Nikola Ivanov, Fedja Netjasov, Radosav Jovanovic Utilizing schedule buffers to reduce propagated delay: A new approach for tactical Air Traffic Flow Management slot allocation Nikola Ivanov, Fedja Netjasov, Radosav Jovanovic

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Summary – key messages Utilizing schedule buffers in ATFM slot allocation may reduce delay propagation to subsequent flights (initial research results) and also increase airport slot adherence at slot coordinated airports. But, efficiency is in conflict with equity. Binding European ANSPs to meet SES Performance targets for ‘capacity’ KPA, i.e. ATFM en-route delay minutes, may not be sufficient to AOs. 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Introduction Establishing demand-capacity balance BUFFER BUFFER Predictability? Predictability? CAPACITY DEMAND NM 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Introduction Schedule buffers Source: Wandeler, 2014 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Delays in Europe – different perspectives Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Delays in Europe – different perspectives 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Performance driven decision-making Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Performance driven decision-making SES goals –> four KPAs -> monitored KPIs -> vs. SES Performance targets One of the binding performance targets for the NM and ANSPs is the average en-route ATFM delay per flight, adopted as the KPI for ‘capacity’ KPA ANSPs decision making: ATFM delay or mandatory re-routing? From AO’s perspective, re-routings are merely seen as an instrument to bring down ATFM delay figures to meet ANSP delay targets [EUROCONTROL, Annual Network Operations Report 2014, Annex I - Airspace Users View, 2015] 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Practical implications Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Practical implications AOs concern regarding the delay metrics used in the SES Performance Scheme - focus on ATFM delay which is of different order of magnitude compared to actual all-cause delay [T. Van Der Veldt, “Flight Efficiency now : Establishing Common Objectives,” Network Manager User Forum, 2015] PRC - a better understanding of ATM contribution towards propagated delays is needed, as well as to identify potential strategies to deal with the delay propagation [Performance Review Report 2015]. 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Modelling framework Compare CASA (min. ATFM delay) with a modified slot allocation algorithm (min. delay propagation) The NM perspective of the demand-capacity imbalance problem Focusing on morning rotations and delay: AO focus on maintaining schedules [Jetzki, 2009] Schedule buffers remain at large [Villemeur et al. 2011] Delay propagation starts building up [EUROCONTROL CODA, 2015] Airport slot adherence 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Assumptions Deterministic model Static ATFM regulation Schedule buffer distribution per market segment [Jetzki, 2009] Simulated CASA algorithm Propagation of delay (potential) Minutes of delay, rather than delay costs 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Model ATFM slots 𝑁 𝑆 = 𝐶 𝑅 ∙( 𝑅 𝐸𝑛𝑑 − 𝑅 𝑆𝑡𝑎𝑟𝑡 ) 𝐿 𝑖 = 𝑅 𝑆𝑡𝑎𝑟𝑡 +(𝑖−1)/ 𝐶 𝑅 , 𝑖∈ 2, …, 𝑁 𝑆 , 𝐿 1 = 𝑅 𝑆𝑡𝑎𝑟𝑡 𝑈 𝑖 = 𝐿 𝑖 +1/ 𝐶 𝑅 −1, 𝑖∈ 1,…, 𝑁 𝑆−1 , 𝑈 𝑁 𝑆 = 𝑅 𝐸𝑛𝑑 ATFM and propagated delay 𝑑 𝑓𝑖 𝐴 = max 𝐸𝑇𝑂 𝑓 , 𝐿 𝑖 − 𝐸𝑇𝑂 𝑓 𝑑 𝑓𝑖 𝑃 = 0, max 𝐸𝑇𝑂 𝑓 , 𝐿 𝑖 − 𝐸𝑇𝑂 𝑓 − SB 𝑓 ≤0 max 𝐸𝑇𝑂 𝑓 , 𝐿 𝑖 − 𝐸𝑇𝑂 𝑓 − SB 𝑓 ,𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Model MINP (assignment problem) Minimize: 𝑚𝑖𝑛 𝑓∈ 𝐹 𝑅 𝑖∈ 𝑆 𝑓 𝑑 𝑓𝑖 𝑃 𝑥 𝑓𝑖 Subject to: 𝑓∈ 𝐹 𝑅 𝑖∈ 𝑆 𝑓 𝑥 𝑓𝑖 ≤1 𝑖∈ 𝑆 𝑓 𝑥 𝑓𝑖 =1, ∀𝑓∈ 𝐹 𝑅 𝑥 𝑓𝑖 =0 ⋁ 1,∀𝑓∈ 𝐹 𝑅 ,∀𝑖∈ 𝑆 𝑓 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Model APSA Airport slot adherence @ slot coordinated airports 𝑎 𝑓𝑑𝑒𝑝 = 𝑆𝑇𝐷 𝑓 −𝐶𝑂𝐵𝑇 𝑓 𝑎 𝑓𝑎𝑟𝑟 = 𝑆𝑇𝐴 𝑓 − 𝐶𝑇𝐴 𝑓 Sequential algorithm Sorts flights with remaining schedule buffer ( 𝑟 𝑓𝑖 𝑃 = 𝑆𝐵 𝑓 − 𝑑 𝑓𝑖 𝐴 >0) in descending order and increase ATFM delay until: no schedule buffer remains for the flight: 𝑟 𝑓𝑖 𝑃 =0 or airport slot adherence is maximized for that flight 𝑎 𝑓 =0 or any additional ATFM delay will lead to moving the flight to already allocated ATFM slots. 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Numerical experiment Single constraint ATC en-route sector (fixed network): 2 hour long regulation capacity reduced from 20 to 10 a/c per hour (rate) Demand 18 flights over the period (9 H&S, 6LCC, 3P2P) Schedule buffer distribution: LCC: U[-5min, 15min] H&S: U[-4min, 10min] P2P: U[-3min, 7min] EOBT aligned with STD, except for one flight - ‘early filer’ EOBT 20 minutes before STD. 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Results 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Results Vary schedule buffers for the same traffic (30 random samples For this particular traffic demand and capacity constraints, MINP could save above 5% of delay propagated to subsequent flights compared to CASA. Slot adherence improvements ranged between 0 and 25 minutes 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Discussion For the regulation and traffic demand considered, propagated delay savings depend on the schedule buffer distribution. More flights with longer schedule buffers – the more room for efficiency improvement (propagated delay and airport slot adherence)… But it comes at the expense of ATFM delay increase (equity). Mind the SES delay targets! – the NM and ANSPs are struggling to bring the figures down. Number of ‘scenarios’ more than doubled: 2076, 3371 and 5226, in 2012, 2013 and 2014 respectively! 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Further steps It is a question how MINP policy would affect scheduling practices of AOs? Optimal level of delays (schedule buffers) [Villemeur et al. 2011] But what about capacity buffers? There is a mismatch between the predictability for ANSPs and flexibility for AOs -> substantial (and costly) capacity buffers built into ANSPs planning decisions as well [Jovanovic et al., 2015] How to timely coordinate and align demand and capacity side decisions (predictability for ANSPs vs. flexibility for AOs) to reduce buffer costs on both sides of inequality and incentivize more cost-efficient outcome? (H2020-SESAR-2015-1 : Coordinated capacity ordering and trajectory pricing for better-performing ATM) 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA

Utilizing schedule buffers to reduce propagated delay Ivanov N., Netjasov F., Jovanovic R. – Utilizing schedule buffers to reduce propagated delay Ivanov, Netjasov, Jovanovic Thank you! Questions? 21-Jun-2016 ICRAT 2016, Drexler University, Philadelphia PA