An industrial perspective on simulation versus optimisation decision-making capabilities

Despite the increasing development of optimisation-based approaches over the last decades, simulation methods are still widely employed for problems that could instead be optimised. Simulation relies on achieving feasibility through a trial-and-error procedure and provides meaningful and useful resources to find quick or rigorous solutions. On the other hand, optimisation provides proper capabilities for automatic and systematic searching over the solution space. Although more computationally expensive, optimisation has been increasingly used due to the recent technological improvements and research developments in the wake of the Industry 4.0. However, industrial decision-making is still often carried out manually, which indicates an urgent need for addressing the difficulties, complexities, and issues in the implementation of automated tools. This work aims to provide guidance for industrial operations towards selecting proper decision-making capabilities. An industrial perspective on simulation and optimisation decision-making is provided, whereby highlighting the issues and challenges faced by industrial peers, and the current gaps between academic research and industrial implementation. This includes the interplay between simulation and optimisation tools, academic research, and industrial implementation. From an industrial perspective, we provide guidance for incorporating academic knowledge by the industry, exposing what are the industrial requirements to be further addressed, and assisting industrial peers towards achieving further breakthroughs.