DynSyst_Special_Topics: Optimization of Enterprise Dynamical Systems Described By Rules

University Of Washington, Seattle WA

Investigators

Abstract

Enterprises, such as energy management, supply chain management, transportation and disaster management are complex engineering systems. Decision makers struggle to reduce costs and increase efficiencies in the face of uncertainty and complicated interactions among a large number of agents. Traditional optimization approaches that rely on static mixed integer programming models cannot accurately capture the complexities of the dynamic enterprise and are limited by computational complexity. We propose a novel approach to integrate rule-based systems with optimization to dynamically control modern enterprises. By allowing decision makers to model their complex enterprise with if-then rules, we will enable them to accurately reflect the real-life environment and to easily modify the rules to capture unexpected changes in the system. We will consider one of several potential application areas. One area is the transmission and distribution of electrical power, where rules or protocol are used to decide how to distribute power reserves to accommodate uncertainties in the power market (due to weather, power outage, demand, etc.). Another possible application area is transportation management, where vehicle routing and scheduling must respond to dynamic pick-up and delivery requests that consider traffic, truck maintenance, and personnel. We may also test our rule-based methodology with disaster management teams, including medical providers and fire departments, to design protocols for efficient responses to disasters. Current approaches to optimizing enterprise systems are limited by their ability to accurately model a complex system, as well as the computational limitations to solve NP-hard problems. The intellectual merit of our approach lies in developing a mathematical scheme that converts rules of an enterprise to an automaton, which provides the dynamics of the rules. That allows us to solve an optimal control problem. We translate the solution back into the rules of the application via the automaton. This research will contribute to computer science, applied math and operations research communities by creating a new hybrid methodology. Our approach is transformative in that it can apply to virtually any enterprise, and provides the decision makers with rules in their own natural language. Its broad impact resides in the potential to provide a new hybrid methodology for combinatorial type enterprise problems, such as routing, sequencing and scheduling, that currently rely on mixed integer programs and heuristics. In our research and education missions, we are committed to fostering diversity and will recruit and mentor underrepresented students.