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|Title:||AI-empowered community work organization||Authors:||Zhao, Han Qing||Keywords:||Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Zhao, H. Q. (2021). AI-empowered community work organization. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148830||Project:||SCSE20-0327||Abstract:||In response to this period of peril and uncertainty due to Covid-19, many people have come up with creative ways to provide support for those affected. Such efforts are important to help the community deal with Covid-19 or other potential crisis in the future. For these efforts to be successful, effective coordination between those who require help and those who are willing to help is important. Many existing applications attempt to do so by matching between the needs of those who seeks help and help provided by those willing to help. However, there are limitations to these applications. Some rely on manual matching, which may result in biasness, and thus unfairness, or inefficiency in matching. This project aims to address the limitations of these platforms by attempting to answer the question: How to prioritise limited resources while being aware of dynamic contexts and situation to allocate or recommend allocation of resources in a fair manner. A web application was developed over the course of the project whereby donors can make donations, beneficiaries can request for donated items while volunteers can respond to delivery requests. This web application serves as a prototype platform that matches those that need help in society with those that are willing to offer help. Additionally, an algorithm was developed to allocate resources in a fair manner using Lyapunov optimisation. Finally, simulations were conducted to ascertain the performance of the algorithm and determine if it can allocate resources in a fair manner. Results of the simulation shows that the Lyapunov optimisation was able to achieve fair allocation of resources as compared to random allocation or allocation based on a first-come-first-served basis, especially when different beneficiaries have different need for the item. The algorithm was able to allocate resources in such a way that it generates more value for those that needs the donation more, such as those who are of a lower household status.||URI:||https://hdl.handle.net/10356/148830||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
Updated on May 16, 2022
Updated on May 16, 2022
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