EM Untruncation 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. Additionally, the EM algorithm is employed to detruncate censored demand. 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

from passengersim.utils.codeview import show_file

In [3]:

show_file("network/09-untrunc-em-high-demand.yaml")

show_file("network/09-untrunc-em-high-demand.yaml")

include:
  - 08-untrunc-em.yaml

simulation_controls:
  demand_multiplier: 1.1

In [4]:

cfg = pax.Config.from_yaml([
    "network/09-untrunc-em-high-demand.yaml",
])

cfg = pax.Config.from_yaml([ "network/09-untrunc-em-high-demand.yaml", ])

In [5]:

sim = pax.Simulation(cfg)

sim = pax.Simulation(cfg)

In [6]:

summary = sim.run()

summary = sim.run()

Task Completed after 23.64 seconds

In [7]:

summary.fig_carrier_revenues()

summary.fig_carrier_revenues()

Out[7]:

In [8]:

summary.fig_carrier_load_factors()

summary.fig_carrier_load_factors()

Out[8]:

In [9]:

summary.fig_fare_class_mix()

summary.fig_fare_class_mix()

Out[9]:

In [10]:

summary.fig_bookings_by_timeframe()

summary.fig_bookings_by_timeframe()

Out[10]:

In [11]:

summary.to_xlsx("outputs/3mkt-09.xlsx")

summary.to_xlsx("outputs/3mkt-09.xlsx")

Comparing against Targets¶

In [12]:

import targets

target = targets.load(9, cfg)

import targets target = targets.load(9, cfg)

In [13]:

from passengersim import contrast

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

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

In [14]:

comps.fig_bookings_by_timeframe(by_carrier="AL1")

comps.fig_bookings_by_timeframe(by_carrier="AL1")

Out[14]:

In [15]:

comps.fig_carrier_revenues()

comps.fig_carrier_revenues()

Out[15]:

In [16]:

comps.fig_carrier_load_factors()

comps.fig_carrier_load_factors()

Out[16]:

In [17]:

comps.fig_fare_class_mix()

comps.fig_fare_class_mix()

Out[17]:

In [18]:

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

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

Out[18]:

In [19]:

comps.fig_bookings_by_timeframe(by_carrier=False, by_class=True)

comps.fig_bookings_by_timeframe(by_carrier=False, by_class=True)

Out[19]:

In [20]:

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

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

Out[20]:

In [21]:

comps.fig_leg_forecasts(by_flt_no=111, of=["mu", "sigma"])

comps.fig_leg_forecasts(by_flt_no=111, of=["mu", "sigma"])

Out[21]:

In [22]:

from passengersim.utils.targets import computed_targets

c_targets = computed_targets(cfg)

from passengersim.utils.targets import computed_targets c_targets = computed_targets(cfg)

In [23]:

contrast.fig_bookings_by_timeframe({
    "c_targets": c_targets,
    "simulation": summary,
    "target": target,
}, by_carrier=False, by_class=True)

contrast.fig_bookings_by_timeframe({ "c_targets": c_targets, "simulation": summary, "target": target, }, by_carrier=False, by_class=True)

Out[23]: