Machine Learning based Simulation Optimisation for Trailer Management

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