AIRCRAFT LANDING SCHEDULING USING EMBEDDED FLOWER POLLINATION ALGORITHM Ayman A.Ataher Mahmud Research Scholar , Department of Computer Science. SHUATS, Allahabad , Uttar Pradesh, India [email protected]Dr. Satakshi (Co-Advisor) Assistant Professor Department of Mathematics & Statistics. SHUATS, Allahabad, Uttar Pradesh, India [email protected]Prof.(Dr.) W. Jeberson Advisor and Head of Dept. Computer Science & I.T. SHUATS, Allahabad, Uttar Pradesh, India [email protected]ABSTRACT: Aircraft landing scheduling is an important issue in the air traffic management, that aims to decide the best allocation considering the landing time for assigning to a sequence with limit the aggregate of the deviation of the actual as well as target landing time under the state of safe landing. The paper presents an Embedded Flower Pollination Algorithm (EFPA) for aircraft landing schedule. The aircrafts are categorized depending on the class of each aircraft, small, large and heavy. Considering every single aircraft classes, runway occupation profile, landing time and separation time computation are done. EFPA is used to schedule the arrival of the airplanes. Context cognitive learning and runway balance methodology are contrived to upgrade the searching ability. Experimental outcomes demonstrate that the proposed EFPA outperforms compared with other existing strategies. Keywords: Aircraft Landing Scheduling, Embedded Flower Pollination Algorithm, Landing time, Separation time, Runway occupation profile, Runway balance strategy. 1. INTRODUCTION With the swift globalization in economies, the aviation business has assumed a vital part of social and economic frameworks [1]. Air transport has turned into the crucial modes of transportation for individual and business voyaging and business delivery, In this way the request of air transportation is expanded for numerous purposes. The increment in the number of aircraft departures and arrivals within a given time period at a certain airport causes risk air traffic [2]. Over the preceding few years, air transport has encountered expanded rivalry. These circumstances requires airline companies to improve their frameworks keeping in mind that the ultimate goal is to advance the planning of aircraft maintenance [3]. The test lies in simultaneously accomplishing safety, productivity, and equity which are regularly contending goals [4]. Because of constrained space for further infrastructural expansion, enhancing the productivity of air traffic management winds up basic for the aviation business to adapt to the normal request surge [5]. Due to this the airline companies are required to improve their frameworks. Numerous researchers distinguish airport as the bottlenecks of the air transport frameworks, where an applicable offer of the delays is frequently created by a wasteful management of the runway restrictions [6]. International Journal of Pure and Applied Mathematics Volume 119 No. 16 2018, 1719-1735 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ 1719
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AIRCRAFT LANDING SCHEDULING USING EMBEDDED FLOWER POLLINATION ALGORITHM
Ayman A.Ataher Mahmud
Research Scholar , Department of Computer Science. SHUATS, Allahabad , Uttar Pradesh, India
Aircraft landing scheduling is an important issue in the air traffic management, that aims to
decide the best allocation considering the landing time for assigning to a sequence with limit the
aggregate of the deviation of the actual as well as target landing time under the state of safe
landing. The paper presents an Embedded Flower Pollination Algorithm (EFPA) for aircraft
landing schedule. The aircrafts are categorized depending on the class of each aircraft, small,
large and heavy. Considering every single aircraft classes, runway occupation profile, landing
time and separation time computation are done. EFPA is used to schedule the arrival of the
airplanes. Context cognitive learning and runway balance methodology are contrived to upgrade
the searching ability. Experimental outcomes demonstrate that the proposed EFPA outperforms
compared with other existing strategies.
Keywords: Aircraft Landing Scheduling, Embedded Flower Pollination Algorithm, Landing time,
Separation time, Runway occupation profile, Runway balance strategy.
1. INTRODUCTION
With the swift globalization in economies, the aviation business has assumed a vital part of
social and economic frameworks [1]. Air transport has turned into the crucial modes of
transportation for individual and business voyaging and business delivery, In this way the request
of air transportation is expanded for numerous purposes. The increment in the number of aircraft
departures and arrivals within a given time period at a certain airport causes risk air traffic [2].
