Assistant Professor

Assistant Professor

Singapore University of Technology and Design

Posts by Collection

portfolio

Airport Slot Allocation and Capacity Declaration

Airport congestion poses a substantial challenge, leading to costly delays and negative environmental impacts. In response, airports employ slot allocation mechanisms to manage scheduled air traffic. This involves two key strategies: (i) setting declared capacity limits to govern the volume of flights at the airport, and (ii) optimizing flight schedules through slot allocation. This project is dedicated to devising analytical methods that integrate optimization techniques, machine learning, and queueing models. The goal is to support airport managers and slot coordinators in establishing efficient declared capacity limits and optimizing slot allocation decisions. This approach aims to yield benefits across the board, positively impacting airports, airlines, and passengers by mitigating delays and enhancing overall operational efficiency.

Resilient Airspace Operations

The seamless functioning of air travel heavily relies on enhanced air traffic management. At the heart of this system lies the Terminal Manoeuvring Area (TMA) –– a controlled airspace that handles a high volume of traffic, spanning approximately 50 to 200 nautical miles around the airport vicinity. This confined yet crucial airspace is where incoming and outgoing aircraft converge and diverge, making meticulous planning and execution of utmost importance to ensure smooth and safe operations. In this research, we propose an integrated optimization approach that utilizes matheuristic algorithms to optimize runway aircraft sequencing decisions in the TMA, including aircraft speeds, utilization of holding stacks, vectoring and point-merger implementation. Our proposed algorithm employs an iterative process that combines a Linear Programming (LP) model with a Genetic Algorithm, enabling the rapid generation of feasible solutions (within 5 second) and convergence to near-optimal solutions in approximately 5 minutes (for time-windows of 3 hours).

Integrated Airfield and Airspace Management

The seamless functioning of air travel heavily relies on enhanced air traffic management. At the heart of this system lies the Terminal Manoeuvring Area (TMA) –– a controlled airspace that handles a high volume of traffic, spanning approximately 100 to 200 nautical miles around the airport vicinity. This confined yet crucial airspace is where incoming and outgoing aircraft converge and diverge, making meticulous planning and execution of utmost importance to ensure smooth and safe operations. In this research, we propose an integrated optimization approach that utilizes matheuristic algorithms to optimize runway aircraft sequencing decisions in the TMA, including aircraft speeds, utilization of holding stacks, vectoring and point-merger implementation. Our proposed algorithm employs an iterative process that combines a Linear Programming (LP) model with a Genetic Algorithm, enabling the rapid generation of feasible solutions (within 5 second) and convergence to near-optimal solutions in approximately 5 minutes (for time-windows of 3 hours).

publications

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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