Risk-averse two-stage stochastic programming model for gate assignment problem under arrival time uncertainty

• Published in: Computers & industrial engineering Vol. 207; p. 111269
• Main Authors: Zhang, Chenliang, Ng, Kam K.H., Jin, Zhongyi, Sun, Xuting, Qin, Yichen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2025
• Subjects: Benders-based branch-and-cut; Conditional-value-at-risk; Gate assignment operations; Risk aversion; Two-stage stochastic programming; Risk aversion; Conditional-value-at-risk; Benders-based branch-and-cut; Gate assignment operations; Two-stage stochastic programming
• ISSN: 0360-8352
• DOI: 10.1016/j.cie.2025.111269

•A risk-averse two-stage stochastic programming model for gate assignment problem is studied.•An efficient multi-cut Benders-based branch-and-cut method is devised.•Extensive computational experiments show the performance of the solution method and model. The gate assignment problem (GAP) is a crucial task in airport operations, aiming to allocate aircraft to terminal gates and aprons. Airport operations are subject to various uncertainties, which can affect the quality and feasibility of the gate assignment plans. Therefore, it is essential to consider these uncertainties and associated risks when constructing an effective gate assignment plan. This paper proposes a risk-averse two-stage stochastic programming (RA-TSSP) model for the GAP, where aircraft arrival times are modelled as uncertain parameters, and the conditional value at risk is adopted as the risk measure. The proposed model is reformulated as a mixed-integer linear programming model, and the sample average approximation method is employed to enhance tractability. Additionally, we propose a risk-averse multi-cut Benders-based branch-and-cut (RA-MC-BBC) method to solve the RA-TSSP model for the GAP efficiently. The performance of the proposed RA-TSSP model is validated through numerical experiments and a case study at Xiamen Gaoqi Airport in China. Results show that the proposed model offers valuable insights for airport decision-makers in managing the uncertainty and risk associated with gate assignment decision-making. Additionally, the results of the scalability analysis demonstrate significant statistical improvements in the RA-MC-BBC method.