Over the preceding few years, air transport has encountered expanded rivalry. These
circumstances requires airline companies to improve their frameworks keeping in mind that the
ultimate goal is to advance the planning of aircraft maintenance [3]. The test lies in
simultaneously accomplishing safety, productivity, and equity which are regularly contending
goals [4]. Because of constrained space for further infrastructural expansion, enhancing the
productivity of air traffic management winds up basic for the aviation business to adapt to the
normal request surge [5]. Due to this the airline companies are required to improve their
frameworks. Numerous researchers distinguish airport as the bottlenecks of the air transport
frameworks, where an applicable offer of the delays is frequently created by a wasteful
management of the runway restrictions [6].
International Journal of Pure and Applied MathematicsVolume 119 No. 16 2018, 1719-1735ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/
1719
The airline business works in a strongly competitive environment where aircraft companies plan
to give best services at lower expenses [7]. The productivity of the air traffic framework is a
considerable traffic since air traffic delays have been costing billions of dollars to airlines every
year. Two issues engaged with modern air traffic framework are, air traffic control (ATC) and
air traffic flow management(ATM) [8]. ATC is a set of services returned by the air traffic
controllers to aircraft, to help in the protected, quick and effective execution of the flights. The
fundamental motivation behind air control is to prevent the crashes between the airplanes and the
ground or vehicles and collision in between the aircrafts [9]. Aircraft Landing Problem (ALP) is
one of the significant characteristics of air traffic flow management to keep up smooth air traffic
from airspace to the corresponding airport [10]. The Aircraft Landing Scheduling (ALS) issue is
choosing a landing time for each plane to such extent that each plane lands within a foresaid
time window. The separation criteria between the landing of a plane and of next progressive
plane is also considered. ALS in terminal zone essentially affects decreasing flight delays, so it is
one of the critical pieces of the air traffic management [11]. The issue turns out to be more
noteworthy for busy airports where loads of aircraft are expected to land at each time period and
at the same time these are limited resources(or runways) [2].
Scheduling of aircraft is a regular dynamic scheduling with numerous strong constraints, for
example, time urgency, space constraint, and resources limitation etc. Also there exist numerous
vulnerabilities and disturbances amid the scheduling procedure [12]. Due to the quick expand of
flights and passengers, the finite airport resources regularly can't satisfy the demand at busy time
optimal outcomes with fewer computational exertions as to an hour flight traffic planning
horizon. Concerning the solution strategy of the robust optimization utilizing the min-max regret
approach, the recommended proficient artificial bee colony algorithm could be an advantage to
ATC to acquire the close-to-optimal schedules within a sensible calculation time for practical
utilization. The computational outcomes showed the adequacy of the recommended algorithm by
contrast with other meta-heuristic methodologies on produced occasions. The recommended
algorithm outflanked other meta-heuristic methodologies in regards to target function and
calculation time.
3. AIRCRAFT LANDING SCHEDULING (ALS) USING EFPA
The goal of ALS is to decide the best combination of doling out the sequence and relating
landing time for a given set of aircraft to a runway. In another word, the scheduling algorithm of
ALS aims to limit the total of the deviations of target landing times and the real arrival times of
aircraft while fulfilling the minimum separation time between two adjoining airplane landing on
the same runway. This paper presents an EFPA for aircraft landing schedule. The standard FPA
is a persistent optimization algorithm. Nonetheless, when managing discrete optimizations like
ALS issue, its deficiencies for discrete optimization will be uncovered straightforwardly. EFPA
is a combination of FPA and runway balance technique. At first, the aircraft are partitioned as
separate classes, for example, small, large and heavy classes. For every aircraft classes, runway
occupation profile, landing time and separation time are computed, EFPA is used to plan the
landing of the aircraft as presented in Figure 1.
