@article{pecker2025hybrid,

title = {Hybrid resource allocation control in cyber-physical systems: a novel simulation-driven methodology with applications to UAVs},

author = {Ezequiel Pecker-Marcosig and Juan I. Giribet and Rodrigo Castro},

url = {https://doi.org/10.1177/00375497241313404},

doi = {10.1177/00375497241313404},

year  = {2025},

date = {2025-01-01},

journal = {SIMULATION},

volume = {101},

number = {5},

pages = {597–619},

abstract = {Designing hybrid controllers for cyber-physical systems (CPSs) where computational and physical components influence each other is a challenging task, as it requires considering the performance of very different types of dynamics simultaneously. Meanwhile, controlling each of these dynamics separately can lead to unacceptable results. Common approaches to controller design rely on the use of analytical methods. Although this approach can provide formal guarantees of stability and performance, the analytical design of hybrid controllers can become quite cumbersome. Alternatively, modeling and simulation (M&S)-based design techniques have proven successful for hybrid controllers, providing robust results based on Monte Carlo techniques. This requires simulation models and platforms capable of seamlessly composing the underlying hybrid domains. Unmanned Aerial Vehicles (UAVs) are CPSs with sensitive physical–computational couplings. We address the development of a hybrid model and simulation platform for a data collection application involving UAVs with onboard data processing. The quality of control (QoC) of the physical dynamics must be ensured together with the quality of service (QoS) of the onboard software competing for scarce processing resources. In this scenario, it is imperative to find safe trade-offs between flight stability and processing throughput that can adapt to uncertain environments. The goal is to design a hybrid supervisory controller that dynamically adapts the use of resources to balance the performance of both aspects in a CPS, while ensuring system-level QoS. We present the end-to-end M&S-based design methodology, which can be regarded as a design template for a broader class of CPSs.},

keywords = {Cyber-Physical Systems, DEVS, QSS, UAVs},

pubstate = {published},

tppubtype = {article}

}

@article{lanzarotti_multi-scale_2025,

title = {A multi-scale agent-based model of aerosol-mediated indoor infections in heterogeneous scenarios},

author = {Esteban Lanzarotti and Andrea Pineda Rojas and Francisco Roslan and Leandro Groisman and Lucio Santi and Rodrigo Castro},

url = {https://doi.org/10.1080/17477778.2025.2476456},

doi = {10.1080/17477778.2025.2476456},

issn = {1747-7778},

year  = {2025},

date = {2025-01-01},

urldate = {2025-07-01},

journal = {Journal of Simulation},

volume = {0},

number = {0},

pages = {1–19},

abstract = {We present a hybrid agent-based and equation-based simulation model to study airborne infection transmission events in heterogeneous indoor spaces. Agents move between different rooms, generating evolving networked social interactions. Suspended aerosols enable both direct and indirect transmission, shaping multi-scale contagion at local (room) and global (building layout) levels within a SEIR process. This approach provides a very flexible platform for dealing with environmental and population heterogeneities. Our model reproduces well-documented real-world contagion events and analytical models in the literature over a wide range of environments (hospitals, offices, restaurants, and classrooms). The model is applied to study transmission patterns in a typical school day, exploring three heterogeneity-driven scenarios: (i) varying break-time activity intensity, (ii) localized ventilation reductions, and (iii) flexible class/break durations, uncovering emerging nonlinear system-level dynamics. Our results show that neglecting heterogeneities can lead to considerable underestimation of attack rates. Such scenarios cannot be modeled with previous agent-based frameworks.},

note = {Publisher: Taylor & Francis 

_eprint: https://doi.org/10.1080/17477778.2025.2476456},

keywords = {Agent-based model, Airborne transmission, DEVS, Multi-scale simulation, QSS, Viral infection},

pubstate = {published},

tppubtype = {article}

}

@article{carreira-munich_complex_2025,

title = {Complex Adaptive Systems Simulation Modeling: A Decade's Overview},

author = {Tobías Carreira-Munich and Agustín Caputo Bugallo and Claudia Szabo and Rodrigo Castro},

year  = {2025},

date = {2025-01-01},

journal = {Complex System Modeling and Simulation},

note = {In press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ghersa_agroptim_2024,

title = {AgrOptim: A novel multi-objective simulation optimization framework for extensive cropping systems},

author = {Felipe Ghersa and Lucas Figarola and Rodrigo Castro and Diego Ferraro},

