The integration of optimization paradigms and simulation practice in industrial management

Abstract

Over the past decades, simulation has become one of the most widely used decision support techniques both in science and in industry. This increasing popularity is mainly caused by the ongoing performance improvements in hardware and software and by the growing maturity of simulation methodology. Since the start of modern computer simulation practice at the end of the forties, simulation has mainly been used to model and to analyze the behavior of complex and non-deterministic systems, such as physical an biological systems, but also industrial processes, such as chemical reactors, manufacturing lines etc... The most important advantage of having a simulation model of such a system is that it allows for numerous experiments without interfering with the real system and its potential risks. Although simulation as a methodology has no inherent optimization capabilities, the goal of simulation experiments is to enhance understanding of the system’s behavior in order to optimize one or more system parameters (design) or variables (operation). In this paper, an overview will be given of the options that are at the
disposal of a simulation practitioner in the process of including optimization approaches in simulation projects.