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Figure 1: The Proposed Framework
3.1 Parameter Calculation
The runways can be possessed by just a solitary aircraft at once each, and a division time
should be guaranteed between any combination of the airship. A minimum partition between each pair of progressive aircraft focused on the sort and relative spots of the two aircraft must be guaranteed for scheduling reason. For processing the assessed time entry of aircraft it is critical to know the bits of knowledge about landing and minimum separation times between each aircraft classes. Minimum separation time is the security distance betwixt the aircraft that can be translated in a separation time by considering the distinctive aircraft speeds. Expected that the runways are self-governing, inferring that the flight ways for all runways being utilized are confined sufficiently, so the runways can be dealt with independently. Amid the planning time frame the runway considered might be utilized for arriving flights. 3.1.1 Runway Occupation Profile (ROP).
The rectangular ranges of an aerodrome arranged for the arrival and also depart are of the airship is a runway. Which is most imperative piece of an airfield. A mischance on a runway will influence the air terminal accessibility and any mishap on a runway typically causes a few reasons of harm and wounds. Runway occupation time is the measure of time for which every
airplane occupies the runway. ROP is characterized as a vector RR cTcT ,......, 11 that contains
the time RT and aircraft class Rc of the newest landing on every runway. A runway without any
operations scheduled is meant in a ROP as (-1,-1). Estimation of runway occupation profile is quickly talked about in [22]. The runway occupation profile can be evaluated by the accompanying condition (1)
)( 21 TTTROP R (1)
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3.2 Embedded Flower Pollination Algorithm(EFPA).
The standard FPA is a continuous optimization algorithm. However, when dealing with discrete optimizations like ALS problem, FPA may not be suitable, so Embedded FPA is introduced which is a combination of FPA and runway balance strategy. Suppose if there are ten aircraft to be scheduled and three runways available aimed at assigning, then the candidate solution should be a ten-dimensional vector and every decision variable should be one, two or three.
Figure 2: Pseudocode for EFPA The pseudo code for embedded FPA with runway balance strategy is presented in Figure 2.
Begin
Initialize Z of P flowers
\\ Each flower represents a scheduling sequence for an aircraft landing
instance.
Find the Best Solution O in the initial population.
Define a switch probability 1,0q
While Generationt max
For Pu :1 (all the flowers in the population)
If qrand
(Global Search Process)
Carryout context cognitive learning and get the via Eq.2;
Else
(Local Search Process)
Carryout walking one strategy and get the via Eqs.3-5;
End if
(Turbulent Process)
Carryout turbulent operator and get xT
(Runway Balance)
Carryout the runway balance and get xB
Find the best one from 1t
ux , xT and xB ,then set it to 1t
ux ;
Evaluate the scheduling sequence 1t
ux ;
If t
u
t
u xfitnessxfitness 1
Update flower ux
End if
End for
Find the current best scheduling sequence O
End while
Output the best scheduling sequence
End
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Initialization: In the initialization process relied upon runways, P candidate solutions are produced, where P is the populace size and set to ten on account of ten aircraft. For each solution, a Latest Active Time (LAT), whose initial value should be zero and a Target Landing
Time uT of the considerable number of aircraft, should be arranged in ascending sequence, which
says that Target Landing Time of the former aircraft in sorted sequence might be after the
preceding one. The chief aircraft 1u will land at an arbitrary runway. For every sorted aircraft
ku and 1ku , if11 ,
kkkk uuuu STT , aircraft 1ku is assigned to the runway that ku lands on and the
last dynamic time of the runway which 1ku lands on are to be updated and its value has got to be
set to1kuT . Otherwise, the airplane 1ku should be appointed to another runway with smallest last
active time and the last active time of this runway is set to1kuT . In particular, aircraft ku and
1ku are allotted to various runways. This generated procedure of initialization repeats until
P candidate solutions are generated.
Context Cognitive Learning: In nature, the biotic pollination process of flowers often resorts to
pollinator and honeybees are a good instance of pollinator. Honey bees get pollen as of one
flower and then take it to another flower with same species to help the plants to finish the biotic
pollination after flying a long distance. When the honeybees complete the transportation mission,
they will get together and communicate amongst themselves to share the experience of nectar
resources and biotic pollination. By sharing the experience, honeybees will understand better
places for pollination in the subsequent time. Namely, this ability of pollinators is described as
context cognition.
For simplicity, better solutions are defined as the finest solutions in the population. This
subset is named as ES (Elite Set) and the ES size is set to P3.0 . The context cognitive learning
is depicted using the equation given in (2).
