how we can increase airline revenue through genetic algorithm?

1. ERP PROJECT
GENETIC ALGORITHM

2. TEAM MEMBERS
1).PINNAMANENI HITESH(A20405219048)
2).SAI SRINIVAS BANDURU(A20405219133)
3).INDU PEDDIBOYINA (A20405219067)
SUBMITTED TO:-
DR.MUSKAN GARG

3. INTRODUCTION
 Inspired by Darwin's theory about Evolution
 Part of Evolutionary Computing
 Rapid growing area of AI
EA Idea – I. Rechenberg – 1960 - “Evolution Strategies”
GA Invent & Develop – John Holland team – “Adaption and
Artificial Systems” - 1975
John Koza – 1992 – GA to evolve programs to perform certain task
–Genetic algorithm

4. Genetic algorithm
– Solution to a problem solved by genetic algorithms, is evolved
– Algorithm is started with a set of solutions (represented by
chromosomes) called Population
– Solutions from one population are taken and used to form a new
population for a better one.
– Solutions which are selected to form new solutions (offspring) are
selected according to their fitness - the more suitable they are
the more chances they have to reproduce
– Repeated until some condition is satisfied.

5. Some terminologies used in GA as follows:-
– 1. Chromosome – it is raw genetic information that GA deals with
(sequence of gene).
– 2. Individuals – it is a single solution to each of the chromosome.
– 3. Gene – it is basic set of instructions to build GA.
– 4. Fitness - The fitness of an individual in a genetic algorithm is the value
of an objective function for its genetic information.
– 5. Population - A population is a collection of individuals?>
The steps to be followed in GA are as follows:-
– 1. Selection of a population.
– 2. Assigning the fitness value.
– 3. Breeding – this process consist of three steps first is a selection of
parent, second is a crossing the parent to create new individuals
(crossover) and third is replacing the old individuals with new ones.
– 4. Termination .
– The population size and the fitness value is change from application to
application

6. Application of GA
– Nonlinear dynamical systems - predicting, data analysis
– Designing neural networks, both architecture and weights
– Robot trajectory
– Evolving LISP programs (genetic programming)
– Strategy planning
– Finding shape of protein molecules
– TSP and sequence scheduling
– Functions for creating images

7. STRATEGY PLANNING
AIRLINE REVENUE MANAGEMENT
REVENUE MANAGEMENT:-
– In the area of revenue management, the main aim is increase the revenue
– “Revenue Management is the art and science of predicting real-time
customer demand at the micro market and optimizing the price and
availability of products”
– The revenue management is also be known as yield management, but basis
difference in these is Revenue Management is more strategical and Yield
Management is more tactical,Revenue Management also allows us to apply
this in a wide area.
– Most of the RM systems uses the GA technique that is Genetic Algorithm.

8. – STEPS TO BE TAKEN WHILE IMPLEMENTED AIRLINE REVENUE
MANAGEMENT:-
– 1. Market segmentation
– 2. Price management
– 3. Demand forecasting
– 4. Availability
– 5. Reservation negotiation

9. Use of Genetic algorithm in Airline Revenue Management
Terminal 1
Terminal 2
Terminal 3
Terminal N
Yield
management
strategies to
analyze the
behaviour of
the system.
Optimization
using Genetic
Algorithm
Acccpeted request
of one terminal as
a result
optimization
Revenue
Management

10. The important data is categorized in various factors as follows
Terminal- There can be any number of terminals which can raise the request
for booking the seats in the aircraft for the particular flight
Seats-Total number of seats in the aircraft is fixed.
Customers: There can be number of customer classes. Customers can be
categorized on the basis of their priorities and the fare they can pay etc.
Time: The total number of booking period must be specified
Fare: For each requested seat the customer has to pay the fare.
Revenue: Initially the total revenue is 0. Total revenue will be increased
each time a booking is made.

11. Use of genetic Algorithm
– The only way we could explain it in easy way is by taking example,so
– Let’s take example regarding an airline(indigo,airindia,airasia,e.t.c).
For an example of this following assumptions are made:-
1) Total number of Terminals = 3
2) Types of customers = 2 (Business Customer, Leisure Customer)
3) Total booking period = 3 months
4) Time slots = 4 (slot 1(1st month), slot 2(2nd month), slot 3( 3rd month,
slot 4(few hours before flight takes off))
5) Initial total revenue = 0

12. – 6) Total seats = 70
– 7) Initial remaining seats = 70
– 8) Maximum booking allowed to the
customer at one time = 3
– 9) Minimum Booking for a month = 18
– 10) Maximum booking for a month =
30
– 11) Fare for the business customer
and leisure customer are shown in
table.
Business
customer
(customer
type 2) Terminal 1 Terminal 2 Terminal 3
Month 1 1500 2000 2500
Month 2 2000 2500 3000
Month 3 2500 3000 3500

13. Leisure
customer
(customer
type 1)
Terminal 1 Terminal 2 Terminal 3
Month 1 1000 1500 2000
Month 2 1500 2000 2500
Month 3 2000 2500 3000

14. – Now the front task is to book the airline seats. But the main task that runs behind
parallel with the front task is to maximize the profit while booking the seats.
– So according to the example the following requests are generated to book the
seats:
– Month1:
– Terminal1: customer type-2, number of seats- 1
– Terminal2: customer type-2, number of seats- 2
– Terminal3: customer type-1, number of seats- 1
– Among these three requests, one terminal is selected as this request is passed to
the proposed system and proposed system find out the optimized result. The
system gives the output as a terminal which contributes maximum to the revenue.

15. – In this example the second terminal is selected. The revenue generated by
the three terminals are 1500, 4000 and 2000.The second terminal is selected
out of the three terminals because it contributes maximum in the revenue.
Similarly the requests are accepted for every month until all the seats are
booked or the time period for booking is finished. Every time the system
selects the best terminal out of the requested terminal. In this way at last the
system generates the maximum possible revenue.
– The fitness function that is used in genetic algorithm to find out the best
terminal is revenue = no. Of seats * fare Fare depends upon the customer
type, time of request and terminal. For this example the total revenue
generated for the 70 seats is 168200 where as the revenue generated by the
simple booking system is 136500. The result can be shown by graph also:

16. Airline
Revenue of
proposed
system VS
simple system
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
Category 1
proposed system
simple system

17. DISCUSSION
Every business including airline industry always wants their profit
should be high. this target is achieved by using Genetic algorithm. As
problem stated above as a result it is better to show unavailability
rather than to close the terminal. When no. of customers’ requests
for seat reservation then the tickets gets booked on those terminals
where ticket costs more which further increases the revenue of the
airline company