By leveraging subordinated Markov chains, we construct time-lagged partially subordinated Markov chains and characterize the inter-temporal correlation between continuous-time Markov chains. We demonstrate the advantages of our model using three illustrative examples. In the first example, we explore airline delay
propagation, showcasing the ease of calibration of our framework. In the second example, we highlight the advantages of using our framework for decision-making through the route optimization problem. Finally, we extend our approach to model propagation in larger networks in a parsimonious manner. Across these
three examples, we argue that our approach presents a practical and comprehensive framework for modeling correlation in Markov chains.
Vishwakant Malladi is an Assistant Professor of Operations Management at the Indian School of Business (ISB). He obtained his PhD in Risk and Operations Management from the McCombs School of Business, University of Texas at Austin. His research primarily focuses on risk in an operations management context and can be broadly divided into two areas. First, he works on parsimonious modelling of risk in high-dimensional systems using Lévy processes. Second, he studies the impact of risk and risk dependence in operations management problems such as inventory theory, reliability, and the facility location problem. Prior to his doctoral studies, Professor Malladi has worked as a Statistical Analyst for
Fractal Analytics and as an Equity Research Analyst for Centrum Capital. He holds a B. Tech in Mechanical Engineering from Indian Institute of Technology (IIT) Bombay and an MBA from Indian Institute of Management (IIM) Ahmedabad.
