Optimization of a Factory Line Using Multi-Objective Evolutionary Algorithms

Abstract

In this paper, we describe a simulation for an automotive manufacturing process using automated guided vehicles (AGVs). The simulation is used to optimize a generalized factory model layout using multi-objective evolutionary algorithms. The Pareto front of the optimization is analyzed, and layouts are compared to the industry standard transfer line in terms of objectives that include capital cost, energy usage, and product throughput. We seek to determine from the results whether genetic algorithms are a feasible tool for the optimization of manufacturing automobiles.