SizeESkAvPuotherwisex
randrandex
t
vu
t
vkt
vu _1,1,1,,
,
,
21,1
,
(2)
At this time, t
vke , is the thv variable of thk the individual in ES and the evolution generation is t .
The t
vke , should be chosen randomly which could enhance the diversity of the population to some
extent. t
vux , is the thv variable of thu individual of the population at tht generation.
Walking One Strategy: Standard FPA use mutation operator of differential evolution to do the
local search. The major purpose of the local search is to find a better solution in neighbor space.
Thus, walking one strategy is launched in this discrete version FPA. It simulates the local
cognitive behavior of pollen. In the local search process, a crossover rate CR is set to 0.01. the
complete process of walking one strategy is modeled by succeeding equations (3), (4) and (5).
randroundstepsize vu 21, (3)
1,mod ,,, RstepsizexmutVec vu
t
vuvu (4)
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otherwisex
CRrandmutVecx
t
vu
jit
vu,
,
,
,1
,
(5)
Where, AvPu 1,1 , R is the number of runways. From equation (3), it can be discovered
that the variable vustepsize ,can only be -1, 0 and 1. This ensures that the searching space of
walking one strategy is around the existing solution vux , . And vumutVec , is the mutation vector
after carried out the walking strategy and it is also an input variable of equation (5). The essence
of (5) is a crossover operator.
Turbulent Operator: Context cognitive learning and walking one strategy are used as the main
procedure of global search and local search in proposed EFPA. When facing large scales
scheduling, their limited searching ability makes it hard to jump out of the local optimal. Thus,
the turbulent operator is applied to the proposed EFPA to ameliorate the population diversity.
This turbulent process contains swap operator and inserts operator.
Runway Balance Strategy: Consider a scheduling task with 1000 aircraft and 3 runways for
handling. On account of its huge quantity of aircraft and little runways to assign, the load of
every runway may be severe unbalance. For example, if the current best solution shows 451
aircraft will land on the first runway and also a load of other two runways are 444 and 105. It can
be easily discovered that loads of three runways are not balanced. And nearly ninety percent
aircraft are assigned to first two runways. If scheduling the aircraft by using this best scheduling
solution, the first runway and the second runway are occupied for landing almost all the time, but
the third runway is unused in most portion of the scheduling process, which wastes many
resources is found. Thus, balancing the load of each provided runway is a vital problem to be
handled. Thus, runway balance strategy is designed here to balance the load of each runway.
Firstly, the number of aircraft on every runway should be calculated, an aircraft should be chosen
randomly from the runway which count value of landing aircraft is the maximum. Then the
runway with least aircraft to land on should also be found and assigned to chosen aircraft to land
on. If the quantity of airplanes on runways, minimum and maximum, is equivalent, then a
runway ought to be selected randomly. Furthermore, the process will continue until the deviation
of those runway count values is below one. The pseudocode of runway balance strategy is
presented in Figure 3.
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Figure 3: Pseudocode for Runway Balance Strategy
4. RESULT AND DISCUSSION
The proposed embedded flower pollination algorithm for aircraft landing scheduling is simulated
in JAVA platform and the dataset comprises of flight landing and also takeoff details for each of
the commercial flight inside the USA. So as to scrutinize the proposed work’s performance,
measured the different parameters on basis of the execution situation. Figure 4 reveals the
relative comparison of the proposed EFPA algorithm with prevailing KH optimization and
Cuckoo Search optimization. Table 1 shows the comparative analysis of the proposed EFPA and
the existing Krill Herd (KH) and Cuckoo optimization algorithms in term of CPU time.
