The Stochastic Model for Queue Simulation
DOI:
https://doi.org/10.51278/bpr.v5i1.1747Keywords:
Queue Simulation, Stochastic Model, Queuing SystemsAbstract
The stochastic model for queue simulation can be defined as a model that can explain the nature of the system in probability, with data or information entering as input of random probability and the output that can be generated is also random. This stochastic model is often also used to model multiple probabilities or Monte Carlo. In stochastic processes, the properties of the outcomes are formed from a random selection process, so that the outcomes obtained can be described in terms of averaged counts, but are often also reinforced by the concept or trend of significant increase. Models based on the probability of an event occurring and taking into account the concept of uncertainty are often represented by probabilistic models or stochastic models. Stochastic models are used to model queuing systems that have variations in the process, such as manufacturing systems that use random variables to model the range. This stochastic model is used to predict the performance of queuing systems that have variations in the process, such as the application of the M / M / 1 queuing model to the vehicle queuing system at the northern ringroad three intersection to predict the level of busyness on the east and west arms. The contribution of this research is that stochastic models can also be used to analyze and optimize complex business processes by considering random factors that affect the system. The application of stochastic models in supply chain management allows companies to more accurately estimate customer demand and optimize inventory. In addition, stochastic models can be used in financial risk analysis to evaluate various investment scenarios and help make better decisions. The use of stochastic models in the service industry can help improve operational efficiency by predicting customer arrival patterns and optimizing resource allocation. In the context of urban planning, stochastic models can be applied to analyze traffic patterns and design more effective transportation infrastructure.
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