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Full-Text Articles in Systems Architecture
Scalable Multi-Agent Reinforcement Learning For Aggregation Systems, Tanvi Verma
Scalable Multi-Agent Reinforcement Learning For Aggregation Systems, Tanvi Verma
Dissertations and Theses Collection (Open Access)
Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc. for matching restaurants to customers. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. However, individuals (e.g., drivers, delivery boys) in the system are self interested and they try to maximize their own long term profit. The central entity has the full view of the system and …
Online Spatio - Temporal Demand Supply Matching, Meghna Lowalekar
Online Spatio - Temporal Demand Supply Matching, Meghna Lowalekar
Dissertations and Theses Collection (Open Access)
The rapid growth of cities in developing world coupled with the increase in rural to urban migration have led to cities being identified as the key actor for any nation's economy. Shared mobility has become an integral part of life of people in cities as it improves efficiency and enhances transportation accessibility. As a result, the mismatch between the demand and supply of shared mobility resources has a direct impact on people's life. Thus, the goal of my dissertation is to develop solution strategies for these real-time (online) spatio-temporal demand supply matching problems for shared mobility resources which can enhance …