Problem
Size
KH Cuckoo Proposed
EFPA
100 20 7 5
200 48 35 20
300 55 63 48
400 100 90 84
500 135 125 112
Table 1: CPU Time (seconds)
Begin
Read Rmax , Maximum number of Runways
Rmin Minimum number of Runways
Countmax , Number of aircrafts land on Rmax
Countmin , Number of aircrafts land on Rmin
Function res= Runway_Bal (solution)
[maxR, maxCount]= findMaxRunway (solution)
[minR, minCount]= findMinRunway (solution)
While 1minmax CountCount
Select an aircraft from Rmax randomly
Assign the selected aircraft to Rmin
Update the solution
[maxR, maxCount]= findMaxRunway (solution)
[minR, minCount]= findMinRunway (solution)
End while
res= solution
End
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Figure 4: Comparision of EFPA with existing Methods
. It is observed that as size increases , the CPU time for processing also increases. Among
the three compared techniques, the proposed EFPA outperforms as compared to existing
methods for the entire problem sizes.
Aircraft Scheduling: The aircraft scheduling decides the best blend of assigning sequence also
corresponding landing time for a specified set of the airplane to a runway. The scheduling
algorithm of ALP should diminish the sum of the deviations of target landing times and the
actual landing times of aircraft by concurrently fulfilling the least separation time between two
adjacent airplane landing on the same runway.
Figure 5: Performance Analysis of Aircraft Scheduling
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Figure 5 analyzes the performance of aircraft scheduled for the proposed EFPA and the existing
KH and also Cuckoo optimization algorithms. The proposed EFPA outperforms both the KH and
cuckoo optimization techniques in term of effective aircraft scheduling.
Computation Time: Computation time (additionally called "running time") is the time span
required to play out a computational process. In aircraft schedule, the length of the time essential
to play out a schedule update is called as a computational time (The time difference between start
and end time of aircrafts schedule). The calculation times for the proposed EFPA and the
existing KH and Cuckoo optimization algorithms are depicted in Table 2.
Table 2: Comparative Analysis of Computation time for Small, Large and Heavy Aircrafts
Table 2, summaries all class of aircrafts and observed that the computation time varies for the small, large and heavy aircraft that are individually depicted.
(a) Small Aircraft
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(b) Large Aircraft
(c) Heavy Aircrafts Figure 6: Performance analysis of Computation time for the existing KH, Cuckoo and the
proposed EFPA for (a) Small aircraft (b) Large aircraft and (c) Heavy aircrafts
Figure 6 analyzes the performance concerning computation time intended for the existing KH, Cuckoo and the proposed EFPA for the small, large and heavy aircraft that are individually depicted. Figure 6(a) demonstrates the graphical portrayal of computation time for small aircraft. For any quantity of problem sizes, the proposed EFPA shows superior performance, that is, low computation time than the other conventional techniques. Figure 6(b) displays the graphical delineation of computation time for large aircraft. Here, on considering KH and the proposed EFPA, there is a vast variation, among which the proposed EFPA performs well. On considering Cuckoo and the proposed EFPA, though there is a slight variation in the computation time, the proposed EFPA outperforms the cuckoo technique. Figure 6(c) shows the graphical portrayal of computation time for heavy aircraft. Here, the existing Cuckoo optimization technique has
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computation time approximately equal to the proposed EFPA but the existing KH shows poor performance. When comparing all the three techniques, the proposed EFPA shows superior performance. 4.4 Aircraft Scheduling Distribution The scheduling of aircraft based on the target landing time is established in the graph displays in Figure 7.
Figure 7: Aircraft Scheduling Distribution based on Target Landing Time
It is deduced from the Figure 7 that these ten aircrafts are assigned to three runways. The aircraft1, aircraft 4, aircraft 7 and aircraft 10 will land at first runway. Aircraft 2, aircraft 5 and aircraft 8 will be assigned to the second runway. And the rest aircrafts will get the right to land at the third runway. 5. CONCLUSION The ALS problem at an airport has become exceptionally challenging on account of the expansion of air traffic. Customarily, this problem is broadly examined by defining it as an optimization model resolved by different operation research approaches. In any case, these methodologies are not ready to catch the dynamic nature of the ALS issue fittingly and handle vulnerability effectively. To overcome such demerits, this paper introduces an Embedded Flower Pollination Algorithm (EFPA) for aircraft landing scheduling, which embeds Runway Balance strategy with the FPA. The proposed EFPA is contrasted with the existing techniques with metrics CPU time, computation time and aircraft scheduling distribution. Experimental outcome established that the proposed EFPA is efficient to get optimal results when compared with other conventional techniques.
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