Stochastic Optimization to Reduce Aircraft Taxi-in Time at IGIA, New Delhi

Abstract

Verbatim from Embry-Riddle Scholarly Commons. Not paraphrased, not summarized.

Since there is an uncertainty in the arrival times of flights, pre-scheduled allocation of runways and stands and the subsequent first-come-first-served treatment results in a sub-optimal allocation of runways and stands, this is the prime reason for the unusual delays in taxi-in times at IGIA, New Delhi. We simulated the arrival pattern of aircraft and utilized stochastic optimization to arrive at the best runway-stands allocation for a day. Optimization is done using a GRG Non-Linear algorithm in the Frontline Systems Analytic Solver platform. We applied this model to eight representative scenarios of two different days. Our results show that without altering any physical infrastructure and using this stochastic simulation model, there is a potential 38.97% improvement in the average taxiing time of the arriving aircraft, assuming a constraint of simultaneous arrivals of two aircraft within a minute. A simulated run optimization technique, if applied at the IGIA, would produce huge savings in passengers’ time, additional fuel costs, and, after all, environmental degradation.

Authors

• Das, Rajib Embry-Riddle Aeronautical University
• Ghosh, Saileswar Embry-Riddle Aeronautical University
• Desai, Rajendra Embry-Riddle Aeronautical University
• Bhuin, Pijus Kanti Embry-Riddle Aeronautical University
• Agarwal, Stuti Embry-Riddle Aeronautical University

Keywords

• airport
• airport traffic management
• congestion
• delays
• IGIA
• simulation
• stochastic optimization
• taxi-in time.
• Analysis
• Management and Operations
• Multi-Vehicle Systems and Air Traffic Control
• Operational Research