With the continuous deepening of the application of information technology in the
manufacturing field, the informatization of manufacturing systems has developed from unit …

Recent research in Computer Science has investigated the use of Deep Learning (DL)
techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) …

In discrete event simulation (DES) models, stochastic behaviour is modelled by sampling
random variates from probability distributions to determine event outcomes. However, the …

Discrete event modeling and simulation and reinforcement learning are two frameworks
suited for cyberphysical system design, which, when combined, can give powerful tools for …

In the context of modern industry, optimization emerges as one of the most powerful tools,
allowing decision-makers to allocate their resources more assertively and deal with complex …

Abstract Recent research in Computer Science has investigated the use of Deep Learning
(DL) techniques to complement outcomes or decisions within a Discrete Event Simulation …

In the last years, the use of metamodel-based simulation optimization techniques to solve
industrial problems stood out as a promising research field, mainly due to the advance of …

Computer model validation is an essential step in simulation projects. The literature
suggests using statistical techniques for comparing the outputs from the simulated model …

In discrete event simulation (DES), the events are random (aleatory) and typically
represented by a probability distribution that fits the real phenomena that is studied. The true …

Purpose: This study aims to determine the economic and technical feasibility of operating
and leasing sewage treatment plants through an application that uses mathematical …