url = {https://www.sciencedirect.com/science/article/pii/S0168169924005106},

doi = {10.1016/j.compag.2024.109119},

issn = {0168-1699},

year  = {2024},

date = {2024-01-01},

urldate = {2025-07-01},

journal = {Computers and Electronics in Agriculture},

volume = {224},

pages = {109119},

abstract = {Cropping systems should be designed to be more productive and have a smaller environmental footprint to sustainably meet the growing demand for food, fiber, and fuel. However, this requires the evaluation and ranking of many cropping system designs based on their economic and biophysical performance, which are often in conflict. Although field experiments and simple crop simulation models have been used for this purpose, studies have generally considered a limited number of agronomic decision combinations or indicators that partially capture ecosystem functions. Coupling evolutionary algorithms with process-based crop simulation models provides a less resource-intensive alternative and can incorporate many indicators to (1) quantify the trade-offs between biophysical and economic performance, and (2) identify the set of agronomic decision combinations that minimize these trade-offs. The objective of this paper was to present AgrOptim, a novel cropping system simulation optimization framework that uses genetic algorithms to optimize a holistic set of biophysical and economic performance indicators through different combinations of agronomic decision variables (i.e., crop sequence, crop structure, pesticide dose, and fertilizer dose). Indicators were derived from a process-based crop simulation model, an ecotoxicological risk simulation model, and emergy synthesis. The framework was implemented in Argentina to (1) characterize the relationship between economic and biophysical indicators and (2) evaluate the current state and potential improvements of three frequently used cropping system designs. A multi-objective optimization experiment was designed to simultaneously optimize 30-year cropping sequences based on one economic objective (return on investment) and four biophysical objectives (crop residue carbon inputs, precipitation use efficiency, nonrenewable to renewable energy ratio, and pesticide ecotoxicity). Results showed that trade-offs exist between economic and all biophysical objectives, albeit with varying intensities. Additionally, the decision variables that provided improved performance in terms of carbon residues, precipitation use efficiency, and ecotoxicological risk also presented higher levels of nonrenewable energy use. For the three frequently used cropping system designs, the decision variables that improved the performance of each indicator were identified. These findings highlight the challenges faced by agricultural producers considering the trade-offs between their economic and biophysical objectives. Additionally, they reveal potential model-aided improvements that can be obtained using crop simulation models and optimization algorithms to redesign cropping systems.},

keywords = {Crop sequence, Crop simulation, Genetic algorithm, Optimization, Sustainability gap},

pubstate = {published},

tppubtype = {article}

}

@article{ferraro_predicting_2024,

title = {Predicting land use and environmental dynamics in Argentina's Pampas region: An agent-based modeling approach across varied price and climatic scenarios.},

author = {Diego Ferraro and Felipe Ghersa and Rodrigo Castro},

url = {https://www.sciencedirect.com/science/article/pii/S0304380024002692},

doi = {10.1016/j.ecolmodel.2024.110881},

issn = {0304-3800},

year  = {2024},

date = {2024-01-01},

urldate = {2025-07-01},

journal = {Ecological Modelling},

volume = {498},

pages = {110881},

abstract = {This study, employing the AGRODEVS Agent-Based Model (ABM), systematically examined land use dynamics in Argentina's Pampas Region. Simulations under diverse scenarios highlighted the significant role of economic determinants, particularly crop price relationships, in influencing maize or wheat/soybean double cropping prevalence. Maize-dominated landscapes consistently achieved carbon sequestration goals, while wheat/soybean landscapes faced challenges, notably in ecotoxicity. Scenarios encompassed varying climatic conditions and soybean/maize price ratios, providing insights into the interplay shaping agricultural land use decisions among individual agents. The AGRODEVS model's robust performance underscored its effectiveness in integrating economic and environmental factors, contributing to a practical understanding of sustainable land use planning complexities.},

keywords = {Agent-based modeling, Agricultural decision-making, Climate-sensitive shifts, Environmental sustainability, Land use dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{bergonzi_quantization-based_2024,

title = {Quantization-based simulation of spiking neurons: theoretical properties and performance analysis},

author = {Mariana Bergonzi and Joaquín Fernández and Rodrigo Castro and Alexandre Muzy and Ernesto Kofman},

url = {https://doi.org/10.1080/17477778.2023.2284143},

doi = {10.1080/17477778.2023.2284143},

issn = {1747-7778},

year  = {2024},

date = {2024-01-01},

urldate = {2025-07-01},

journal = {Journal of Simulation},

volume = {18},

number = {5},

pages = {789–812},

abstract = {In this work we present an exhaustive analysis of the use of Quantized State Systems (QSS) algorithms for the discrete event simulation of Leaky Integrate and Fire models of spiking neurons. Making use of some properties of these algorithms, we first derive theoretical error bounds for the sub-threshold dynamics as well as estimates of the computational costs as a function of the accuracy settings. Then, we corroborate those results on different simulation experiments, where we also study how these algorithms scale with the size of the network and its connectivity. The results obtained show that the QSS algorithms, without any type of optimisation or specialisation, obtain accurate results with low computational costs even in large networks with a high level of connectivity.},

note = {Publisher: Taylor & Francis 

_eprint: https://doi.org/10.1080/17477778.2023.2284143},

keywords = {discontinuity handling, event-driven simulation, Hybrid systems, Quantized State Systems, spiking neural networks},

pubstate = {published},

tppubtype = {article}

}