What is discrete-event simulation (DES)?

Discrete-event simulation (DES), also called a discrete-event model, describes a system as a sequence of separate events, like a customer arriving, a machine finishing a part, or a truck leaving. Between events, nothing changes. It suits operations where counting items, waiting, and using limited resources matter more than smooth rates.

How it works

The model keeps a clock and a list of upcoming events. At each event, it updates the state, for example by starting a service or adding to a queue, and schedules new events. Typical outputs are waiting times, queue lengths, and how busy each resource is. Randomness enters through arrival and service times, so one pass gives one possible history, and many passes are needed to see typical results. The classic result Little's law links them: the average number in a system L equals the arrival rate times the average time spent, L = λW.

Discrete-event thinking is most helpful when resources are scarce and work waits for them. Utilization close to 100 percent usually means long queues, which is why a busy server is not the same as an efficient one.

Example

Say a repair shop receives 4 jobs per hour, and each job spends on average 0.5 hours in the shop. By Little's law, the average number of jobs in the shop is 4 times 0.5, which is 2. If jobs start taking 0.75 hours, the average becomes 3. The shop does not need to follow every job to know this. This is an illustration with round numbers.

How to model it in Machinations

Use a Source for arrivals, a Queue for waiting, a Delay for the service time, and a Drain for departures. Add a Gate for rework or routing by probability. Play the diagram many times at once and chart the Queue to see typical and worst-case waits.