EMSR-B with High Demand¶

In this example, we include product and advance purchase (AP) restrictions in the simulation, and have each airline use the leg-based EMSR-B algorithm to manage revenue. Demand is simulated at a level 10% higher than normal.

In [1]:

import passengersim as pax
pax.versions()

import passengersim as pax pax.versions()

passengersim 0.18.1
passengersim.core 0.18.1

In [2]:

from passengersim.utils.codeview import show_file
show_file("network/06-emsrb-high-demand.yaml")

from passengersim.utils.codeview import show_file show_file("network/06-emsrb-high-demand.yaml")

include:
  - 01-base.yaml
  - 02-buyup.yaml
  - 03-ap.yaml

simulation_controls:
  demand_multiplier: 1.1

airlines:
  - name: AL1
    rm_system: rm_no_detruncation
  - name: AL2
    rm_system: rm_no_detruncation

In [3]:

sim = pax.Simulation.from_yaml([
    "network/06-emsrb-high-demand.yaml",
])

sim = pax.Simulation.from_yaml([ "network/06-emsrb-high-demand.yaml", ])

In [4]:

summary = sim.run(log_reports=False)

summary = sim.run(log_reports=False)

Task Completed after 20.21 seconds

In [5]:

summary.fig_carrier_revenues()

summary.fig_carrier_revenues()

Out[5]:

In [6]:

summary.fig_carrier_load_factors()

summary.fig_carrier_load_factors()

Out[6]:

In [7]:

summary.fig_fare_class_mix()

summary.fig_fare_class_mix()

Out[7]:

In [8]:

summary.fig_bookings_by_timeframe()

summary.fig_bookings_by_timeframe()

Out[8]:

In [9]:

summary.to_xlsx("outputs/3mkt-06.xlsx")

summary.to_xlsx("outputs/3mkt-06.xlsx")

Comparing against Targets¶

In [10]:

import targets

target = targets.load(6, sim.config)

import targets target = targets.load(6, sim.config)

In [11]:

from passengersim import contrast

comps = contrast.Contrast({
    "simulation": summary,
    "target": target,
})

from passengersim import contrast comps = contrast.Contrast({ "simulation": summary, "target": target, })

In [12]:

comps.fig_carrier_revenues()

comps.fig_carrier_revenues()

Out[12]:

In [13]:

comps.fig_carrier_load_factors()

comps.fig_carrier_load_factors()

Out[13]:

In [14]:

comps.fig_fare_class_mix()

comps.fig_fare_class_mix()

Out[14]:

In [15]:

comps.fig_bookings_by_timeframe(by_carrier="AL1", by_class=True)

comps.fig_bookings_by_timeframe(by_carrier="AL1", by_class=True)

Out[15]:

In [16]:

comps.fig_leg_forecasts(by_flt_no=101, of=["mu", "sigma"])

comps.fig_leg_forecasts(by_flt_no=101, of=["mu", "sigma"])

Out[16]:

In [17]:

comps.fig_leg_forecasts(by_flt_no=211, of=["mu", "sigma"])

comps.fig_leg_forecasts(by_flt_no=211, of=["mu", "sigma"])

Out[17]: