Machine Learning based Simulation Optimisation for Trailer Management

TL;DR

This paper presents a novel simulation optimisation approach combining genetic algorithms and neural network filters to improve trailer management in a logistics setting, demonstrating enhanced efficiency and solution quality.

Contribution

It introduces an integrated metaheuristic and approximation model framework with an ensure probability mechanism for better simulation optimisation.

Findings

The proposed method outperforms single approximation and random approaches in solution quality.

Parameter settings significantly affect optimisation performance.

The approach reduces computation time while maintaining high-quality solutions.

Abstract

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.