Cost optimisation modelling for airport capacity expansion problems in metropolitan areas
Abstract
The research described in this paper aimed to develop a cost optimisation model for determining the optimal approach to expanding airport capacity in metropolitan areas, taking into account demand uncertainties. The study began by analysing airport capacity expansion cases from diverse global regions to identify potential metropolitan-level solutions and key cost functions associated with airport capacity issues. A deterministic optimisation model was then developed using mixed-integer nonlinear programming (MINLP), incorporating six cost functions: capital cost, operation cost, delay cost, noise cost, operational readiness airport transfer (ORAT) cost and passenger access cost. The model was validated through a case study of the Sydney metropolitan area in Australia over a 50-year horizon and further tested for reliability using six additional experimental models. Subsequently, the deterministic model was adapted into a stochastic optimisation model employing Monte Carlo simulation to address the uncertainties in future traffic demand. This stochastic model was evaluated under three different demand scenarios, including the impact of the COVID-19 pandemic. The findings highlight the model’s reliability and reveal the trade-offs among the six cost functions over time, as well as the influence of demand uncertainty on identifying optimal solutions. The research underscores the effectiveness of integrating MINLP and Monte Carlo simulation methods for long-term airport capacity planning in metropolitan areas and addressing the diverse needs of airport stakeholders. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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Author's Biography
Woojin Choi Dr Woojin Choi PhD is the Lead Project Engineer for the Western Sydney Airport development project and serves as a Distinguished Technical Specialist at Bechtel Corporation. With 28 years’ experience in the airport and aviation sector, Woojin has contributed globally across the full project life cycle, specialising in airport infrastructure capacity expansion and optimising solutions to meet capacity targets. His areas of expertise encompass airport project initiation, planning, design, procurement, execution, commissioning, operations and facility management. Woojin holds a PhD in aviation and an MBA in aerospace. As a Visiting Professor at Korea Aeronautical University, he frequently lectures on airport planning, management and growth strategy to global airport operators and regulators. He also serves as the lead instructor for the Airport Management course in the Global Aviation Professional Programme (GAPP). Throughout his career, Woojin has worked on 19 airport capacity expansion projects across Asia, the Middle East, the USA, Europe and Oceania, including both greenfield and brownfield projects. He has held various roles, including project manager, economist, planner, researcher and operations and facilities manager. His expertise extends to research and strategic planning for the capacity expansion of airports and air transport systems, employing advanced methodologies such as data mining, multivariate data analysis, machine learning (ML), optimisation and simulation.
Dothang Truong Dr Dothang Truong PhD is the Associate Dean for the School of Graduate Studies and Professor of Aviation Data Science at Embry-Riddle Aeronautical University. He leads and oversees all academic, administrative and strategic aspects of the graduate programmes within the School of Graduate Studies. Dothang holds a PhD in manufacturing management and engineering from the University of Toledo. He is a member of the American Institute of Aeronautics and Astronautics (AIAA) Air Transportation Systems Technical Committee and serves on the National Science Foundation (NSF) Review Panel and the Transportation Research Board committee. His research focuses on natural language processing (NLP), machine learning (ML) and artificial intelligence (AI) for aviation safety and air transport efficiency. He has worked on multiple research grants funded by the Federal Aviation Administration (FAA), NSF, airlines, the FIRST grant and the Boeing Center for Aviation and Aerospace Safety. Dothang’s research is widely recognised, earning him the Frank Sorenson Research Award in 2022. He has published 65 peer-reviewed articles, one book and 38 conference proceedings, with about 2,200 citations, an h-index of 26 and an i10-index of 37. His most recent book, Data Science and Machine Learning for Non-programmers: Using SAS Enterprise Miner, is well recognised and reflects his dedication to making complex topics accessible to broader audiences.
