G
Graziana Cavone
Researcher at Polytechnic University of Bari
Publications - 33
Citations - 578
Graziana Cavone is an academic researcher from Polytechnic University of Bari. The author has contributed to research in topics: Petri net & Model predictive control. The author has an hindex of 9, co-authored 33 publications receiving 286 citations. Previous affiliations of Graziana Cavone include Instituto Politécnico Nacional & University of Cagliari.
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Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario.
TL;DR: An optimal control approach is proposed that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario and supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model.
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IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings.
TL;DR: An IoT based architecture for the implementation of Model Predictive Control of HVAC systems in real environments and the effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.
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A Survey on Petri Net Models for Freight Logistics and Transportation Systems
TL;DR: A survey on contributions in the application of Petri Nets in freight logistics and transportation systems is presented, debating the approaches’ viability, discussing contributions and limitations, and identifying future research directions to enhance the successful application of PNs.
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A Timed Petri Nets Model for Performance Evaluation of Intermodal Freight Transport Terminals
TL;DR: The proposed modeling framework is modular and based on timed Petri Nets, where places represent resources and capacities or conditions, transitions model inputs, flows, and activities into the terminal and tokens are intermodal transport units or the means on which they are transported.
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A decision making procedure for robust train rescheduling based on mixed integer linear programming and Data Envelopment Analysis
TL;DR: A self-learning decision making procedure for robust real-time train rescheduling in case of disturbances, applicable to aperiodic timetables of mixed-tracked networks, which is able to determine appropriate relevance weights to employ when disturbances of the same type affect the network.