Giulia Zarpellon
Impact in
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- Vehicle Routing Optimization Methods
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- Micro and Nano Robotics
Papers in
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- Advanced Optimization Algorithms Research 3
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- Metaheuristic Optimization Algorithms Research 1
- Machine Learning and Algorithms 1
- Co-authors
- Andrea Lodi (2 shared papers)Tania Patiño (1 shared paper)Samuel Sánchez (1 shared paper)Rafael Mestre (1 shared paper)Maria Guix (1 shared paper)Marco De Corato (1 shared paper)Pierre Bonami (1 shared paper)
- Journals
- Top (1 paper)Operations Research (1 paper)Science Robotics (1 paper)The Journal of Open Source Software (1 paper)PolyPublie (École Polytechnique de Montréal) (1 paper)
- Partner nations
- CanadaUnited StatesItaly
In The Last Decade
Giulia Zarpellon
4 papers receiving 204 citations
Peers
Comparison fields: 5 of 52
- Industrial and Manufacturing Engineering 45
- Condensed Matter Physics 51
- Management Science and Operations Research 21
- Biomedical Engineering 74
- Numerical Analysis 8
Countries citing papers authored by Giulia Zarpellon
This map shows the geographic impact of Giulia Zarpellon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Giulia Zarpellon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giulia Zarpellon more than expected).
Fields of papers citing papers by Giulia Zarpellon
This network shows the impact of papers produced by Giulia Zarpellon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Giulia Zarpellon. The network helps show where Giulia Zarpellon may publish in the future.
Co-authors
The 7 scholars most cited alongside Giulia Zarpellon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 103 | |
| 2 | 2017 | 89 | |
| 3 | 2022 | 19 | |
| 4 | Machine learning algorithms in Mixed-Integer Programming | 2020 | 1 |
| 5 | 2023 | 0 |
About Giulia Zarpellon
Giulia Zarpellon is a scholar working on Numerical Analysis, Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research and Condensed Matter Physics, having authored 5 papers that have together received 212 indexed citations. Recurring topics across this work include Advanced Optimization Algorithms Research (3 papers), Micro and Nano Robotics (1 paper), Modular Robots and Swarm Intelligence (1 paper), Scheduling and Timetabling Solutions (1 paper), Metaheuristic Optimization Algorithms Research (1 paper), Machine Learning and Algorithms (1 paper), Polynomial and algebraic computation (1 paper) and Climate variability and models (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (45 citations), Condensed Matter Physics (51 citations), Management Science and Operations Research (21 citations), Biomedical Engineering (74 citations) and Numerical Analysis (8 citations). Giulia Zarpellon has collaborated with scholars based in Canada, United States and Italy. Frequent co-authors include Andrea Lodi, Tania Patiño, Samuel Sánchez, Rafael Mestre, Maria Guix, Marco De Corato and Pierre Bonami. Their work appears in journals such as Top, Operations Research, Science Robotics, The Journal of Open Source Software and PolyPublie (École Polytechnique de Montréal).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.