Compositional Optimization of Discrete Event Systems

Optimizing the execution of industrial processes such as manufacturing cells or whole assembly plants can have great impact on their performance. However, finding an optimal sequence of tasks in a large-scale system is a complex optimization problem. Most systems are comprised of multiple sub-systems and the search space of the optimization generally grows exponentially with each sub-system. In this paper, we propose the method compositional optimization to mitigate this problem. Compositional optimization integrates methods from optimization and compositional supervisory control theory to exploit the local behavior of the sub-systems, reducing them individually, and then synthesize a globally optimal controller compositionally. The local optimization technique avoids a monolithic model of the system, which can reduce the complexity of the optimization significantly. The potential of compositional optimization is demonstrated using a realistic example, similar to a large scale industrial application, while we also reflect on the limitations and highlight specific system properties that can be exploited by the method.