(51b) Graph-Based Modeling Abstractions and Computational Tools for Complex Systems
We also show that a graph-based abstraction can be naturally be extended to represent computational workflows. A computational workflow is a virtual graph in which a collection of computational tasks live in nodes and edges represent communication links between tasks. This abstraction generalizes other modeling paradigms such as discrete-event and agent-based simulation, naturally accommodates computational algorithms, and enables the simulation of synchronous and asynchronous computing environments . A computational workflow can be used to simulate the performance of algorithms such as Benders decomposition, decentralized control architectures, markets, and swarm robotic systems under communication and decision-making delays and failures [6,7,8,9]. We discuss an implementation of a graph-based modeling platform in Julia, that we call Plasmo.jl.
 Jalving, J., Abhyankar, S., Kim, K.,Hereld, M., Zavala, V. M. A Graph-Based Computational Framework for Simulation and Optimization of Coupled Infrastructure Networks. To Appear in IET Generation, Transmission & Distribution, 2016.
 Tang, W., Allman, A., Pourkargar, D. B., & Daoutidis, P. (2017). Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection. Computers & Chemical Engineering, 111, 43â54
 N. Chiang, C. G. Petra, and V. M. Zavala. Structured nonconvex optimization of large-scale energy systems using PIPS-NLP. In Proc. of the 18th Power Systems Computation Conference (PSCC), Wroclaw, Poland, 2014.
 K. Kim and V. M. Zavala. Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs. Optimization Online, 2015
 Cassandras, C. G., & Lafortune, S. Introduction to Discrete Event Systems, (2008).
 RuszczyÅski, Andrzej P. Nonlinear optimization. Vol. 13. Princeton university press, 2006.
 J. B.Rawlings and D. Mayne. Model predictive control: theory and design. Nob Hill Publishing, 2009.
 Lopes, Y. K., Trenkwalder, S. M., Leal, A. B., Dodd, T. J., & GroÃ, R. (2016). Supervisory control theory applied to swarm robotics. Swarm Intelligence, 10(1), 65â97.
 Scott, W. Naomi L. (2017). Optimal evasive strategies for groups of interacting agents with motion constraints, (January), 1â12.