  In this work, we investigate the statistical properties of drink serving in a
nightclub bar, utilizing a stochastic model to characterize pedestrian dynamics
within the venue. Our model comprises a system of n agents moving across an
underlying square lattice of size l representing the nightclub venue. Each
agent can exist in one of three states: thirsty, served, or dancing. The
dynamics governing the state changes are influenced by a memory time, denoted
as {\tau}, which reflects their drinking habits. Agents' movement throughout
the lattice is controlled by a parameter {\alpha} which measures the impetus
towards/away from the bar. When {\alpha} = 0, a power-law distribution emerges
due to the non-objectivity of the agents. As {\alpha} moves into intermediate
values, an exponential behavior is observed, as it becomes possible to mitigate
the drastic jamming effects in this scenario. However, for higher {\alpha}
values, the power-law distribution resurfaces due to increased jamming. We also
demonstrate that the average concentration of served, thirsty, and dancing
agents provide a reliable indicator of when the system reaches a jammed state.
Subsequently, we construct a comprehensive map of the system's stationary
state, supporting the idea that for high densities, {\alpha} is not relevant,
but for lower densities, the optimal values of measurements occurs at high
values of {\alpha}. To complete the analysis, we evaluate the conditional
persistence, which measures the probability of an agent failing to receive
their drink despite attempting to do so. In addition to contributing to the
field of pedestrian dynamics, the present results serve as valuable indicators
to assist commercial establishments in providing better services to their
clients, tailored to the average drinking habits of their customers.
