Related papers: Data-Driven Revenue Management for Air Cargo
Problem definition: Drones, despite being acknowledged as a transformative force in the city logistics sector, are unable to execute the \textit{last-meter delivery} (unloading goods directly to customers' doorsteps) due to airspace…
Problem definition: Transportation terminals such as airports often experience persistent oversupply of idle ride-sourcing drivers, resulting in long driver waiting times and inducing externalities such as curbside congestion. While…
Traditional diesel-based airport service vehicles are characterized by a heavy-duty, high-usage-frequency nature and a high carbon intensity per vehicle per hour. Transforming these vehicles into electric vehicles would reduce CO2 emissions…
In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee balanced consumption across different resources. For instance, in the retailing industry, ensuring a balanced consumption of…
The joint optimization of physical parameters and controllers in robotic systems is challenging. This is due to the difficulties of predicting the effect that changes in physical parameters have on final performances. At the same time,…
Guaranteeing stringent data freshness for low-altitude unmanned aerial vehicles (UAVs) in shared spectrum forces a critical trade-off between two operational costs: the UAV's own energy consumption and the occupation of terrestrial channel…
This work proposes a framework for the robust design of UAV-assisted wireless networks that combine 3D trajectory optimization with user mobility prediction to address dynamic resource allocation challenges. We proposed a sparse…
Accurate aircraft-mass estimation is critical to airlines from the safety-management and performance-optimization viewpoints. Overloading an aircraft with passengers and baggage might result in a safety hazard. In contrast, not fully…
High-quality machine learning models are dependent on access to high-quality training data. When the data are not already available, it is tedious and costly to obtain them. Data markets help with identifying valuable training data: model…
Air Traffic Flow and Capacity Management (ATFCM) is one of the constituent parts of Air Traffic Management (ATM). The goal of ATFCM is to make airport and airspace capacity meet traffic demand and, when capacity opportunities are exhausted,…
The assortment problem in revenue management is the problem of deciding which subset of products to offer to consumers in order to maximise revenue. A simple and natural strategy is to select the best assortment out of all those that are…
Motivated by applications in online marketplaces such as ride-hailing, we study how strategic servers impact the system performance. We consider a discrete-time process in which, heterogeneous types of customers and servers arrive. Each…
In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a…
Quantum computing provides a novel approach to addressing conventionally intractable issues in large-scale optimization. Space logistics missions require the efficient routing of payloads, spacecraft, and resources across complex networks,…
Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that in the presence of users with varying price sensitivity (responsiveness), conventional monotonic pricing can lead to…
Industrial refrigeration systems have substantial energy needs, but optimizing their operation remains challenging due to the tension between minimizing energy costs and meeting strict cooling requirements. Load shifting--strategic…
Accurate prediction of flight-level passenger traffic is of paramount importance in airline operations, influencing key decisions from pricing to route optimization. This study introduces a novel, multimodal deep learning approach to the…
Reinforcement learning (RL) has shown promise in solving various combinatorial optimization problems. However, conventional RL faces challenges when dealing with complex, real-world constraints, especially when action space feasibility is…
As urban logistics demand continues to grow, UAV delivery has become a key solution to improve delivery efficiency, reduce traffic congestion, and lower logistics costs. However, to fully leverage the potential of UAV delivery networks,…
Intensified netload uncertainty and variability led to the concept of a new market product, flexible ramping product (FRP). The main goal of FRP is to enhance the generation dispatch flexibility inside real-time (RT) markets to mitigate…