Config Files
Most control of the simulation is done via configuration files written in YAML format.
Simulation Controls
scenario: Three Market Test Network
simulation_controls:
random_seed: 42
num_trials: 1
num_samples: 300
sys_k_factor: 0.1
mkt_k_factor: 0.2
pax_type_k_factor: 0.4
tf_k_factor: 0.1
tot_z_factor: 2.0
tf_z_factor: 2.0
prorate_revenue: true
dwm_lite: false
max_connect_time: 120
disable_ap: false
demand_multiplier: 1.0
manual_paths: true
RM Systems
The rm_systems key allows the user
to define one or more revenue management systems that may be used by carriers.
These systems can either be defined as a list, where each item in the list defines both a name and steps, or you can write the same instruction as a nested mapping, with the names as keys and the values giving the other attributes of each RM system, (for now, just a list of steps) like this:
rm_systems:
- name: rm_test1
steps:
- step_type: untruncation #(1)!
name: untruncation
algorithm: em
kind: leg
- step_type: forecast
name: forecast
algorithm: exp_smoothing
alpha: 0.1
kind: leg
- step_type: optimization
name: optimization
algorithm: emsrb #(2)!
kind: leg
Untruncation allows us to figure out how much demand was censored.- If you define different RM systems, you can attach different optimization algorithms, such as ProBP.
rm_systems:
rm_test1:
steps:
- step_type: untruncation #(1)!
name: untruncation
algorithm: em
kind: leg
- step_type: forecast
name: forecast
algorithm: exp_smoothing
alpha: 0.1
kind: leg
- step_type: optimization
name: optimization
algorithm: emsrb #(2)!
kind: leg
- Untruncation allows us to figure out how much demand was censored.
- If you define different RM systems, you can attach different optimization algorithms, such as ProBP.
Passenger Choice Models
choice_models:
business:
kind: pods
emult: 1.6
basefare_mult: 2.5
path_quality: [38.30, 0.10]
preferred_airline: [-12.29, 0.17]
tolerance: 2.0
r1: 0.30
r2: 0.10
r3: 0.20
r4: 0.15
leisure:
kind: pods
emult: 1.5
basefare_mult: 1.0
path_quality: [2.02, 0.12]
preferred_airline: [-1.98, 0.11]
tolerance: 5.0
r1: 0.30
r2: 0.15
r3: 0.25
r4: 0.20
Define Carriers
airlines:
- name: AL1
rm_system: rm_test1
- name: AL2
rm_system: rm_test1
- name: AL3
rm_system: rm_test1
- name: AL4
rm_system: rm_test1
Define Booking Classes
Data Collection Points (DCPs)
Booking Curves
booking_curves:
- name: '1'
curve:
63: 0.01
56: 0.02
49: 0.05
42: 0.13
35: 0.19
31: 0.23
28: 0.29
24: 0.35
21: 0.45
17: 0.54
14: 0.67
10: 0.79
7: 0.86
5: 0.91
3: 0.96
1: 1.0
- name: '2'
curve:
63: 0.13
56: 0.22
49: 0.37
42: 0.52
35: 0.64
31: 0.7
28: 0.75
24: 0.78
21: 0.83
17: 0.87
14: 0.91
10: 0.94
7: 0.96
5: 0.98
3: 0.99
1: 1.0
- name: '3'
curve:
63: 0.04
56: 0.06
49: 0.12
42: 0.26
35: 0.35
31: 0.41
28: 0.48
24: 0.54
21: 0.63
17: 0.7
14: 0.81
10: 0.88
7: 0.93
5: 0.96
3: 0.98
1: 1.0
- name: '4'
curve:
63: 0.21
56: 0.35
49: 0.53
42: 0.67
35: 0.76
31: 0.8
28: 0.83
24: 0.85
21: 0.88
17: 0.91
14: 0.94
10: 0.96
7: 0.97
5: 0.98
3: 0.99
1: 1.0
Legs
legs:
- carrier: AL1
fltno: 1
orig: BOS
dest: SFO
date: '2020-01-01'
dep_time: 08:00
arr_time: '10:00'
capacity: 100
distance: 867.0
- carrier: AL2
fltno: 2
orig: BOS
dest: SFO
date: '2020-01-01'
dep_time: '14:00'
arr_time: '16:00'
capacity: 100
distance: 867.0
...