability to generate observations from random variables during the execution of a simulation run. Researchers integrated DES into their developed frameworks to support decision-making, such as enhancing offshore service levels [25] and reducing material logistics costs in road construction [26].