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.


The authors are grateful to Flightradar24 for providing data on landing time and time of arrival to a parking bay which helped in calculating the taxi-in time for each arriving aircraft at the IGIA, New Delhi, and for supplying accurate data on ‘from which runway end aircraft are landing, each of their taxi routes and finding their parking stand numbers’.

1. Model of 600 Flts - 02 Apr 23.xlsx (805 kB)
Model of 600 flights 02 Apr 2023

2. Model of 600 Flts - 01 May 23.xlsx (773 kB)
Model of 600 flights 01 May 2023

3. Time Saving on 02 April 2023.xlsx (225 kB)
Time saving on 02 April 2023

4. Time Saving on 01 May 2023.xlsx (277 kB)
Time saving on 01 May 2023

02 Apr 23 Optimised Output.pdf (1099 kB)
Model pdf 02-04-23 Optimized taxi-in time 4.82 minutes

01 May 23 Optimised Output.pdf (1153 kB)
Model pdf 01-05-23 Optimized taxi-in time 4.62 minutes



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