A Markov process is a mathematical model used to describe a system that undergoes transitions between different states over time, with the key property that the future state of the system depends only on its current state and not on the sequence of events that preceded it, known as the "memoryless" property. This type of process is characterized by a set of states, transition probabilities between these states, and a way to model how the system evolves over time. In operations research, Markov processes are applied to model various decision-making problems, such as inventory management, queuing systems, reliability analysis, and maintenance scheduling. By analyzing the transition probabilities and steady-state distributions of these processes, operations researchers can optimize systems for efficiency, reduce costs, and improve decision-making under uncertainty.