This thesis offers a detailed exploration of the integration of input signals and control mechanisms within max-min-plus-scaling (MMPS) systems, a subclass of discrete event (DE) systems. Unlike traditional control systems, which rely on continuous evolution modeled by
differential equations, DE systems progress through the occurrence of discrete events. MMPS
systems enhance this adaptability by encompassing maximization, minimization, scaling, and
addition, creating a framework for modeling and managing various processes, including logistics networks and urban railway systems.

The primary objective of this thesis is to introduce input signals into MMPS systems and
systematically investigate control strategies. This involves establishing a structure accommodating these input signals while preserving essential properties such as time invariance. The study examines both open-loop and closed-loop control strategies, focusing on the latter to implement optimization-based control to optimize system performance through effective
feedback control.

This thesis is organized, beginning with the mathematical foundation of MMPS systems and
progressing to the development of control methods. The implementation of these methods is
validated through practical applications such as manufacturing systems and the urban railway system, demonstrating their effectiveness.

By advancing our understanding of control in MMPS systems, this research provides a systematic methodology that integrates control and illustrates how optimization-based techniques
can enhance overall performance. The insights gained from this work lay a groundwork for
future research, potentially extending beyond transportation systems to other discrete eventdriven industries. Engaging with this research has the potential to control numerous fields, promising innovative solutions and improved efficiencies across sectors.