  In many situations, simulation models are developed to handle complex
real-world business optimisation problems. For example, a discrete-event
simulation model is used to simulate the trailer management process in a big
Fast-Moving Consumer Goods company. To address the problem of finding suitable
inputs to this simulator for optimising fleet configuration, we propose a
simulation optimisation approach in this paper. The simulation optimisation
model combines a metaheuristic search (genetic algorithm), with an
approximation model filter (feed-forward neural network) to optimise the
parameter configuration of the simulation model. We introduce an ensure
probability that overrules the rejection of potential solutions by the
approximation model and we demonstrate its effectiveness. In addition, we
evaluate the impact of the parameters of the optimisation model on its
effectiveness and show the parameters such as population size, filter
threshold, and mutation probability can have a significant impact on the
overall optimisation performance. Moreover, we compare the proposed method with
a single global approximation model approach and a random-based approach. The
results show the effectiveness of our method in terms of computation time and
solution quality.
